IA Scholar Query: Learning Nearly Monotone k-term DNF.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgWed, 16 Nov 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Ensemble learning with discrete classifiers on small devices
https://scholar.archive.org/work/ueosywvwgngpda3plzsi7plimy
Machine learning has become an integral part of everyday life ranging from applications in AI-powered search queries to (partial) autonomous driving. Many of the advances in machine learning and its application have been possible due to increases in computation power, i.e., by reducing manufacturing sizes while maintaining or even increasing energy consumption. However, 2-3 nm manufacturing is within reach, making further miniaturization increasingly difficult while thermal design power limits are simultaneously reached, rendering entire parts of the chip useless for certain computational loads. In this thesis, we investigate discrete classifier ensembles as a resource-efficient alternative that can be deployed to small devices that only require small amounts of energy. Discrete classifiers are classifiers that can be applied -- and oftentimes also trained -- without the need for costly floating-point operations. Hence, they are ideally suited for deployment to small devices with limited resources. The disadvantage of discrete classifiers is that their predictive performance often lacks behind their floating-point siblings. Here, the combination of multiple discrete classifiers into an ensemble can help to improve the predictive performance while still having a manageable resource consumption. This thesis studies discrete classifier ensembles from a theoretical point of view, an algorithmic point of view, and a practical point of view. In the theoretical investigation, the bias-variance decomposition and the double-descent phenomenon are examined. The bias-variance decomposition of the mean-squared error is re-visited and generalized to an arbitrary twice-differentiable loss function, which serves as a guiding tool throughout the thesis. Similarly, the double-descent phenomenon is -- for the first time -- studied comprehensively in the context of tree ensembles and specifically random forests. Contrary to established literature, the experiments in this thesis indicate that there is no double-descent in random for [...]Sebastian Buschjäger, Technische Universität Dortmundwork_ueosywvwgngpda3plzsi7plimyWed, 16 Nov 2022 00:00:00 GMTCertification with an NP Oracle
https://scholar.archive.org/work/ws6w4kgznbga5ce6tzw3sxh724
In the certification problem, the algorithm is given a function f with certificate complexity k and an input x^⋆, and the goal is to find a certificate of size ≤poly(k) for f's value at x^⋆. This problem is in 𝖭𝖯^𝖭𝖯, and assuming 𝖯𝖭𝖯, is not in 𝖯. Prior works, dating back to Valiant in 1984, have therefore sought to design efficient algorithms by imposing assumptions on f such as monotonicity. Our first result is a 𝖡𝖯𝖯^𝖭𝖯 algorithm for the general problem. The key ingredient is a new notion of the balanced influence of variables, a natural variant of influence that corrects for the bias of the function. Balanced influences can be accurately estimated via uniform generation, and classic 𝖡𝖯𝖯^𝖭𝖯 algorithms are known for the latter task. We then consider certification with stricter instance-wise guarantees: for each x^⋆, find a certificate whose size scales with that of the smallest certificate for x^⋆. In sharp contrast with our first result, we show that this problem is 𝖭𝖯^𝖭𝖯-hard even to approximate. We obtain an optimal inapproximability ratio, adding to a small handful of problems in the higher levels of the polynomial hierarchy for which optimal inapproximability is known. Our proof involves the novel use of bit-fixing dispersers for gap amplification.Guy Blanc and Caleb Koch and Jane Lange and Carmen Strassle and Li-Yang Tanwork_ws6w4kgznbga5ce6tzw3sxh724Fri, 04 Nov 2022 00:00:00 GMTSuperpolynomial Lower Bounds for Decision Tree Learning and Testing
https://scholar.archive.org/work/dhe3vxkdifastc5ddv5r2xitqy
