IA Scholar Query: Rounding and Propagation Heuristics for Mixed Integer Programming.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgTue, 06 Dec 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help14407. Framing the Problem and Identifying Potential Solutions
https://scholar.archive.org/work/tnkxzditffdobb77q7y5hrtspa
The creation of effective policy and practice starts by framing the problem to be solved. This requires deciding what is important, identifying the current and potential future threats, diagnosing the actual cause of the problems, and identifying solutions, including innovating to create new ones when required. In this chapter we describe various techniques that can be used to frame the problem including horizon scanning, situation models and theory of change diagrams. These can be used to identify the analytical questions and specific assumptions that underpin the assessment of evidence and decision making.Nafeesa Esmail, Rhys Green, Silviu O. Petrovan, Nick Salafsky, Nigel G. Taylor, Jeremy D. Wilsonwork_tnkxzditffdobb77q7y5hrtspaTue, 06 Dec 2022 00:00:00 GMTUnifying Short and Long-Term Tracking with Graph Hierarchies
https://scholar.archive.org/work/p7xj5idljbgcpl7dszy7xco73e
Tracking objects over long videos effectively means solving a spectrum of problems, from short-term association for un-occluded objects to long-term association for objects that are occluded and then reappear in the scene. Methods tackling these two tasks are often disjoint and crafted for specific scenarios, and top-performing approaches are often a mix of techniques, which yields engineering-heavy solutions that lack generality. In this work, we question the need for hybrid approaches and introduce SUSHI, a unified and scalable multi-object tracker. Our approach processes long clips by splitting them into a hierarchy of subclips, which enables high scalability. We leverage graph neural networks to process all levels of the hierarchy, which makes our model unified across temporal scales and highly general. As a result, we obtain significant improvements over state-of-the-art on four diverse datasets. Our code and models will be made available.Orcun Cetintas, Guillem Brasó, Laura Leal-Taixéwork_p7xj5idljbgcpl7dszy7xco73eTue, 06 Dec 2022 00:00:00 GMTQEBVerif: Quantization Error Bound Verification of Neural Networks
https://scholar.archive.org/work/mp6bgjelmnadbflv7cvgxrijw4
While deep neural networks (DNNs) have demonstrated impressive performance in solving many challenging tasks, they are limited to resource-constrained devices owing to their demand for computation power and storage space. Quantization is one of the most promising techniques to address this issue by quantizing the weights and/or activation tensors of a DNN into lower bit-width fixed-point numbers. While quantization has been empirically shown to introduce minor accuracy loss, it lacks formal guarantees on that, especially when the resulting quantized neural networks (QNNs) are deployed in safety-critical applications. A majority of existing verification methods focus exclusively on individual neural networks, either DNNs or QNNs. While promising attempts have been made to verify the quantization error bound between DNNs and their quantized counterparts, they are not complete and more importantly do not support fully quantified neural networks, namely, only weights are quantized. To fill this gap, in this work, we propose a quantization error bound verification method (QEBVerif), where both weights and activation tensors are quantized. QEBVerif consists of two analyses: a differential reachability analysis (DRA) and a mixed-integer linear programming (MILP) based verification method. DRA performs difference analysis between the DNN and its quantized counterpart layer-by-layer to efficiently compute a tight quantization error interval. If it fails to prove the error bound, then we encode the verification problem into an equivalent MILP problem which can be solved by off-the-shelf solvers. Thus, QEBVerif is sound, complete, and arguably efficient. We implement QEBVerif in a tool and conduct extensive experiments, showing its effectiveness and efficiency.Yedi Zhang and Fu Song and Jun Sunwork_mp6bgjelmnadbflv7cvgxrijw4Tue, 06 Dec 2022 00:00:00 GMTGeneral Cutting Planes for Bound-Propagation-Based Neural Network Verification
https://scholar.archive.org/work/ntgk7nccgbcx5epbefood77uei
Bound propagation methods, when combined with branch and bound, are among the most effective methods to formally verify properties of deep neural networks such as correctness, robustness, and safety. However, existing works cannot handle the general form of cutting plane constraints widely accepted in traditional solvers, which are crucial for strengthening verifiers with tightened convex relaxations. In this paper, we generalize the bound propagation procedure to allow the addition of arbitrary cutting plane constraints, including those involving relaxed integer variables that do not appear in existing bound propagation formulations. Our generalized bound propagation method, GCP-CROWN, opens up the opportunity to apply general cutting plane methods for neural network verification while benefiting from the efficiency and GPU acceleration of bound propagation methods. As a case study, we investigate the use of cutting planes generated