Miracle-dz/UniFAR-FTUs huggingface dataset:
Path weights in concentration graphs

A graphical model provides a compact and efficient representation of the association structure in a multivariate distribution by means of a graph. Relevant features of the distribution are represented by vertices, edges and higher-order graphical structures such as cliques or paths. Typically, paths play a central role in these models because they determine the dependence relationships between variables. However, while a theory of path coefficients is available for directed graph models, little research exists on the strength of the association represented by a path in an undirected graph. Essentially, it has been shown that the covariance between two variables can be decomposed into a sum of weights associated with each of the paths connecting the two variables in the corresponding concentration graph. In this context, we consider concentration graph models and provide an extensive analysis of the properties of path weights and their interpretation. Specifically, we give an interpretation of covariance weights through their factorization into a partial covariance and an inflation factor. We then extend the covariance decomposition over the paths of an undirected graph to other measures of association, such as the marginal correlation coefficient and a quantity that we call the inflated correlation. Application of these results is illustrated with an analysis of dietary intake networks.
Stochastic Star Communication Topology in Evolutionary Particle Swarms (EPSO)

This paper reports the results of the adoption of a probabilistically defined communication structure in a special algorithm coined as EPSO – Evolutionary Particle Swarm Optimization, which is classified as an evolutionary algorithm using a particle movement rule as the recombination operator. Alternatively, EPSO may be seen as an algorithm of the family of PSO (Particle Swarm Optimization) but with a self-adaptive mechanism applied to make the weights of the movement rule evolve improving the performance of the algorithm. The paper presents results showing that a probabilistically controlled communication (to the particles of a swarm) of the location of the best-sofar point leads to better convergence and that the optimal value of the probability of communication depends on the topology of the surface being searched. Also, full communication (similar to classical PSO) has in all cases been shown to be worse than probabilistically constrained communication. This is demonstrated by comparing results in different test functions and also in the application of EPSO to an industrially relevant application – the reactive power planning in large scale power systems.
Vanishing point detection for road detection

Given a single image of an arbitrary road, that may not be well-paved, or have clearly delineated edges, or some a priori known color or texture distribution, is it possible for a computer to find this road? This paper addresses this question by decomposing the road detection process into two steps: the estimation of the vanishing point associated with the main (straight) part of the road, followed by the segmentation of the corresponding road area based on the detected vanishing point. The main technical contributions of the proposed approach are a novel adaptive soft voting scheme based on variable-sized voting region using confidence-weighted Gabor filters, which compute the dominant texture orientation at each pixel, and a new vanishing-point-constrained edge detection technique for detecting road boundaries. The proposed method has been implemented, and experiments with 1003 general road images demonstrate that it is both computationally efficient and effective at detecting road regions in challenging conditions.
Beamforming Design in Multiple-Input-Multiple-Output Symbiotic Radio Backscatter Systems

Symbiotic radio (SR) backscatter is a possible low-power wireless communication technique for future Internet of Things devices. In this letter, a multiple-input-multiple-output (MIMO) SR backscatter system is proposed, where the secondary multi-antenna transmission from the backscatter device (BD) to the receiver is riding on the primary multi-antenna transmission from the transmitter to the receiver. We investigate the beamforming design optimization problem which maximizes the sum capacity of primary and secondary transmissions under the capacity constraint of secondary transmission. In the MIMO SR backscatter system, each antenna of the SR BD reflects its received ambient radio frequency signals from all the transmitting antennas of the transmitter, which causes that the globally optimal solution is difficult to obtain. Here, we derive an algorithm to obtain the capacity upper bound. Furthermore, considering both primary and secondary transmissions, we propose an exact penalty method based locally optimal solution. Simulation results display that the proposed exact penalty method based locally optimal solution achieves the sum capacity which is close to the upper bound.
Testing the reality of Wigner's friend's experience

Quantum mechanics is a robust theory which pro duces highly accurate predictions. Despite its successes, it is still plagued with controversies like the measure- ment problem. In short, the measurement problem is the conflict between the evolution of the wavefunction described by Schro¨dinger’s equation and the apparent collapse of the wavefunction after a measurement is done. The Wigner’s friend paradox illustrates the conflict be- tween the two rules. In the thought experiment, we consider an observer (Wigner’s friend) who is able to per- form measurement on the quantum system and assign a quantum state. Wigner is a super-observer who is able to measure the laboratory his friend is in and also as- sign a quantum state. This process, however, leads to a quantum state that does not ascribe a well-defined value to the outcome of the friend’s observation, in apparent contradiction with the friend’s perspective.
Data‐Driven Materials Science: Status, Challenges, and Perspectives

Data‐driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high‐throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data‐driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data‐driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.
Exploration by Optimisation in Partial Monitoring