We establish new hardness results for decision tree optimization problems, adding to a line of work that dates back to Hyafil and Rivest in 1976. We prove, under randomized ETH, superpolynomial lower bounds for two basic problems: given an explicit representation of a function f and a generator for a distribution 𝒟, construct a small decision tree approximator for f under 𝒟, and decide if there is a small decision tree approximator for f under 𝒟. Our results imply new lower bounds for distribution-free PAC learning and testing of decision trees, settings in which the algorithm only has restricted access to f and 𝒟. Specifically, we show: n-variable size-s decision trees cannot be properly PAC learned in time n^Õ(loglog s), and depth-d decision trees cannot be tested in time exp(d^ O(1)). For learning, the previous best lower bound only ruled out poly(n)-time algorithms (Alekhnovich, Braverman, Feldman, Klivans, and Pitassi, 2009). For testing, recent work gives similar though incomparable bounds in the setting where f is random and 𝒟 is nonexplicit (Blais, Ferreira Pinto Jr., and Harms, 2021). Assuming a plausible conjecture on the hardness of Set-Cover, we show our lower bound for learning decision trees can be improved to n^Ω(log s), matching the best known upper bound of n^O(log s) due to Ehrenfeucht and Haussler (1989). We obtain our results within a unified framework that leverages recent progress in two lines of work: the inapproximability of Set-Cover and XOR lemmas for query complexity. Our framework is versatile and yields results for related concept classes such as juntas and DNF formulas.Caleb Koch and Carmen Strassle and Li-Yang Tanwork_dhe3vxkdifastc5ddv5r2xitqyWed, 12 Oct 2022 00:00:00 GMTEfficient Quantum Agnostic Improper Learning of Decision Trees
https://scholar.archive.org/work/bxly67bzvbaxdgdtarkwvj6dim
The agnostic setting is the hardest generalization of the PAC model since it is akin to learning with adversarial noise. We study an open question on the existence of efficient quantum boosting algorithms in this setting. We answer this question in the affirmative by providing a quantum version of the Kalai-Kanade potential boosting algorithm. This algorithm shows the standard quadratic speedup in the VC dimension of the weak learner compared to the classical case. Using our boosting algorithm as a subroutine, we give a quantum algorithm for agnostically learning decision trees in polynomial running time without using membership queries. To the best of our knowledge, this is the first algorithm (quantum or classical) to do so. Learning decision trees without membership queries is hard (and an open problem) in the standard classical realizable setting. In general, even coming up with weak learners in the agnostic setting is a challenging task. We show how to construct a quantum agnostic weak learner using standard quantum algorithms, which is of independent interest for designing ensemble learning setups.Debajyoti Bera, Sagnik Chatterjeework_bxly67bzvbaxdgdtarkwvj6dimSat, 01 Oct 2022 00:00:00 GMTFourier Growth of Regular Branching Programs
https://scholar.archive.org/work/pjh5g5xh2rcqbm7eluanmsjfoi
We analyze the Fourier growth, i.e. the L₁ Fourier weight at level k (denoted L_{1,k}), of read-once regular branching programs. We prove that every read-once regular branching program B of width w ∈ [1,∞] with s accepting states on n-bit inputs must have its L_{1,k} bounded by min{Pr[B(U_n) = 1](w-1)^k, s ⋅ O((n log n)/k)^{(k-1)/2}}. For any constant k, our result is tight up to constant factors for the AND function on w-1 bits, and is tight up to polylogarithmic factors for unbounded width programs. In particular, for k = 1 we have L_{1,1}(B) ≤ s, with no dependence on the width w of the program. Our result gives new bounds on the coin problem and new pseudorandom generators (PRGs). Furthermore, we obtain an explicit generator for unordered permutation branching programs of unbounded width with a constant factor stretch, where no PRG was previously known. Applying a composition theorem of Błasiok, Ivanov, Jin, Lee, Servedio and Viola (RANDOM 2021), we extend our results to "generalized group products," a generalization of modular sums and product tests.Chin Ho Lee, Edward Pyne, Salil Vadhan, Amit Chakrabarti, Chaitanya Swamywork_pjh5g5xh2rcqbm7eluanmsjfoiThu, 15 Sep 2022 00:00:00 GMTQuery Embedding on Hyper-relational Knowledge Graphs
https://scholar.archive.org/work/nmlhbngwjzenzknc3vvxnxyfci
Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs (KGs). It subsumes both one-hop link prediction as well as other more complex types of logical queries. Existing algorithms operate only on classical, triple-based graphs, whereas modern KGs often employ a hyper-relational modeling paradigm. In this paradigm, typed edges may have several key-value pairs known as qualifiers that provide fine-grained context for facts. In queries, this context modifies the meaning of relations, and usually reduces the answer set. Hyper-relational queries are often observed in real-world KG applications, and existing approaches for approximate query answering cannot make use of qualifier pairs. In this work, we bridge this gap and extend the multi-hop reasoning problem to hyper-relational KGs allowing to tackle this new type of complex queries. Building upon recent advancements in Graph Neural Networks and query embedding techniques, we study how to embed and answer hyper-relational conjunctive queries. Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.Dimitrios Alivanistos and Max Berrendorf and Michael Cochez and Mikhail Galkinwork_nmlhbngwjzenzknc3vvxnxyfciTue, 06 Sep 2022 00:00:00 GMTThe Approximate Degree of DNF and CNF Formulas
https://scholar.archive.org/work/oc2yj3oyvzdfflkgf4u56vorry
The approximate degree of a Boolean function f{0,1}^n→{0,1} is the minimum degree of a real polynomial p that approximates f pointwise: |f(x)-p(x)|≤1/3 for all x∈{0,1}^n. For every δ>0, we construct CNF and DNF formulas of polynomial size with approximate degree Ω(n^1-δ), essentially matching the trivial upper bound of n. This improves polynomially on previous lower bounds and fully resolves the approximate degree of constant-depth circuits (AC^0), a question that has seen extensive research over the past 10 years. Previously, an Ω(n^1-δ) lower bound was known only for AC^0 circuits of depth that grows with 1/δ (Bun and Thaler, FOCS 2017). Moreover, our CNF and DNF formulas are the simplest possible in that they have constant width. Our result holds even for one-sided approximation, and has the following further consequences. (i) We essentially settle the communication complexity of AC^0 circuits in the bounded-error quantum model, k-party number-on-the-forehead randomized model, and k-party number-on-the-forehead nondeterministic model: we prove that for every δ>0, these models require Ω(n^1-δ), Ω(n/4^kk^2)^1-δ, and Ω(n/4^kk^2)^1-δ, respectively, bits of communication even for polynomial-size constant-width CNF formulas. (ii) In particular, we show that the multiparty communication class coNP_k can be separated essentially optimally from NP_k and BPP_k by a particularly simple function, a polynomial-size constant-width CNF. (iii) We give an essentially tight separation, of O(1) versus Ω(n^1-δ), for the one-sided versus two-sided approximate degree of a function; and O(1) versus Ω(n^1-δ) for the one-sided approximate degree of a function f versus its negation f.Alexander A. Sherstovwork_oc2yj3oyvzdfflkgf4u56vorrySun, 04 Sep 2022 00:00:00 GMTBeyond Natural Proofs: Hardness Magnification and Locality
https://scholar.archive.org/work/pxzw5rfppzbopiwrw7cjwxevky
Hardness magnification reduces major complexity separations (such as \(\mathsf {\mathsf {EXP}} \nsubseteq \mathsf {NC}^1 \) ) to proving lower bounds for some natural problem Q against weak circuit models. Several recent works [11, 13, 14, 40, 42, 43, 46] have established results of this form. In the most intriguing cases, the required lower bound is known for problems that appear to be significantly easier than Q , while Q itself is susceptible to lower bounds but these are not yet sufficient for magnification. In this work, we provide more examples of this phenomenon, and investigate the prospects of proving new lower bounds using this approach. In particular, we consider the following essential questions associated with the hardness magnification program: – Does hardness magnification avoid the natural proofs barrier of Razborov and Rudich [51] ? – Can we adapt known lower bound techniques to establish the desired lower bound for Q ? We establish that some instantiations of hardness magnification overcome the natural proofs barrier in the following sense: slightly superlinear-size circuit lower bounds for certain versions of the minimum circuit size problem \({\sf MCSP} \) imply the non-existence of natural proofs. As the non-existence of natural proofs implies the non-existence of efficient learning algorithms, we show that certain magnification theorems not only imply strong worst-case circuit lower bounds but also rule out the existence of efficient learning algorithms. Hardness magnification might sidestep natural proofs, but we identify a source of difficulty when trying to adapt existing lower bound techniques to prove strong lower bounds via magnification. This is captured by a locality barrier : existing magnification theorems unconditionally show that the problems Q considered above admit highly efficient circuits extended with small fan-in oracle gates, while lower bound techniques against weak circuit models quite often easily extend to circuits containing such oracles. This explains why direct adaptations of certain lower bounds are unlikely to yield strong complexity separations via hardness magnification.Lijie Chen, Shuichi Hirahara, Igor C. Oliveira, Ján Pich, Ninad Rajgopal, Rahul Santhanamwork_pxzw5rfppzbopiwrw7cjwxevkyFri, 12 Aug 2022 00:00:00 GMTLearning Interpretable Decision Rule Sets: A Submodular Optimization Approach