by off-the-shelf mixed integer programming (MIP) solver. We find that MIP solvers can generate high-quality cutting planes for strengthening bound-propagation-based verifiers using our new formulation. Since the branching-focused bound propagation procedure and the cutting-plane-focused MIP solver can run in parallel utilizing different types of hardware (GPUs and CPUs), their combination can quickly explore a large number of branches with strong cutting planes, leading to strong verification performance. Experiments demonstrate that our method is the first verifier that can completely solve the oval20 benchmark and verify twice as many instances on the oval21 benchmark compared to the best tool in VNN-COMP 2021, and also noticeably outperforms state-of-the-art verifiers on a wide range of benchmarks. GCP-CROWN is part of the α,β-CROWN verifier, the VNN-COMP 2022 winner. Code is available at http://PaperCode.cc/GCP-CROWNHuan Zhang, Shiqi Wang, Kaidi Xu, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, J. Zico Kolterwork_ntgk7nccgbcx5epbefood77ueiSun, 04 Dec 2022 00:00:00 GMTExample-Driven Question Answering
https://scholar.archive.org/work/mwcgwnnxvndsxo4cvbbcjmg2fu
Open-domain question answering (QA) is an emerging information-seeking paradigm, which automatically generates accurate and concise answers to natural language questions from humans. It becomes one of the most natural and efficient ways to interact with the web and especially desirable in hands-free speech-enabled environments. Building QA systems, however, either has to rely on off-the-shelf natural language processing tools that are not optimized for the QA task or train domain-specific modules (e.g., question type classification) with annotated data. Additionally, optimizing QA systems with hand-crafted procedures or feature engineering is costly, timeconsuming and laborious to transfer to new domains and languages. This dissertation studies the idea of example-driven question answering, which focuses on learning to search, select, and generate answers to unseen questions solely by observing existing noisy question-answer examples along with text corpus or knowledge base. To achieve this goal, we developed novel neural network architectures throughout the QA pipeline, that can be trained directly from question-answer examples. First, we propose candidate retrieval models that can utilize noisy signals to produce dense indexing for text corpus and generate structured queries for knowledge graphs. Second, we developed generative relevance models that do not require annotated negative QA pairs and discriminative relevance models that can utilize pseudo negative examples. Third, we improved encoderdecoder models for response text generation which can accept external guidance for specific language style and topic. The integrated QA pipeline aims to generate answer-like embedding vectors to search, select the most relevant passages, and compose a natural-sounding response based on the selected passages. This dissertation demonstrates the feasibility of creating open-domain example-driven QA pipelines based on neural networks without any feature engineering or dedicated manual annotations for each QA module. [...]Di Wangwork_mwcgwnnxvndsxo4cvbbcjmg2fuFri, 02 Dec 2022 00:00:00 GMTSurveillance Video Analysis with External Knowledge and Internal Constraints
https://scholar.archive.org/work/xe3er4eajbb35c2gamlnco3ati
The automated analysis of video data becomes ever more important as we are inundated with the ocean of videos generated every day, thus leading to much research in tasks such as content-based video retrieval, pose estimation and surveillance video analysis. Current state-of-the-art algorithms in these tasks are mainly supervised, i.e. the algorithms learn models based on manually labeled training data. However, it is difficult to manually collect large quantities of high quality labeled data. Therefore, in this thesis, we propose to circumvent this problem by automatically harvesting and exploiting useful information from unlabeled video based on 1) out-of-domain external knowledge sources and 2) internal constraints in video. Two tasks in the surveillance domain were targeted: multi-object tracking and pose estimation. Being able to localize and identify each individual at each time instant would be extremely useful in surveillance video analysis. We tackled this challenge by formulating the problem as an identity-aware multi-object tracking problem. An existing out-ofdomain knowledge source: face recognition, and an internal constraint: the spatialtemporal smoothness constraint were used in a joint optimization framework to localize each person. The spatial-temporal smoothness constraint was further utilized to automatically collect large amounts of multi-view person re-identification training data. This data was utilized to train deep person re-identification networks which further enhanced tracking performance on our 23-day 15-camera data set which consists of 4,935 hours of video. Results show that our tracker has the ability to locate a person 57% of the time with 73% precision. Reliable pose estimation in video enables us to understand the actions of a person, which would be very useful in surveillance video analysis. However, domain differences between surveillance videos and the pose detector's training set often cause degradation in pose estimation performance. Therefore, an unsupervised domain adaptati [...]Shoou-I Yuwork_xe3er4eajbb35c2gamlnco3atiFri, 02 Dec 2022 00:00:00 GMTDynamic Load Balancing Techniques in the IoT: A Review
