Miracle-dz/UniFAR-FTUs huggingface dataset:
Luring of Adversarial Perturbations

The growing interest for adversarial examples, i.e. maliciously modified examples which fool a classifier, has resulted in many defenses intended to detect them, render them inoffensive or make the model more robust against them. In this paper, we pave the way towards a new approach to defend a distant system against adversarial examples, which we name the luring of adversarial perturbations. A component is included in the target model to form an augmented and equally accurate version of it. This additional component is designed to be removable and to give false indications on the way to fool the target model alone: the adversary is tricked into fooling the augmented version of the target model, and not the target model. We explain the intuition of our defense with the principle of the luring effect, inspired by the notion of robust and non-robust features, and experimentally justify its validity. Eventually, we propose a simple prediction strategy which takes advantage of this effect, and show that our defense scheme on MNIST, SVHN and CIFAR10 can efficiently thwart an adversary using state-of-the-art attacks and allowed to perform large perturbations.
Learning Unsplit-field-based PML for the FDTD Method by Deep Differentiable Forest

Alternative unsplit-filed-based absorbing boundary condition (ABC) computation approach for the finite-difference time-domain (FDTD) is efficiently proposed based on the deep differentiable forest. The deep differentiable forest (DDF) model is introduced to replace the conventional perfectly matched layer (PML) ABC during the computation process of FDTD. The field component data on the interface of traditional PML are adopted to train the DDF-based PML model. DDF has the advantages of both trees and neural networks. Its tree structure is easy to use and explain for the numerical PML data. It has full differentiability like neural networks. DDF could be trained by powerful techniques from deep learning. So compared to the traditional PML implementation, the proposed method can greatly reduce the size of FDTD physical domain and the calculation complexity of FDTD due to the novel model which only involves the one-cell thickness of boundary layer. Numerical simulations have been carried out to benchmark the performance of the proposed approach. Numerical results illustrate that the proposed method can not only easily replace the traditional PML, but also be integrated into the FDTD computation process with satisfactory numerical accuracy and compatibility to the FDTD.
Pseudo Bayesian Mixed Models under Informative Sampling

Abstract When random effects are correlated with survey sample design variables, the usual approach of employing individual survey weights (constructed to be inversely proportional to the unit survey inclusion probabilities) to form a pseudo-likelihood no longer produces asymptotically unbiased inference. We construct a weight-exponentiated formulation for the random effects distribution that achieves approximately unbiased inference for generating hyperparameters of the random effects. We contrast our approach with frequentist methods that rely on numerical integration to reveal that the pseudo Bayesian method achieves both unbiased estimation with respect to the sampling design distribution and consistency with respect to the population generating distribution. Our simulations and real data example for a survey of business establishments demonstrate the utility of our approach across different modeling formulations and sampling designs. This work serves as a capstone for recent developmental efforts that combine traditional survey estimation approaches with the Bayesian modeling paradigm and provides a bridge across the two rich but disparate sub-fields.
Straggler-Aware Distributed Learning: Communication–Computation Latency Trade-Off

When gradient descent (GD) is scaled to many parallel workers for large-scale machine learning applications, its per-iteration computation time is limited by straggling workers. Straggling workers can be tolerated by assigning redundant computations and/or coding across data and computations, but in most existing schemes, each non-straggling worker transmits one message per iteration to the parameter server (PS) after completing all its computations. Imposing such a limitation results in two drawbacks: over-computation due to inaccurate prediction of the straggling behavior, and under-utilization due to discarding partial computations carried out by stragglers. To overcome these drawbacks, we consider multi-message communication (MMC) by allowing multiple computations to be conveyed from each worker per iteration, and propose novel straggler avoidance techniques for both coded computation and coded communication with MMC. We analyze how the proposed designs can be employed efficiently to seek a balance between the computation and communication latency. Furthermore, we identify the advantages and disadvantages of these designs in different settings through extensive simulations, both model-based and real implementation on Amazon EC2 servers, and demonstrate that proposed schemes with MMC can help improve upon existing straggler avoidance schemes.
Study of recent outburst in the Be/X-ray binary RX J0209.6−7427 with AstroSat: a new ultraluminous X-ray pulsar in the Magellanic Bridge?

