1,123 Hits in 2.3 sec

130 GeV Gamma Ray Signal in NMSSM by Internal Bremsstrahlung [article]

Gaurav Tomar, Subhendra Mohanty, Soumya Rao
2013 arXiv   pre-print
fit the γ-ray signal.  ...  There is a possible γ-ray signal at 130 GeV coming from the Galactic Center as seen by Fermi-LAT experiment. We give a SUSY dark matter model to explain this γ-ray feature in NMSSM.  ...  There are hints of possible signals of dark matter in cosmic ray and like the 130 GeV gamma ray signal from the Galactic Center, observed by Fermi-LAT experiment [17] , which can be explained by the dark  ... 
arXiv:1306.3646v1 fatcat:skhlnvqtqve5bm67tusb6sdbwu

Sequential skewing

Soumya Ray, David Page
2004 Twenty-first international conference on Machine learning - ICML '04  
Our previous work introduced an approach called Skewing (Page & Ray, 2003) , which attempts to alleviate the myopia of greedy tree learners by changing the split selection function.  ...  Our previous work observed that, in contrast to skewing, other methods of reweighting (such as boosting) or resampling (such as bagging) did not make hard functions easier to learn (Page & Ray, 2003)  ...  could be found 1: N ⇐ Entropy of class variable in D 2: v ⇐ Variable with max gain in D 3: g ⇐ Gain of v in D 4: if g < G × N then Skewing Algorithm The motivation for the Skewing procedure (Page & Ray  ... 
doi:10.1145/1015330.1015392 dblp:conf/icml/RayP04 fatcat:afjmrly3mjgirap72a542sktby

Standardless EDXRF technique using bremsstrahlung radiation from a transmission type x-ray generator [article]

Soumya Chatterjee, T. Nandi, Debasis Mitra
2020 arXiv   pre-print
The bremsstrahlung radiation can be taken from a small, portable, transmission type x-ray generator.  ...  In this case knowledge of incoming x-ray flux, geometry of experimental arrangement are not required.  ...  EDXRF system may consist either a radioactive x-ray source or an x-ray generator, an x-ray detection device and a data acquisition system.  ... 
arXiv:1911.10396v2 fatcat:5lxnja5w5fc4xag7cdxxia5wim

Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing [article]

Boaz Shmueli, Jan Fell, Soumya Ray, Lun-Wei Ku
2021 arXiv   pre-print
The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding the use of crowdworkers within the NLP research community is typically confined in scope to issues related to labor conditions such as fair pay. We draw attention to the lack of ethical considerations related to the various tasks performed by workers, including labeling, evaluation, and production. We find that the
more » ... al Rule, the common ethical framework used by researchers, did not anticipate the use of online crowdsourcing platforms for data collection, resulting in gaps between the spirit and practice of human-subjects ethics in NLP research. We enumerate common scenarios where crowdworkers performing NLP tasks are at risk of harm. We thus recommend that researchers evaluate these risks by considering the three ethical principles set up by the Belmont Report. We also clarify some common misconceptions regarding the Institutional Review Board (IRB) application. We hope this paper will serve to reopen the discussion within our community regarding the ethical use of crowdworkers.
arXiv:2104.10097v1 fatcat:qjlcivrkrfhmffyauvnxipmcfy

Rankboost $$+$$ + : an improvement to Rankboost

Harold Connamacher, Nikil Pancha, Rui Liu, Soumya Ray
2019 Machine Learning  
B Harold Connamacher Nikil Pancha Rui Liu Soumya Ray Algorithm 1 Template Pseudocode for Rb-d, Rb-c and  ... 
doi:10.1007/s10994-019-05826-x fatcat:5n5awur3dnhynmbyslb3z3725a

Reactive Supervision: A New Method for Collecting Sarcasm Data [article]

Boaz Shmueli, Lun-Wei Ku, Soumya Ray
2020 arXiv   pre-print
Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data. We introduce reactive supervision, a novel data collection method that utilizes the dynamics of online conversations to overcome the limitations of existing data collection techniques. We use the new method to create and release a first-of-its-kind large dataset of tweets with sarcasm perspective labels and new contextual features. The dataset is expected to advance sarcasm detection
more » ... Our method can be adapted to other affective computing domains, thus opening up new research opportunities.
arXiv:2009.13080v1 fatcat:pdvcvfivyvasdellljjkgmf3g4