https://scholar.archive.org/work/wyxi3yje2jaqpfr4fbufpcrchm
The Internet of things (IoT) extends the Internet space by allowing smart things to sense and/or interact with the physical environment and communicate with other physical objects (or things) around us. In IoT, sensors, actuators, smart devices, cameras, protocols, and cloud services are used to support many intelligent applications such as environmental monitoring, traffic monitoring, remote monitoring of patients, security surveillance, and smart home automation. To optimize the usage of an IoT network, certain challenges must be addressed such as energy constraints, scalability, reliability, heterogeneity, security, privacy, routing, quality of service (QoS), and congestion. To avoid congestion in IoT, efficient load balancing (LB) is needed for distributing traffic loads among different routes. To this end, this survey presents the IoT architectures and the networking paradigms (i.e., edge–fog–cloud paradigms) adopted in these architectures. Then, it analyzes and compares previous related surveys on LB in the IoT. It reviews and classifies dynamic LB techniques in the IoT for cloud and edge/fog networks. Lastly, it presents some lessons learned and open research issues.Dimitris Kanellopoulos, Varun Kumar Sharmawork_wyxi3yje2jaqpfr4fbufpcrchmFri, 02 Dec 2022 00:00:00 GMTA Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids
https://scholar.archive.org/work/r7tuonluhfdv3c24scqnvicz2e
This paper reviews the current techniques used in energy management systems to optimize energy schedules into microgrids, accounting for uncertainties for various time frames (day-ahead and real-time operations). The current uncertainties affecting applications, including residential, commercial, virtual power plants, electric mobility, and multi-carrier microgrids, are the main subjects of this article. We outline the most recent modeling approaches to describe the uncertainties associated with various microgrid applications, such as prediction errors, load consumption, degradation, and state of health. The modeling approaches discussed in this article are probabilistic, possibilistic, information gap theory, and deterministic. Then, the paper presents and compares the current optimization techniques, considering the uncertainties in their problem formulations, such as stochastic, robust, fuzzy optimization, information gap theory, model predictive control, multiparametric programming, and machine learning techniques. The optimization techniques depend on the model used, the data available, the specific application, the real-time platform, and the optimization time. We hope to guide researchers to identify the best optimization technique for energy scheduling, considering the specific uncertainty and application. Finally, the most challenging issues to enhance microgrid operations, despite uncertainties by considering new trends, are discussed.Ana Cabrera-Tobar, Alessandro Massi Pavan, Giovanni Petrone, Giovanni Spagnuolowork_r7tuonluhfdv3c24scqnvicz2eThu, 01 Dec 2022 00:00:00 GMTOvercoming the Convex Relaxation Barrier for Neural Network Verification via Nonconvex Low-Rank Semidefinite Relaxations
https://scholar.archive.org/work/zz5623mrjbbfbopfqw52m4zmvy
To rigorously certify the robustness of neural networks to adversarial perturbations, most state-of-the-art techniques rely on a triangle-shaped linear programming (LP) relaxation of the ReLU activation. While the LP relaxation is exact for a single neuron, recent results suggest that it faces an inherent "convex relaxation barrier" as additional activations are added, and as the attack budget is increased. In this paper, we propose a nonconvex relaxation for the ReLU relaxation, based on a low-rank restriction of a semidefinite programming (SDP) relaxation. We show that the nonconvex relaxation has a similar complexity to the LP relaxation, but enjoys improved tightness that is comparable to the much more expensive SDP relaxation. Despite nonconvexity, we prove that the verification problem satisfies constraint qualification, and therefore a Riemannian staircase approach is guaranteed to compute a near-globally optimal solution in polynomial time. Our experiments provide evidence that our nonconvex relaxation almost completely overcome the "convex relaxation barrier" faced by the LP relaxation.Hong-Ming Chiu, Richard Y. Zhangwork_zz5623mrjbbfbopfqw52m4zmvyWed, 30 Nov 2022 00:00:00 GMTA scalable architecture for federated service chaining
https://scholar.archive.org/work/t4p5evltondelmgxn4uuc5gnki