We present the timing and spectral studies of RX J0209.6–7427 during its rare 2019 outburst using observations with the Soft X-ray Telescope (SXT) and Large Area X-ray Proportional Counter (LAXPC) instruments on the AstroSat satellite. Pulsations having a periodicity of 9.29 s were detected for the first time by the NICER mission in the 0.2–10 keV energy band and, as reported here, by AstroSat over a broad energy band covering 0.3–80 keV. The pulsar exhibits a rapid spin-up during the outburst. Energy resolved folded pulse profiles are generated in several energy bands in 3–80 keV. To the best of our knowledge this is the first report of the timing and spectral characteristics of this Be binary pulsar in hard X-rays. There is suggestion of evolution of the pulse profile with energy. The energy spectrum of the pulsar is determined and from the best-fitting spectral values, the X-ray luminosity of RX J0209.6−7427 is inferred to be 1.6 × 1039 erg s−1. Our timing and spectral studies suggest that this source has features of an ultraluminous X-ray pulsar in the Magellanic Bridge. Details of the results are presented and discussed in terms of the current ideas.
Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset

The Neural Covidex is a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset (CORD-19) curated by the Allen Institute for AI. It exists as part of a suite of tools we have developed to help domain experts tackle the ongoing global pandemic. We hope that improved information access capabilities to the scientific literature can inform evidence-based decision making and insight generation.
Dense Passage Retrieval for Open-Domain Question Answering

Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.
Uncrowded Hypervolume-Based Multiobjective Optimization with Gene-Pool Optimal Mixing

Abstract Domination-based multiobjective (MO) evolutionary algorithms (EAs) are today arguably the most frequently used type of MOEA. These methods, however, stagnate when the majority of the population becomes nondominated, preventing further convergence to the Pareto set. Hypervolume-based MO optimization has shown promising results to overcome this. Direct use of the hypervolume, however, results in no selection pressure for dominated solutions. The recently introduced Sofomore framework overcomes this by solving multiple interleaved single-objective dynamic problems that iteratively improve a single approximation set, based on the uncrowded hypervolume improvement (UHVI). It thereby however loses many advantages of population-based MO optimization, such as handling multimodality. Here, we reformulate the UHVI as a quality measure for approximation sets, called the uncrowded hypervolume (UHV), which can be used to directly solve MO optimization problems with a single-objective optimizer. We use the state-of-the-art gene-pool optimal mixing evolutionary algorithm (GOMEA) that is capable of efficiently exploiting the intrinsically available grey-box properties of this problem. The resulting algorithm, UHV-GOMEA, is compared with Sofomore equipped with GOMEA, and the domination-based MO-GOMEA. In doing so, we investigate in which scenarios either domination-based or hypervolume-based methods are preferred. Finally, we construct a simple hybrid approach that combines MO-GOMEA with UHV-GOMEA and outperforms both.
Prescribing Pattern and drug utilization study in inpatients of department of Orthopaedics in a rural teaching hospital of Ujjain, Madhya Pradesh, India

Background: Drug utilization study can increase our understanding of how drugs are being used. This study was done to evaluate the current drug prescribing trend in management of indoor patient of orthopaedic department and to comment on rationality of the prescribed medicines.Methods: This is a prospective observational study conducted for 12 months, in Chandrikaben Rashmikant Gardi Hospital, a 600 bedded tertiary care rural based, teaching hospital. Total 611 patients were included in this study.Results: Total 5416 drugs were prescribed in 611 prescriptions. Average number of drugs per prescription was 8.86. Average duration of prescription was 10.7 days. Percentage of drugs prescribed by generic name was 28.8%. Percentage of encounter with an Antibiotic prescribed was 60.23% i.e. out of 611 prescriptions antibiotics were prescribed in 368. Percentage of encounter with an Injection prescribed was 63% which means out of 611 prescriptions, injectables were prescribed in 385. Percentage of drugs prescribed from National Essential Medicine List was 52.63%. Percentage of drugs prescribed from WHO model List was 32.46%. Diclofenac (14.25%) was most commonly prescribed drug. Incidence of polypharmacy was quite high in context of Analgesics. Almost 40% of prescriptions had 2 drugs. Orally prescribed Analgesics were 62.6%, Injectables 34.38% and Topical 3.02%. Conclusions: This study reveals that the pattern of prescription in terms of rationality is poor. Special attention needs to be given to the irrational prescribing in terms of polypharmacy and long duration. Continuing medical education regarding appropriate use of drugs, knowledge of adverse effects and standard prescription guidelines will play pivotal role in rational prescription of drugs.
Revisiting the thermal relaxation of neutron stars