Offline reinforcement learning with task hierarchies

Devin Schwab, Soumya Ray
2017 Machine Learning  
While some work has explored how to learn pseudo-rewards (Schultink et al 2008; Cao and Ray 2012) , in this work we show that using offline RL, we can sidestep this issue entirely.  ...  We use two problem domains in our experiments: Taxi-World (Dietterich 2000) and Resource-Collection (Cao and Ray 2012) . 2 We use several variations of Resource-Collection depending on the hypothesis  ... 
doi:10.1007/s10994-017-5650-8 fatcat:7ejphetjtjflfhcfvc3y72blgu

Simulated X-ray Emission in Galaxy Clusters with Feedback from Active Galactic Nuclei [article]

Rudrani Kar Chowdhury, Soumya Roy, Suchetana Chatterjee, Nishikanta Khandai, Craig L. Sarazin, Tiziana Di Matteo
2020 arXiv   pre-print
We show that AGN are effective in displacing the hot X-ray emitting gas from the centers of groups and clusters, and that these signatures remain evident in observations of the X-ray surface brightness  ...  We construct a statistical sample of synthetic Chandra X-ray photon maps to observationally characterize the effect of AGN on the ambient medium.  ...  RKC thanks Chandra X-ray Observatory helpdesk system, Dr. Nicholas P. Lee and Dr. Antonella Fruscione for helping us to work with the CIAO software package.  ... 
arXiv:2011.05580v1 fatcat:67yb2a7evngtxds7dhrzdrckji

Model-Based Reinforcement Learning [chapter]

Soumya Ray, Prasad Tadepalli
2014 Encyclopedia of Machine Learning and Data Mining  
Model-based Reinforcement Learning refers to learning optimal behavior indirectly by learning a model of the environment by taking actions and observing the outcomes that include the next state and the immediate reward. The models predict the outcomes of actions and are used in lieu of or in addition to interaction with the environment to learn optimal policies.
doi:10.1007/978-1-4899-7502-7_561-1 fatcat:4pwzznqsefhq3e2oqs2mavvxp4

Model-Based Reinforcement Learning [chapter]

Soumya Ray, Prasad Tadepalli
2017 Encyclopedia of Machine Learning and Data Mining  
Indirect Reinforcement Learning Definition Model-based Reinforcement Learning refers to learning optimal behavior indirectly by learning a model of the environment by taking actions and observing the outcomes that include the next state and the immediate reward. The models predict the outcomes of actions and are used in lieu of or in addition to interaction with the environment to learn optimal policies. Motivation and Background Reinforcement Learning (RL) refers to learning to behave
more » ... in a stochastic environment by taking actions and receiving rewards [1]. The environment is assumed Markovian in that there is a fixed probability of the next state given the current state and the agent's action. The agent also receives an immediate reward based on the current state and the action. Models of the next-state distribution and the immediate rewards are referred to as "action models" and, in general, are not known to the learner. The agent's goal is to take actions, observe the outcomes including rewards and next states, and learn a policy or a mapping from states to actions that optimizes some performance measure. Typically the performance measure is the expected total reward in episodic domains, and the expected average reward per time step or expected discounted total reward in infinite-horizon domains. The theory of Markov Decision Processes (MDPs) implies that under fairly general conditions, there is a stationary policy, i.e., a time-invariant mapping from states to actions, that maximizes each of the above reward measures. Moreover, there are MDP solution algorithms, e.g., value iteration and policy iteration [2], which can be used to solve the MDP exactly given the action models. Assuming that the number of states is not exceedingly high, this suggests a straightforward approach for model-based reinforcement learning. The models can be learned by interacting with the environment by taking actions, observing the resulting states and rewards, and estimating the parameters of the action models through maximum likelihood methods. Once the models are estimated to a desired accuracy, the MDP solution algorithms can be run to learn the optimal policy. One weakness of the above approach is that it seems to suggest that a fairly accurate model needs to be learned over the entire domain to learn a good policy. Intuitively it seems that we should be able to get by without learning highly accurate models for suboptimal actions. A related problem is that the method does not suggest how best to explore the domain, i.e., which states to visit and which actions to execute to quickly learn an optimal policy. A third issue is one of scaling these methods, including model learning, to very large state spaces with billions of states. The remaining sections outline some of the approaches explored in the literature to solve these problems.
doi:10.1007/978-1-4899-7687-1_561 fatcat:klqdisxo4bffhelmyckzzsqz64