The orchestration of Service Function Chain in multiple clouds calls for low-cost, low-latency and scalability. In the existing literature, several techniques have been proposed to meet these requirements. However, how to federate service chains across geo-distributed clouds in light of these requirements remains open. This thesis aims to study how to compose service chains across multiple clouds by considering several factors such as domain autonomy, domain confidential information, scalability, deployment cost, end-to-end latency and dynamic traffic demands. In particular, the proposed schemes in this thesis are devised to improve the most crucial performance metrics: the deployment cost and the end-to-end latency. First, we propose a distributed architecture that jointly considers domain autonomy, domain confidential information and scalability. This architecture enables service chains across multiple administrative domains without revealing sensitive network information such as the domain topology. The proposed architecture significantly reduces the deployment cost which consists of resource and traffic routing costs. Moreover, the proposed architecture remarkably reduces the execution time which suggests that it processes the SFC requests timely. Second, the network traffic is dynamic in nature. To accommodate the varying traffic demand in edge clouds, it is important to dynamically scale VNFs in an agile and efficient manner by considering the resource scarcity at the edge. Hence, we propose a bottleneck-aware VNF scaling and traffic routing algorithm to effectively handle the incoming traffic. The proposed algorithm uses vertical and horizontal scaling in light of the VNF category. The experimental results show that the proposed algorithm efficiently shortens the end-to-end latency, improves the VNF utilization rate and reduces the running time.Chen Chenwork_t4p5evltondelmgxn4uuc5gnkiWed, 30 Nov 2022 00:00:00 GMTDefinitions of entwinement
https://scholar.archive.org/work/vwqjbc777zcy5deskljrm657e4
Entwinement was first introduced as the CFT dual to extremal, non-minimal geodesics of quotiented AdS_3 spaces. It was heuristically meant to capture the entanglement of internal, gauged degrees of freedom, for instance in the symmetric product orbifold CFT of the D1/D5 brane system. The literature now contains different, and sometimes inequivalent, field theory definitions of entwinement. In this paper, we build a discretized lattice model of symmetric product orbifold CFTs, and explicitly construct a gauge-invariant reduced density matrix whose von Neumann entropy agrees with the holographic computation of entwinement. Refining earlier notions, our construction gives meaning to the entwinement of an interval of given size within a long string of specific length. We discuss similarities and differences with previous definitions of entwinement.Ben Craps, Marine De Clerck, Alejandro Vilar Lópezwork_vwqjbc777zcy5deskljrm657e4Wed, 30 Nov 2022 00:00:00 GMTA Search and Detection Autonomous Drone System: from Design to Implementation
https://scholar.archive.org/work/l4eqmjs3jfcbrdm6jf25bkhiwq
Utilizing autonomous drones or unmanned aerial vehicles (UAVs) has shown great advantages over preceding methods in support of urgent scenarios such as search and rescue (SAR) and wildfire detection. In these operations, search efficiency in terms of the amount of time spent to find the target is crucial since with the passing of time the survivability of the missing person decreases or wildfire management becomes more difficult with disastrous consequences. In this work, it is considered a scenario where a drone is intended to search and detect a missing person (e.g., a hiker or a mountaineer) or a potential fire spot in a given area. In order to obtain the shortest path to the target, a general framework is provided to model the problem of target detection when the target's location is probabilistically known. To this end, two algorithms are proposed: Path planning and target detection. The path planning algorithm is based on Bayesian inference and the target detection is accomplished by means of a residual neural network (ResNet) trained on the image dataset captured by the drone as well as existing pictures and datasets on the web. Through simulation and experiment, the proposed path planning algorithm is compared with two benchmark algorithms. It is shown that the proposed algorithm significantly decreases the average time of the mission.Mohammadjavad Khosravi, Rushiv Arora, Saeede Enayati, Hossein Pishro-Nikwork_l4eqmjs3jfcbrdm6jf25bkhiwqTue, 29 Nov 2022 00:00:00 GMT2019
https://scholar.archive.org/work/wcy47hfvvvdwvfgnwx2cuak4ze
On completion of this course, students will have knowledge in: • CO1.Basics of electrochemistry. Classical & modern batteries and fuel cells. CO2. Causes & effects of corrosion of metals and control of corrosion. Modification of surface properties of metals to develop resistance to corrosion, wear, tear, impact etc. by electroplating and electroless plating. CO3. Production & consumption of energy for industrialization of country and living standards of people. Utilization of solar energy for different useful forms of energy. CO4. Understanding Phase rule and instrumental techniques and its applications. CO5.Over viewing of synthesis, properties and applications of nanomaterials.BTECH.CSwork_wcy47hfvvvdwvfgnwx2cuak4zeMon, 28 Nov 2022 00:00:00 GMTProbabilistic models for data efficient reinforcement learning
https://scholar.archive.org/work/lzilrt4mmvcz7gn4k2wpqmhvwy
Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, especially with the advent of deep neural networks. However, the standard deep learning methods often overlook the progress made in control theory by treating systems as black-box. We propose a model-based RL framework based on probabilistic Model Predictive Control (MPC). In particular, we propose to learn a probabilistic transition model using Gaussian Processes (GPs) to incorporate model uncertainty into long-term predictions, thereby, reducing the impact of model errors. We provide theoretical guarantees for first-order optimality in the GP-based transition models with deterministic approximate inference for long-term planning. We demonstrate that our approach not only achieves the state-of-the-art data efficiency, but also is a principled way for RL in constrained environments. When the true state of the dynamical system cannot be fully observed the standard model based methods cannot be directly applied. For these systems an additional step of state estimation is needed. We propose distributed message passing for state estimation in non-linear dynamical systems. In particular, we propose to use expectation propagation (EP) to iteratively refine the state estimate, i.e., the Gaussian posterior distribution on the latent state. We show two things: (a) Classical Rauch-Tung-Striebel (RTS) smoothers, such as the extended Kalman smoother (EKS) or the unscented Kalman smoother (UKS), are special cases of our message passing scheme; (b) running the message passing scheme more than once can lead to significant improvements over the classical RTS smoothers. We show the explicit connection between message passing with EP and well-known RTS smoothers and provide a practical implementation of the suggested algorithm. Furthermore, we address convergence issues of EP by generalising this framework to damped updates and the consideration of general 𝛼-divergences. Probabilistic models can also be used to generate synthetic data. I [...]Sanket Kamthe, Marc Deisenrothwork_lzilrt4mmvcz7gn4k2wpqmhvwyMon, 28 Nov 2022 00:00:00 GMTFirst year results on data plane infrastructure
https://scholar.archive.org/work/xplyadhr5vbj3ea66cb5ypqj64
This deliverable reports on the activities of WP3 during the first year of the B5G-OPEN project. The work was split into different activities with the following objectives: Design of an innovative optical transport infrastructure supporting MB connectivity and transparent network continuum potentially from User Equipment to Data Centers. Modelling transmission and traffic performance of the identified MB data plane solutions. Design, prototyping and experimental assessment of the novel optical network devices for switching, amplification and transmission. Exploring and testing optical innovative solutions for MB PON, Point to Multi-point (PtoMP) with low cost and power consumption for next-generation optical access & 5G X-haul. Design and testing the effective integration of fiber with LiFi systems supporting multi-cell simultaneous transmission for bandwidth maximization and effective hand-over. Design, prototyping and testing advanced monitoring solutions to enable efficient and flexible use of the infrastructure.P. Layecwork_xplyadhr5vbj3ea66cb5ypqj64Mon, 28 Nov 2022 00:00:00 GMT2021
https://scholar.archive.org/work/ggji2kgovvhtlh6nq7bk7mukh4
Module 2 Interaction of radiation with matter -Absorption-Spontaneous emission -Stimulated emission-Einstein's coefficients (expression for energy density). Requisites of a Laser system. Condition for laser action. Principle, Construction and working of He-Ne laser. Propagation mechanism in optical fibers. Angle of acceptance. Numerical aperture. Types of optical fibers-Step index and Graded index fiber. Modes of propagation-Single mode and Multimode fibers. Attenuation-Attenuation mechanisms. Teaching Methodology: Chalk and talk method: Interaction of radiation with matter -Absorption-Spontaneous emission -Stimulated emission-Einstein's coefficients (expression for energy density). Requisites of a Laser system. Condition for laser action. Propagation mechanism in optical fibers. Angle of acceptance. Numerical aperture. Powerpoint presentation: Types of optical fibers-Step index and Graded index fiber. Modes of propagation-Single mode and Multimode fibers. Video: Construction and working of He-Ne laser. Self-study material: Attenuation-Attenuation mechanisms. 9 Hours Module 3 Temperature dependence of resistivity in metals and superconducting materials. Effect of magnetic field (Meissner effect). Isotope effect -Type I and Type II superconductors-Temperature dependence of critical field. BCS theory (qualitative). High temperature superconductors-Josephson effect -SQUID-Applications of superconductors-Maglev vehicles (qualitative). Magnetic dipole-dipole moment-flux density-magnetic field intensity-Intensity of magnetization-magnetic permeability-susceptibility-relation between permeability and susceptibility. Classification of magnetic materials-Dia, Para, Ferromagnetism. Hysteresis-soft and hard magnetic materials. Teaching Methodology: Chalk and talk method: Temperature dependence of resistivity in metals and superconducting materials. Effect of magnetic field (Meissner effect). Isotope effect -Type I and Type II superconductors-Temperature dependence of critical field. BCS theory (qualitative). High temperature superconductors-Powerpoint presentation: Josephson effect -SQUID-Applications of superconductors. Magnetic dipole-dipole moment-flux density-magnetic field intensity-Intensity of magnetization-magnetic permeability-susceptibility-relation between permeability and susceptibility. Hysteresis-soft