In this work we revisit the thermal relaxation process for neutron stars. Such process is associated with the thermal coupling between the core and the crust of neutron stars. The thermal relaxation, which takes place at around 10 -- 100 years, is manifested as a sudden drop of the star's surface temperature. Such drop is smooth for slow cooling objects and very sharp for those with fast cooling. In our study we focus particularly on the cooling of neutron stars whose mass is slightly greater than the value above which the direct Urca (DU) process sets in. Considering different mechanisms for neutrino production in each region of the star, and working with equations of state with different properties, we solve the thermal evolution equation and calculate the thermal relaxation time for ample range of neutron star masses. By performing a comprehensive study of neutron stars just above the onset of the direct Urca process we show that stars under these conditions exhibit a peculiar thermal relaxation behavior. We demonstrate that such stars exhibit an abnormally late relaxation time, characterized by a second drop of its surface temperature taking place a later ages. We qualify such behavior by showing that it is associated with limited spatial distribution of the DU process is such stars. We show that as the star's mass increase, the DU region also grows and the start exhibits the expected behavior of fast cooling stars. Finally we show that one can expect high relaxation times for stars in which the DU process takes place in a radius not larger than 3 km.
A Revisit to Ordered Statistics Decoding: Distance Distribution and Decoding Rules

This paper revisits the ordered statistics decoding (OSD). It provides a comprehensive analysis of the OSD algorithm by characterizing the statistical properties, evolution and the distribution of the Hamming distance and weighted Hamming distance from codeword estimates to the received sequence in the reprocessing stages of the OSD algorithm. We prove that the Hamming distance and weighted Hamming distance distributions can be characterized as mixture models capturing the decoding error probability and code weight enumerator. Simulation and numerical results show that our proposed statistical approaches can accurately describe the distance distributions. Based on these distributions and with the aim to reduce the decoding complexity, several techniques, including stopping rules and discarding rules, are proposed, and their decoding error performance and complexity are accordingly analyzed. Simulation results for decoding various eBCH codes demonstrate that the proposed techniques can significantly reduce the decoding complexity with a negligible loss in the decoding error performance.
Bounding the mim‐width of hereditary graph classes

A large number of NP ‐hard graph problems are solvable in XP time when parameterized by some width parameter. Hence, when solving problems on special graph classes, it is helpful to know if the graph class under consideration has bounded width. In this paper we consider maximum‐induced matching width (mim‐width), a particularly general width parameter that has a number of algorithmic applications whenever a decomposition is “quickly computable” for the graph class under consideration. We start by extending the toolkit for proving (un)boundedness of mim‐width of graph classes. By combining our new techniques with known ones we then initiate a systematic study into bounding mim‐width from the perspective of hereditary graph classes, and make a comparison with clique‐width, a more restrictive width parameter that has been well studied. We prove that for a given graph H , the class of H ‐free graphs has bounded mim‐width if and only if it has bounded clique‐width. We show that the same is not true for ( H 1 , H 2 ) ‐free graphs. We identify several general classes of ( H 1 , H 2 ) ‐free graphs having unbounded clique‐width, but bounded mim‐width; moreover, we show that a branch decomposition of constant mim‐width can be found in polynomial time for these classes. Hence, these results have algorithmic implications: when the input is restricted to such a class of ( H 1 , H 2 ) ‐free graphs, many problems become polynomial‐time solvable, including classical problems, such as k ‐ Colouring and Independent Set, domination‐type problems known as Locally Checkable Vertex Subset and Vertex Partitioning (LC‐VSVP) problems, and distance versions of LC‐VSVP problems, to name just a few. We also prove a number of new results showing that, for certain H 1 and H 2 , the class of ( H 1 , H 2 ) ‐free graphs has unbounded mim‐width. Boundedness of clique‐width implies boundedness of mim‐width. By combining our results with the known bounded cases for clique‐width, we present summary theorems of the current state of the art for the boundedness of mim‐width for ( H 1 , H 2 ) ‐free graphs. In particular, we classify the mim‐width of ( H 1 , H 2 ) ‐free graphs for all pairs ( H 1 , H 2 ) with ∣ V ( H 1 ) ∣ + ∣ V ( H 2 ) ∣ ≤ 8 . When H 1 and H 2 are connected graphs, we classify all pairs ( H 1 , H 2 ) except for one remaining infinite family and a few isolated cases.
Design Considerations and Fabrication Challenges of Surface Electrode Ion Trap with TSV Integration

Surface electrode ion trap with through-silicon-via (TSV) integration enables 3D stacking of ion trap chip on an interposer to eliminate the wire-bonds on the surface electrodes and also addresses the challenge of the ever increasing complexity of surface electrode design with low-parasitic and high-density interconnect requirements. In this work, we demonstrate the design and fabrication of TSV integrated surface electrode ion trap on a 300-mm Si wafer platform. By designing the TSV arrays directly underneath the surface electrodes, the surface electrode foot print is reduced and the TSV traps show better RF performance compared to the planar traps with wire-bonding pads.
Decoding Semantic Categories from EEG Activity in Object-Based Decision Tasks