https://scholar.archive.org/work/bw52v6gxirf45jdq7jb3dgwccy
Rule sets are highly interpretable logical models in which the predicates for decision are expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model comprises an unordered collection of if-then decision rules. In this paper, we consider a submodular optimization based approach for learning rule sets. The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set. We employ an objective function that exhibits submodularity and thus is amenable to submodular optimization techniques. To overcome the difficulty arose from dealing with the exponential-sized ground set of rules, the subproblem of searching a rule is casted as another subset selection task that asks for a subset of features. We show it is possible to write the induced objective function for the subproblem as a difference of two submodular (DS) functions to make it approximately solvable by DS optimization algorithms. Overall, the proposed approach is simple, scalable, and likely to be benefited from further research on submodular optimization. Experiments on real datasets demonstrate the effectiveness of our method.Fan Yang, Kai He, Linxiao Yang, Hongxia Du, Jingbang Yang, Bo Yang, Liang Sunwork_bw52v6gxirf45jdq7jb3dgwccyWed, 08 Jun 2022 00:00:00 GMTEnumerating k-SAT functions
https://scholar.archive.org/work/c7j6boem45aqhpbioh5a7q2ski
How many k-SAT functions on n boolean variables are there? What does a typical such function look like? Bollobás, Brightwell, and Leader conjectured that, for each fixed k ≥ 2, the number of k-SAT functions on n variables is (1+o(1))2^nk + n, or equivalently: a 1-o(1) fraction of all k-SAT functions are unate, i.e., monotone after negating some variables. They proved a weaker version of the conjecture for k=2. The conjecture was confirmed for k=2 by Allen and k=3 by Ilinca and Kahn. We show that the problem of enumerating k-SAT functions is equivalent to a Turán density problem for partially directed hypergraphs. Our proof uses the hypergraph container method. Furthermore, we confirm the Bollobás–Brightwell–Leader conjecture for k=4 by solving the corresponding Turán density problem. Our solution applies a recent result of Füredi and Maleki on the minimum triangular edge density in a graph of given edge density. In an appendix (by Nitya Mani and Edward Yu), we further confirm the k=5 case of the conjecture via a brute force computer search.Dingding Dong, Nitya Mani, Yufei Zhaowork_c7j6boem45aqhpbioh5a7q2skiMon, 25 Apr 2022 00:00:00 GMTSemantics and loop invariant synthesis for probabilistic programs
https://scholar.archive.org/work/n4e6oi3p2vddbop2zps5qjbdxy
In this thesis we consider sequential probabilistic programs. Such programs are a means to model randomised algorithms in computer science. They facilitate the formal analysis of performance and correctness of algorithms or security aspects of protocols. We develop an operational semantics for probabilistic programs and show it to be equivalent to the expectation transformer semantics due to McIver and Morgan. This connection between the two kinds of semantics provides a deeper understanding of the behaviour of probabilistic programs and is instrumental to transfer results between communities that use transition systems such as Markov decision processes to reason about probabilistic behaviour and communities that focus on deductive verification techniques based on expectation transformers. As a next step, we add the concept of observations and extend both semantics to facilitate the calculation of expectations which are conditioned on the fact that no observation is violated during the program's execution. Our main contribution here is to explore issues that arise with non-terminating, non-deterministic or infeasible programs and provide semantics that are generally applicable. Additionally, we discuss several program transformation to facilitate the understanding of conditioning in probabilistic programming. In the last part of the thesis we turn our attention to the automated verification of probabilistic programs. We