We provide a simple and efficient algorithm for adversarial $k$-action $d$-outcome non-degenerate locally observable partial monitoring game for which the $n$-round minimax regret is bounded by $6(d+1) k^{3/2} \sqrt{n \log(k)}$, matching the best known information-theoretic upper bound. The same algorithm also achieves near-optimal regret for full information, bandit and globally observable games.
PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search

Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture. In this paper, we present a novel approach, namely, Partially-Connected DARTS, by sampling a small part of super-network to reduce the redundancy in exploring the network space, thereby performing a more efficient search without comprising the performance. In particular, we perform operation search in a subset of channels while bypassing the held out part in a shortcut. This strategy may suffer from an undesired inconsistency on selecting the edges of super-net caused by sampling different channels. We alleviate it using edge normalization, which adds a new set of edge-level parameters to reduce uncertainty in search. Thanks to the reduced memory cost, PC-DARTS can be trained with a larger batch size and, consequently, enjoys both faster speed and higher training stability. Experimental results demonstrate the effectiveness of the proposed method. Specifically, we achieve an error rate of 2.57% on CIFAR10 with merely 0.1 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.2% on ImageNet (under the mobile setting) using 3.8 GPU-days for search. Our code has been made available at: this https URL.
Evaluation of Ice Conditions in the Northumberland Strait Using C and X Band Sar Imagery

The concept of a fixed crossing linking Prince Edward Island and New Brunswick has raised concern over the associated socio-economic and environmental impacts. An important element of the environmental concerns involves ice climatological effects which could in­ fluence transportation, fisheries and agriculture through possible microclimatic changes. The study described herein involved using simultaneously acquired C and X band synthetic aperture radar (SAR) data, acquired on February 19 and 20, 1988, for establishing baseline data on the ice conditions in the Northumberland Strait. These conditions included floe sizes, floe velocities, ridge frequencies and ice concentrations. In addition, imagery acquired using a real aperture radar (SLAR) system between December, 1987 and April, 1988, were used to provide a broader temporal distribution of the ice coneentrations.
And the Bit Goes Down: Revisiting the Quantization of Neural Networks

In this paper, we address the problem of reducing the memory footprint of convolutional network architectures. We introduce a vector quantization method that aims at preserving the quality of the reconstruction of the network outputs rather than its weights. The principle of our approach is that it minimizes the loss reconstruction error for in-domain inputs. Our method only requires a set of unlabelled data at quantization time and allows for efficient inference on CPU by using byte-aligned codebooks to store the compressed weights. We validate our approach by quantizing a high performing ResNet-50 model to a memory size of 5MB (20x compression factor) while preserving a top-1 accuracy of 76.1% on ImageNet object classification and by compressing a Mask R-CNN with a 26x factor.
Concordance cosmology?

We propose a new intuitive metric for evaluating the tension between two experiments, and apply it to several data sets. While our metric is non-optimal, if evidence of tension is detected, this evidence is robust and easy to interpret. Assuming a flat $\Lambda$CDM cosmological model, we find that there is a modest $2.2\sigma$ tension between the DES Year 1 results and the ${\it Planck}$ measurements of the Cosmic Microwave Background (CMB). This tension is driven by the difference between the amount of structure observed in the late-time Universe and that predicted from fitting the ${\it Planck}$ data, and appears to be unrelated to the tension between ${\it Planck}$ and local esitmates of the Hubble rate. In particular, combining DES, Baryon Acoustic Oscillations (BAO), Big-Bang Nucleosynthesis (BBN), and supernovae (SNe) measurements recovers a Hubble constant and sound horizon consistent with ${\it Planck}$, and in tension with local distance-ladder measurements. If the tension between these various data sets persists, it is likely that reconciling ${\it all}$ current data will require breaking the flat $\Lambda$CDM model in at least two different ways: one involving new physics in the early Universe, and one involving new late-time Universe physics.
Imaging doubled shot noise in a Josephson scanning tunneling microscope

We have imaged the current noise with atomic resolution in a Josephson scanning tunneling microscope with a Pb-Pb junction. By measuring the current noise as a function of applied bias, we reveal the change from single-electron tunneling above the superconducting gap energy to double-electron charge transfer below the gap energy when Andreev processes become dominant. Our spatially resolved noise maps show that this doubling occurs homogeneously on the surface, and also on impurity locations, demonstrating that indeed the charge pairing is not influenced by disruptions in the superconductor smaller than the superconducting coherence length.
Is Less Really More? Towards Better Metrics for Measuring Security Improvements Realized Through Software Debloating