Learning Bayesian Network Structure from Correlation-Immune Data [article]

Eric Lantz, Soumya Ray, David Page
2012 arXiv   pre-print
The motivation for the skewing technique (Page & Ray, 2003) lies in the following observation.  ...  decision tree learners, it was empirically observed that the technique was able to accurately learn functions that were CI under the uniform distribution with only modest amounts of training data (Page & Ray  ... 
arXiv:1206.5271v1 fatcat:oqhwtkh3erbepi2jd4fwuww6lm

Detecting an association between gamma ray and gravitational wave bursts

Lee Samuel Finn, Soumya D. Mohanty, Joseph D. Romano
1999 Physical Review D, Particles and fields  
If γ-ray bursts (GRBs) are accompanied by gravitational wave bursts (GWBs) the correlated output of two gravitational wave detectors evaluated in the moments just prior to a GRB will differ from that evaluated  ...  Gamma Ray Bursts (GRBs), which are known to lie at cosmological distances, likely arise from shocks in a relativistic fireball that is triggered by rapid accretion on to a newly formed black hole [1]  ...  Gamma-ray bursts (GRBs), which are believed to be associated with the violent formation of a stellar mass black hole, may well be immediately preceded by a gravitational wave burst (GWB).  ... 
doi:10.1103/physrevd.60.121101 fatcat:wsillehoabh75acapl57a4yrzm

A comparative study for turbidity removal by electrocoagulation and chemical coagulation

Soumya Kanta Ray, Chanchal Majumder
2020 Zenodo  
Civil Engineering Department, Indian Institute of Engineering Science and Technology, Shibpur, Howrah-711 103, West Bengal, India E-mail:, Manuscript received online 23 December 2019, accepted 25 March 2020 Comparative study between electrocoagulation (EC) and chemical coagulation (CC) was done to assess the relative performance of turbidity removal in batch mode. The single parameter studies were carried out to assess the effect of pH, volume,
more » ... nitial turbidity and coagulant dose on turbidity removal by both EC and CC. It was observed that for equivalent dose of aluminum, EC (90.3% removal) performed better than CC (77.67% removal). It was found that during EC, turbidity removal increased when charge loading was increased. It was the first time reported in this study that for a constant charge loading the turbidity removal was decreased when current density was increased.
doi:10.5281/zenodo.5643537 fatcat:3iy7fhupgjchdoa2c3srs662lq

Protective effect of creatine against inhibition by methylglyoxal of mitochondrial respiration of cardiac cells

Soumya Sinha ROY, Swati BISWAS, Manju RAY, Subhankar RAY
2003 Biochemical Journal  
doi:10.1042/bj20021576 pmid:12605598 pmcid:PMC1223401 fatcat:hrwtldn42rau5kotoicohe2ow4

Temperature Dependent Structural Evolution of WSe2: A Synchrotron X-ray Diffraction Study

Sinu Mathew, Aben Regi Abraham, Sandhya Chintalapati, Soumya Sarkar, Boby Joseph, Thirumalai Venkatesan
2020 Condensed Matter  
Temperature dependent X-Ray powder diffraction (XRPD) measurements were performed at the X-ray diffraction beamline (XRD1) of the Elettra Synchrotron, Trieste (Italy).  ...  During the experiments, Xpress beamline was set to provide intense monochromatic X-ray beam of wavelength 0.495 Å.  ... 
doi:10.3390/condmat5040076 fatcat:tysp2xncybfpzg44rkev3uagku
« Previous Showing results 1 — 15 out of 1,123 results