are interested in automating inductive verification techniques. As usual the main obstacle in program analysis are loops which require either the calculation of fixed points or the generation of inductive invariants for their analysis. This task, which is already hard for standard, i.e. non-probabilistic, programs, becomes even more challenging as our reasoning becomes quantitative. We focus on a technique to generate quantitative loop invariants from user defined templates. This approach is implemented in a software tool called Prinsys and evaluated on several examples.Friedrich Gretzwork_n4e6oi3p2vddbop2zps5qjbdxyMon, 28 Mar 2022 00:00:00 GMTSatellite observations document trends consistent with a boreal forest biome shift
https://scholar.archive.org/work/fncuroqcfrcphktjviomrv64mq
The boreal forest biome is a major component of Earth's biosphere and climate system that is projected to shift northward due to continued climate change over the coming century. Indicators of a biome shift will likely first be evident along the climatic margins of the boreal forest and include changes in vegetation productivity, mortality, and recruitment, as well as overall vegetation greenness. However, the extent to which a biome shift is already underway remains unclear because of the local nature of most field studies, sparsity of systematic ground-based ecological monitoring, and reliance on coarse resolution satellite observations. Here, we evaluated early indicators of a boreal forest biome shift using four decades of moderate resolution (30 m) satellite observations and biogeoclimatic spatial datasets. Specifically, we quantified interannual trends in annual maximum vegetation greenness using an ensemble of vegetation indices derived from Landsat observations at 100,000 sample sites in areas without signs of recent disturbance. We found vegetation greenness increased (greened) at 38 [29, 42] % and 22 [15, 26] % of sample sites from 1985 to 2019 and 2000 to 2019, whereas vegetation greenness decreased (browned) at 13 [9, 15] % and 15 [13, 19] % of sample sites during these respective periods [95% Monte Carlo confidence intervals]. Greening was thus 3.0 [2.6, 3.5] and 1.5 [0.8, 2.0] times more common than browning and primarily occurred in cold sparsely treed areas with high soil nitrogen and moderate summer warming. Conversely, browning primarily occurred in the climatically warmest margins of both the boreal forest biome and major forest types (e.g., evergreen conifer forests), especially in densely treed areas where summers became warmer and drier. These macroecological trends reflect underlying shifts in vegetation productivity, mortality, and recruitment that are consistent with early stages of a boreal biome shift.Logan T. Berner, Scott J. Goetzwork_fncuroqcfrcphktjviomrv64mqThu, 24 Feb 2022 00:00:00 GMTNear-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals
https://scholar.archive.org/work/nguf52ihonadjlznuojjcjwzfu
We consider the well-studied problem of learning intersections of halfspaces under the Gaussian distribution in the challenging agnostic learning model. Recent work of Diakonikolas et al. (2021) shows that any Statistical Query (SQ) algorithm for agnostically learning the class of intersections of k halfspaces over ℝ^n to constant excess error either must make queries of tolerance at most n^-Ω̃(√(log k)) or must make 2^n^Ω(1) queries. We strengthen this result by improving the tolerance requirement to n^-Ω̃(log k). This lower bound is essentially best possible since an SQ algorithm of Klivans et al. (2008) agnostically learns this class to any constant excess error using n^O(log k) queries of tolerance n^-O(log k). We prove two variants of our lower bound, each of which combines ingredients from Diakonikolas et al. (2021) with (an extension of) a different earlier approach for agnostic SQ lower bounds for the Boolean setting due to Dachman-Soled et al. (2014). Our approach also yields lower bounds for agnostically SQ learning the class of "convex subspace juntas" (studied by Vempala, 2010) and the class of sets with bounded Gaussian surface area; all of these lower bounds are nearly optimal since they essentially match known upper bounds from Klivans et al. (2008).Daniel Hsu, Clayton Sanford, Rocco Servedio, Emmanouil-Vasileios Vlatakis-Gkaragkouniswork_nguf52ihonadjlznuojjcjwzfuThu, 10 Feb 2022 00:00:00 GMTExtremely Deep Proofs
https://scholar.archive.org/work/lsc7zmdzendwvhrravjrnuc5hy