and hard magnetic materials. Video: Maglev vehicles (qualitative). Self-study material: Classification of magnetic materials-Dia, Para, Ferromagnetism 9 Hours Module 4 Amorphous and crystalline materials-Space lattice, Bravais lattice-Unit cell, primitive cell. Lattice parameters. Crystal systems. Direction and planes in a crystal. Miller indices -Determination of Miller indices of a plane. Expression for inter -planar spacing. Atoms per unit cell -Co-ordination number. Relation between atomic radius and lattice constant -Atomic packing factors (SC, FCC, BCC). Bragg's law. Determination of crystal structure using Bragg's X-ray diffractometer -X-ray spectrum. Teaching Methodology: Chalk and talk method: Direction and planes in a crystal. Miller indices -Determination of Miller indices of a plane. Powerpoint presentation: Atoms per unit cell -Co-ordination number. Relation between atomic radius and lattice constant -Atomic packing factors (SC, FCC, BCC). Bragg's law. Determination of crystal structure using Bragg's X-ray diffractometer -X-ray spectrum. Self-study material: Amorphous and crystalline materials-Space lattice, Bravais lattice-Unit cell, primitive cell. Lattice parameters. Crystal systems. 9 Hours Module 5 Interference of light -Superposition of two coherent waves-Constructive and destructive interference. Interference in thin films -Wedge shaped thin film-Air wedge -Application to find the diameter of a thin wire. Newton's rings -Application to find the refractive index of a liquid. Diffraction of light -Classes of diffraction -Fresnel and Fraunhofer diffraction. Fresnel theory of half period zone -Zone plate. Diffraction grating -Grating element -Grating equation -Construction of grating-Reflection and transmission grating. Teaching Methodology: Chalk and talk method: Interference of light -Superposition of two coherent waves-Constructive and destructive interference. Powerpoint presentation: Interference in thin films -Wedge shaped thin film-Air wedge -Application to find the diameter of a thin wire. Newton's rings -Application to find the refractive index of a liquid. Fresnel theory of half period zone -Zone plate. Diffraction grating -Grating element -Grating equation -Construction of grating-Reflection and transmission grating. Self-study material: Diffraction of light -Classes of diffraction -Fresnel and Fraunhofer diffraction. 9 Hours C PROGRAMMING Subject Code 21SCS12 IA Marks 50 Number of Lecture Hours/Week 2 (L) + 2 (T) Exam Marks 50 Total Number of Lecture Hours 45 Total Marks 100 Credits 03 Exam Hours 2 Course Objectives: 1. To understand the various steps in program development. 2. To learn the syntax and semantics of C programming language. 3. To learn the usage of structured programming approach in solving problems. Course Outcomes: CO1: On completion of this course students will be able to write algorithms and to draw flowcharts for solving problems. CO2: On completion of this course students will be able to convert the algorithms/flowcharts to C programs. CO3: Students will be able to code and test a given logic in C programming language. CO4: Students will be able to decompose a problem into functions and to develop modular reusable code. CO5: Students will be able to use arrays, pointers, strings and structures to write C programs. Module 1 Introduction to Algorithms: Steps to solve logical and numerical problems. Representation of Algorithm, Flowchart/Pseudo code with examples, Program design and structured programming Introduction to C Programming Language: variables, Syntax and Logical Errors in compilation, object and executable code, Operators, expressions and precedence, Expression evaluation, Storage classes, type conversion, The main method and command line arguments. Bitwise operations: Bitwise AND, OR, XOR and NOT operators. Conditional Branching and Loops: Writing and evaluation of conditionals and consequent branching with if, if-else, switch-case, ternary operator, goto, Iteration with for, while, do-while loops I/O: Simple input and output with scanf and printf, formatted I/O, Introduction to stdin, stdout and stderr. Command line arguments. Teaching Methodology: Chalk and talk using PPT and Demo to explain the concept. 9 Hours Module 2 Arrays, Strings, Structures and Pointers: Arrays: one and two-dimensional arrays, creating, accessing and manipulating elements of arrays. Strings: Introduction to strings, handling strings as array of characters, basic string functions available in C (strlen, strcat, strcpy, strstr etc.), arrays of strings. Structures: Defining structures, initializing structures, unions, Array of structures. Pointers: Idea of pointers, Defining pointers, Pointers to Arrays and Structures, Use of Pointers in self referential structures, usage of self referential structures in linked list (no implementation) Enumeration data type. Teaching Methodology: Chalk and talk using PPT and Demo to explain the concept. Module 3 9 Hours Preprocessor and File handling in C: Preprocessor: Commonly used Preprocessor commands like include, define, undef, if, ifdef, ifndef Files: Text and Binary files, Creating and Reading and writing text and binary files, Appending data to existing files, Writing and reading structures using binary files, Random access using fseek, ftell and rewind functions. Teaching Methodology: Chalk and talk using PPT and Demo to explain the concept. 