This study explores the potential of decoding semantic categories from EEG-activity for the use in Silent Speech Brain-Computer Interfaces (BCI). We used object-based decision tasks to evoke conscious semantic processing for five different semantic categories in the participants cerebral cortical structures and implemented different feature extraction and classification methods to evaluate possible setups for semantic category detection in BCIs. All of the tested classification methods exceeded the chance level for training and testing on the data of the individual and even for a cross-subject condition. The best individual accuracy achieved was 84.61% for a Common Spatial Pattern (CSP) feature extraction method and Random Forrest (RF) classifier presented for the first time in a 5-class classification task illustrating the potential of this approach for possible future use in Silent Speech BCIs.
On the Value of the Non-negativity Constraint in CT

Model based iterative reconstruction (MBIR) has attracted a lot of attention in X-ray computed tomography (CT). A strength of MBIR over classical filtered backprojection is its ability to apply constraints over the voxel values, which can be critical to improve image quality. Given that the linear attenuation coefficient of X-rays is non-negative, applying a non-negativity constraint appears very natural. Indeed, most MBIR-related publications in CT invoke it. However, there is little information in the literature on the intrinsic value of the non-negativity constraint. In this work, we investigate this question in the context of two challenging imaging scenarios: (i) heavy truncation, (ii) photon starvation due to a metal implant. Real CT data sets are used, and the effect of the constraint is examined in terms of closeness to a preferred ground truth. Additionally, convergence properties are studied. The reconstruction is performed using ordered subsets to obtain a result in a manner that can be practical for clinical routine applications. The experiments are an extension of a previous work where we extensively examined the effect of the non-negativity constraint. This was done using a provably converging reconstruction algorithm applied with a large number of iterations to nearly reach convergence. The results presented here are more nuanced than those observed in that examination. They show that, with ordered subsets, the nonnegativity constraint can be beneficial or detrimental depending on the imaging scenario. However, the observed differences tend to be much smaller than the overall level of inaccuracy in the image. We also find that the non-negativity constraint can prevent divergence when using ordered subsets, but this gain does not translate into a satisfactory reconstruction.
Device Free Human Activity Recognition using WiFi Channel State Information

Human Activity Recognition (HAR) is a rather broad research area. While there exist solutions based on sensors and vision-based technologies, these solutions suffer from considerable limitations. Thus in-order to mitigate or avoid these limitations, device free solutions based on radio signals like home WiFi are considered. Recently, channel state information (CSI), available in WiFi networks have been proposed for fine-grained analysis. We are able to detect the human activities like Walk, Stand, Sit, Run, etc. in a Line of Sight scenario (LOS) and a Non Line of Sight (N-LOS) scenario within an indoor environment. We propose two algorithms - one using a support vector machine (SVM) for classification and another one using a long short-term memory (LSTM) recurrent neural network. While the former uses sophisticated pre-processing and feature extraction techniques the latter processes the raw data directly (after denoising with wavelets). We show that it is possible to characterize activities and / or human body presence with high accuracy and we compare both approaches with regards to accuracy and performance.
A Review Paper On The Application Of Knowledge Graph On Various Service Providing Platforms.

During the past decade or so, knowledge graphs have stealthily made way into our daily lives, whether through voice assistants (the likes of Alexa, Google Assistant or Siri), spontaneous search results or customized personal shopping experiences through online stores recommenders. Today, we are surrounded and constantly interacting with knowledge graphs on a regular basis. The scope and impact of knowledge graphs and underlying graph databases are still a mystery to people. Considering its smooth entry into our lives, most of us are unaware of how dependent we actually are on the technology. In this paper, we have shown the working of the Google knowledge graph and how the knowledge graph works as the most effective recommendation system. We have discussed how it finds its way through daily life in various fields like Banking, Social media (Facebook, LinkedIn, Netflix), Food application (Uber Eats) and the Healthcare sector.
Self-initiation of visual object motion has little influence on visual mislocalization by auditory stimuli

Sense of agency (SoA) is the subjective feeling that a person causes a change in the external world. The influence of SoA on crossmodal integration has not yet examined thoroughly. We investigated the effects of auditory stimuli on visual localization and its interaction with SoA. In Experiment 1, participants localized the disappearing position of a moving dot while hearing a continuous sound. The sound duration was manipulated. Participants performed the localization task after self-initiating the dot motion (higher SoA) or after the dot moving automatically (lower SoA). Results showed that localization of disappearing position of the dot was clearly manipulated by the sound duration, but auditory-based mislocalization was not influenced by the presence of action. In attempt to attenuate the reliability of visual signals for localization, we made visual stimuli flickered in Experiment 2. However, the results were similar to those of Experiment 1. Our study indicated that the presence of action (or SoA) might not influence auditory-visual integration. We discussed this possibility along with limitations of the present study.