We further the study of supercritical tradeoffs in proof and circuit complexity, which is a type of tradeoff between complexity parameters where restricting one complexity parameter forces another to exceed its worst-case upper bound. In particular, we prove a new family of supercritical tradeoffs between depth and size for Resolution, Res(k), and Cutting Planes proofs. For each of these proof systems we construct, for each c ≤ n^{1-ε}, a formula with n^{O(c)} clauses and n variables that has a proof of size n^{O(c)} but in which any proof of size no more than roughly exponential in n^{1-ε}/c must necessarily have depth ≈ n^c. By setting c = o(n^{1-ε}) we therefore obtain exponential lower bounds on proof depth; this far exceeds the trivial worst-case upper bound of n. In doing so we give a simplified proof of a supercritical depth/width tradeoff for tree-like Resolution from [Alexander A. Razborov, 2016]. Finally, we outline several conjectures that would imply similar supercritical tradeoffs between size and depth in circuit complexity via lifting theorems.Noah Fleming, Toniann Pitassi, Robert Robere, Mark Bravermanwork_lsc7zmdzendwvhrravjrnuc5hyTue, 25 Jan 2022 00:00:00 GMTRanking Sets of Objects: The Complexity of Avoiding Impossibility Results
https://scholar.archive.org/work/ebd2un5hjzgfllcicctyrdtqgu
The problem of lifting a preference order on a set of objects to a preference order on a family of subsets of this set is a fundamental problem with a wide variety of applications in AI. The process is often guided by axioms postulating properties the lifted order should have. Well-known impossibility results by Kannai and Peleg and by Barbera and Pattanaik tell us that some desirable axioms – namely dominance and (strict) independence – are not jointly satisfiable for any linear order on the objects if all non-empty sets of objects are to be ordered. On the other hand, if not all non-empty sets of objects are to be ordered, the axioms are jointly satisfiable for all linear orders on the objects for some families of sets. Such families are very important for applications as they allow for the use of lifted orders, for example, in combinatorial voting. In this paper, we determine the computational complexity of recognizing such families. We show that it is \Pi_2^p-complete to decide for a given family of subsets whether dominance and independence or dominance and strict independence are jointly satisfiable for all linear orders on the objects if the lifted order needs to be total. Furthermore, we show that the problem remains coNP-complete if the lifted order can be incomplete. Additionally, we show that the complexity of these problems can increase exponentially if the family of sets is not given explicitly but via a succinct domain restriction. Finally, we show that it is NP-complete to decide for a family of subsets whether dominance and independence or dominance and strict independence are jointly satisfiable for at least one linear order on the objects.Jan Malywork_ebd2un5hjzgfllcicctyrdtqguTue, 04 Jan 2022 00:00:00 GMTDigital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques
https://scholar.archive.org/work/42h3azzg3nc55dcvtqchuxx4jm
I, Abiodun Abdullahi SOLANKE, declare that this thesis titled, "Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques" and the work presented in it are my own. I confirm that: • This work was completed in whole or mainly while in candidature for doctoral degree at this university. • Where any part of this thesis has been presented before for a degree or other qualification at this University or another institution, this has been explicitly stated. • Where I have consulted the published work of others, proper citation is always provided. • Where I have quoted the work of others, I always provide the source. Except for such quotations, this thesis is entirely my own work. • I have acknowledged all major sources of help. • Where the thesis is based on work that I did in collaboration with others, I have made it clear exactly what they did and what I contributed. v Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term "Digital Forensics AI" (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings.Abiodun Abdullahi Solankework_42h3azzg3nc55dcvtqchuxx4jmEnvironment-sensitivity functions for gross primary productivity in light use efficiency models
https://scholar.archive.org/work/j5ziikr2pzcxvbesskwkm4mnoa