9 Hours Module 4 Function and Dynamic Memory Allocation: Functions: Designing structured programs, Declaring a function, Signature of a function, Parameters and return type of a function, passing parameters to functions, call by value, Passing arrays to functions, passing pointers to functions, idea of call by reference, Some C standard functions and libraries Recursion: Simple programs, such as Finding Factorial, Fibonacci series etc., Limitations of Recursive functions. Dynamic memory allocation: Allocating and freeing memory, Allocating memory for arrays of different data types. Teaching Methodology: Chalk and talk using PPT and Demo to explain the concept. 9 Hours Module 5 C PROGRAMMING LABORATORY Subject Code 21SCSL12 IA Marks 25 Number of Practical Hours/Week 1 (T) + 2 (L) Exam Marks 25 Total Number of Practical Hours 36 Total Marks 50 Credits 02 Exam Hours 3 Course Objectives: 1. To describe the basics of computer and understand the problem-solving aspect. 2. To demonstrate the algorithm and flow chart for the given problem. 3. To introduce students to the basic knowledge of programming fundamentals of C language. 4. To impart writing skill of C programming to the students and solving problems. 5. To impart the concepts like looping, array, functions, pointers, file, structure. Course Outcomes: CO1: Understand the problem solving to write efficient algorithms to solve real time problems. CO2: Understand and use various constructs of the programming language such as conditionals, iteration, and recursion. CO3: Implement your algorithms to build programs in the C programming language. CO4: Use data structures like arrays, linked lists, and stacks to solve various problems. CO5: Understand and use file handling in the C programming language. EXPERIMENTS: Implement the following programs with WINDOWS / LINUX platform using appropriate C compiler. Course Objectives: 1. To provide basic concepts D.C circuits and circuit analysis techniques 2. To provide knowledge on A.C circuit fundamental techniques 3. To understand construction and operation of BJT and Junction FET 4. Explain the different modes of communications from wired to wireless and the computing involved. 5. To provide fundamental knowledge of Digital Logic. Course Outcomes: CO1: Understand concepts of electrical circuits and elements. CO2: Apply basic electric laws in solving circuit problems. CO3: Analyze simple circuits containing transistors CO4: Understand concept of cellular wireless networks. CO5: Understand Number systems and design basic digital circuits.BTECH.MECHwork_ggji2kgovvhtlh6nq7bk7mukh4Mon, 28 Nov 2022 00:00:00 GMT2021
https://scholar.archive.org/work/n7rhmaerpvfrhha4draeqwscs4
Course Objectives: 1. Learn and understand basic concepts and principles of Physics. 2. Make students familiar with latest trends in material science research and learn about novel materials and its applications. 3. Make students confident in analyzing engineering problems and apply its solutions effectively and meaningfully. 4. Gain knowledge in interference and diffraction of light and its applications in new technology. Course Outcomes: CO1: Learn and understand more about basic principles and to develop problem solving skills and implementation in technology. CO2: Study material properties and their application and its use in engineering applications and studies. CO3: Understand crystal structure and applications to boost the technical skills and its applications. CO4: Apply light phenomena in new technology. Module 1 Classical free electron theory-Free-electron concept (Drift velocity, Thermal velocity, Mean collision time, Mean free path, relaxation time) -Expression for electrical conductivity-Failure of classical free electron theory. Quantum free electron theory, Assumptions, Fermi factor, Fermi-Dirac Statistics. Expression for electrical conductivity based on quantum free electron theory. Merits of quantum free electron theory. Temperature dependence of electrical resistivity -Specific heat -Thermionic emission. Hall effect (Qualitative) -Wiedemann-Franz law. Teaching Methodology: Chalk and talk method: Classical free electron theory-Free-electron concept (Drift velocity, Thermal velocity, Mean collision time, Mean free path, relaxation time) -Expression for electrical conductivity-Failure of classical free electron theory. Powerpoint presentation: Quantum free electron theory, Assumptions, Fermi factor, Fermi-Dirac Statistics. Expression for electrical conductivity based on quantum free electron theory. Merits of quantum free electron theory. Temperature dependence of electrical resistivity -Specific heat -Thermionic emission. Wiedemann-Franz law. Self-study material: Hall effect (Qualitative) 9 Hours Module 2 Interaction of radiation with matter -Absorption-Spontaneous emission -Stimulated emission-Einstein's coefficients (expression for energy density). Requisites of a Laser system. Condition for laser action. Principle, Construction and working of He-Ne laser. Propagation mechanism in optical fibers. Angle of acceptance. Numerical aperture. Types of optical fibers-Step index and Graded index fiber. Modes of propagation-Single mode and Multimode fibers. Attenuation-Attenuation mechanisms. Teaching Methodology: Chalk and talk method: Interaction of radiation with matter -Absorption-Spontaneous emission -Stimulated emission-Einstein's coefficients (expression for energy density). Requisites of a Laser system. Condition for laser action. Propagation mechanism in optical fibers. Angle of acceptance. Numerical aperture. Powerpoint presentation: Types of optical fibers-Step index and Graded index fiber. Modes of propagation-Single mode and Multimode fibers. Video: Construction and working of He-Ne laser. Self-study material: Attenuation-Attenuation mechanisms. 