The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO 2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO 2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.Shanning Bao, Thomas Wutzler, Sujan Koirala, Matthias Cuntz, Andreas Ibrom, Simon Besnard, Sophia Walther, Ladislav Šigut, Alvaro Moreno, Ulrich Weber, Georg Wohlfahrt, Jamie Cleverly, Mirco Migliavacca, William Woodgate, Lutz Merbold, Elmar Veenendaal, Nuno Carvalhaiswork_j5ziikr2pzcxvbesskwkm4mnoaMulti-label Rule Learning
https://scholar.archive.org/work/ho3ikabigbdthf36aefftxxigm
Research on multi-label classification is concerned with developing and evaluating algorithms that learn a predictive model for the automatic assignment of data points to a subset of predefined class labels. This is in contrast to traditional classification settings, where individual data points cannot be assigned to more than a single class. As many practical use cases demand a flexible categorization of data, where classes must not necessarily be mutually exclusive, multi-label classification has become an established topic of machine learning research. Nowadays, it is used for the assignment of keywords to text documents, the annotation of multimedia files, such as images, videos, or audio recordings, as well as for diverse applications in biology, chemistry, social network analysis, or marketing. During the past decade, increasing interest in the topic has resulted in a wide variety of different multi-label classification methods. Following the principles of supervised learning, they derive a model from labeled training data, which can afterward be used to obtain predictions for yet unseen data. Besides complex statistical methods, such as artificial neural networks, symbolic learning approaches have not only been shown to provide state-of-the-art performance in many applications but are also a common choice in safety-critical domains that demand human-interpretable and verifiable machine learning models. In particular, rule learning algorithms have a long history of active research in the scientific community. They are often argued to meet the requirements of interpretable machine learning due to the human-legible representation of learned knowledge in terms of logical statements. This work presents a modular framework for implementing multi-label rule learning methods. It does not only provide a unified view of existing rule-based approaches to multi-label classification, but also facilitates the development of new learning algorithms. Two novel instantiations of the framework are investigated to demonstrat [...]Michael Rappwork_ho3ikabigbdthf36aefftxxigmComparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases
https://scholar.archive.org/work/33i5dl5xerhcfotw2yaue7vfiy
Chronic Diseases are the most dangerous diseases for humans and have significant effects on human life. Chronic Diseases like heart disease & Diabetes are the main causes of death. Precise diagnosis of these diseases on time is very significant for maintaining a healthy life. A comparative study of different machine learning classifiers for chronic disease prediction viz Heart Disease & Diabetes Disease is done in this paper. This paper forms the basis of understanding the difficulty of the domain and the amount of efficiency achieved by the various methods recently.Rajesh Singh, Anita Gehlot, Dharam Buddhiwork_33i5dl5xerhcfotw2yaue7vfiyThe approximate degree of DNF and CNF formulas
https://scholar.archive.org/work/i4aacyec4nbidio2kjrqzjlcxa
The approximate degree of a Boolean function f : {0, 1} n → {0, 1} is the minimum degree of a real polynomial p that approximates f pointwise: | f (x) − p(x)| ⩽ 1/3 for all x ∈ {0, 1} n . For any δ > 0, we construct DNF and CNF formulas of polynomial size with approximate degree Ω(n 1−δ ), essentially matching the trivial upper bound of n. This fully resolves the approximate degree of constantdepth circuits (AC 0 ), a question that has seen extensive research over the past 10 years. Prior to our work, an Ω(n 1−δ ) lower bound was known only for AC 0 circuits of depth that grows with 1/δ (Bun and Thaler, FOCS 2017). Furthermore, the DNF and CNF formulas that we construct are the simplest possible in that they have constant width. Our result gives the first near-linear lower bounds on the boundederror communication complexity of polynomial-size DNF and CNF formulas in the challenging k-party number-on-the-forehead model and two-party quantum model: Ω(n/4 k k 2 ) 1−δ and Ω(n 1−δ ), respectively, where δ > 0 is any constant. Our lower bounds are essentially optimal. Analogous to above, such lower bounds were previously known only for AC 0 circuits of depth that grows with 1/δ .Alexander A. Sherstovwork_i4aacyec4nbidio2kjrqzjlcxa