9 Hours Module 3 Temperature dependence of resistivity in metals and superconducting materials. Effect of magnetic field (Meissner effect). Isotope effect -Type I and Type II superconductors-Temperature dependence of critical field. BCS theory (qualitative). High temperature superconductors-Josephson effect -SQUID-Applications of superconductors-Maglev vehicles (qualitative). Magnetic dipole-dipole moment-flux density-magnetic field intensity-Intensity of magnetization-magnetic permeability-susceptibility-relation between permeability and susceptibility. Classification of magnetic materials-Dia, Para, Ferromagnetism. Hysteresis-soft and hard magnetic materials. Teaching Methodology: Chalk and talk method: Temperature dependence of resistivity in metals and superconducting materials. Effect of magnetic field (Meissner effect). Isotope effect -Type I and Type II superconductors-Temperature dependence of critical field. BCS theory (qualitative). High temperature superconductors-Powerpoint presentation: Josephson effect -SQUID-Applications of superconductors. Magnetic dipole-dipole moment-flux density-magnetic field intensity-Intensity of magnetization-magnetic permeability-susceptibility-relation between permeability and susceptibility. Hysteresis-soft and hard magnetic materials. Video: Maglev vehicles (qualitative). Self-study material: Classification of magnetic materials-Dia, Para, Ferromagnetism 9 Hours Module 4 Amorphous and crystalline materials-Space lattice, Bravais lattice-Unit cell, primitive cell. Lattice parameters. Crystal systems. Direction and planes in a crystal. Miller indices -Determination of Miller indices of a plane. Expression for interplanar spacing. Atoms per unit cell -Co-ordination number. Relation between atomic radius and lattice constant -Atomic packing factors (SC, FCC, BCC). Bragg's law. Determination of crystal structure using Bragg's X-ray diffractometer -X-ray spectrum. Teaching Methodology: Chalk and talk method: Direction and planes in a crystal. Miller indices -Determination of Miller indices of a plane. Powerpoint presentation: Atoms per unit cell -Co-ordination number. Relation between atomic radius and lattice constant -Atomic packing factors (SC, FCC, BCC). Bragg's law. Determination of crystal structure using Bragg's X-ray diffractometer -X-ray spectrum. Self-study material: Amorphous and crystalline materials-Space lattice, Bravais lattice-Unit cell, primitive cell. Lattice parameters. Crystal systems. 9 Hours Module 5 Interference of light -Superposition of two coherent waves-Constructive and destructive interference. Interference in thin films -Wedge shaped thin film-Air wedge -Application to find the diameter of a thin wire. Newton's rings -Application to find the refractive index of a liquid. Diffraction of light -Classes of diffraction -Fresnel and Fraunhofer diffraction. Fresnel theory of half period zone -Zone plate.BTECH.CSwork_n7rhmaerpvfrhha4draeqwscs4Mon, 28 Nov 2022 00:00:00 GMT2021
https://scholar.archive.org/work/zcptjnj56ndzpdejmircducjeu
2021-AI & MLAI & MLwork_zcptjnj56ndzpdejmircducjeuMon, 28 Nov 2022 00:00:00 GMTSimulation Intelligence: Towards a New Generation of Scientific Methods
https://scholar.archive.org/work/rfujm43y4ngcnml5emnvjksbjy
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.Alexander Lavin, David Krakauer, Hector Zenil, Justin Gottschlich, Tim Mattson, Johann Brehmer, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfefferwork_rfujm43y4ngcnml5emnvjksbjySun, 27 Nov 2022 00:00:00 GMTAn Efficient Black-Box Support of Advanced Coverage Criteria for Klee
https://scholar.archive.org/work/smndgn4vl5coxoklcvvgf57uf4
Dynamic symbolic execution (DSE) is a powerful test generation approach based on an exploration of the path space of the program under test. Well-adapted for path coverage, this approach is however less efficient for conditions, decisions, advanced coverage criteria (such as multiple conditions, weak mutations, boundary testing) or user-provided test objectives. While theoretical solutions to adapt DSE to a large set of criteria have been proposed, they have never been integrated into publicly available testing tools. This paper presents a first integration of an optimized test generation strategy for advanced coverage criteria into a popular open-source testing tool based on DSE, namely, Klee. The integration is performed in a fully black-box manner, and can therefore inspire an easy integration into other similar tools. The resulting version of the tool is publicly available. We present the design of the proposed technique and evaluate it on several benchmarks. Our results confirm the benefits of the proposed tool for advanced coverage criteria.Nicolas Berthier and Steven De Oliveira and Nikolai Kosmatov and Delphine Longuet and Romain Soulatwork_smndgn4vl5coxoklcvvgf57uf4Sat, 26 Nov 2022 00:00:00 GMT