Nearly all modern software suffers from bloat that negatively impacts its performance and security. To combat this problem, several automated techniques have been proposed to debloat software. A key metric used in these works to demonstrate improved security is code reuse gadget count reduction. The use of this metric is based on the prevailing idea that reducing the number of gadgets available in a software package reduces its attack surface and makes mounting a gadget-based code reuse exploit such as return-oriented programming (ROP) more difficult for an attacker. In this paper, we challenge this idea and show through a variety of realistic debloating scenarios the flaws inherent to the gadget count metric. Specifically, we demonstrate that software debloating can achieve high gadget count reduction rates, yet fail to limit an attacker's ability to construct an exploit. Worse yet, in some scenarios high gadget count reduction rates conceal instances in which software debloating makes security worse by introducing new quality gadgets. To address these issues, we propose new metrics based on quality rather than quantity for assessing the security impact of software debloaitng. We show that these metrics can be efficiently calculated with our Gadget Set Analyzer tool. Finally, we demonstrate the the utility of these metrics through a realistic debloating case study.
Associate submersions and qualitative properties of nonlinear circuits with implicit characteristics

We introduce in this paper an equivalence notion for submersions [Formula: see text], [Formula: see text] open in [Formula: see text], which makes it possible to identify a smooth planar curve with a unique class of submersions. This idea, which extends to the nonlinear setting the construction of a dual projective space, provides a systematic way to handle global implicit descriptions of smooth planar curves. We then apply this framework to model nonlinear electrical devices as classes of equivalent functions. In this setting, linearization naturally accommodates incremental resistances (and other analogous notions) in homogeneous terms. This approach, combined with a projectively-weighted version of the matrix-tree theorem, makes it possible to formulate and address in great generality several problems in nonlinear circuit theory. In particular, we tackle unique solvability problems in resistive circuits, and discuss a general expression for the characteristic polynomial of dynamic circuits at equilibria. Previously known results, which were derived in the literature under unnecessarily restrictive working assumptions, are simply obtained here by using dehomogenization. Our results are shown to apply also to circuits with memristors. We finally present a detailed, graph-theoretic study of certain stationary bifurcations in nonlinear circuits using the formalism here introduced.
Weighted Sum Power Maximization for Intelligent Reflecting Surface Aided SWIPT

The low efficiency of far-field wireless power transfer (WPT) limits the fundamental rate-energy (R-E) performance trade-off of the simultaneous wireless information and power transfer (SWIPT) system. To address this challenge, we propose in this letter a new SWIPT system aided by the emerging intelligent reflecting surface (IRS) technology. By leveraging massive low-cost passive elements that are able to reflect the signals with adjustable phase shifts, IRS achieve a high passive beamforming gain, which is appealing for drastically enhancing the WPT efficiency and thereby the R-E trade-off of SWIPT systems. We consider an IRS being deployed to assist a multi-antenna access point (AP) to serve multiple information decoding receivers (IDRs) and energy harvesting receivers (EHRs). We aim to maximize the weighted sum-power received by EHRs via jointly optimizing the transmit precoders at the AP and reflect phase shifts at the IRS, subject to the individual signal-to-interference-plus-noise ratio (SINR) constraints for IDRs. Since this problem is non-convex, we propose efficient algorithms to obtain suboptimal solutions for it. In particular, we prove that it is sufficient to send information signals only at the AP to serve both IDRs and EHRs regardless of their channel realizations. Moreover, simulation results show significant performance gains achieved by our proposed designs over benchmark schemes.
Learning a Static Bug Finder from Data

We present an alternative approach to creating static bug finders. Instead of relying on human expertise, we utilize deep neural networks to train static analyzers directly from data. In particular, we frame the problem of bug finding as a classification task and train a classifier to differentiate the buggy from non-buggy programs using Graph Neural Network (GNN). Crucially, we propose a novel interval-based propagation mechanism that leads to a significantly more efficient, accurate and scalable generalization of GNN. We have realized our approach into a framework, NeurSA, and extensively evaluated it. In a cross-project prediction task, three neural bug detectors we instantiate from NeurSA are effective in catching null pointer dereference, array index out of bound and class cast bugs in unseen code. We compare NeurSA against several static analyzers (e.g. Facebook Infer and Pinpoint) on a set of null pointer dereference bugs. Results show that NeurSA is more precise in catching the real bugs and suppressing the spurious warnings. We also apply NeurSA to several popular Java projects on GitHub and discover 50 new bugs, among which 9 have been fixed, and 3 have been confirmed.
Coulomb Gases Under Constraint: Some Theoretical and Numerical Results

We consider Coulomb gas models for which the empirical measure typically concentrates, when the number of particles becomes large, on an equilibrium measure minimizing an electrostatic energy. We study the behavior when the gas is conditioned on a rare event. We first show that the special case of quadratic confinement and linear constraint is exactly solvable due to a remarkable factorization, and that the conditioning has then the simple effect of shifting the cloud of particles without deformation. To address more general cases, we perform a theoretical asymptotic analysis relying on a large deviations technique known as the Gibbs conditioning principle. The technical part amounts to establishing that the conditioning ensemble is an I-continuity set of the energy. This leads to characterizing the conditioned equilibrium measure as the solution of a modified variational problem. For simplicity, we focus on linear statistics and on quadratic statistics constraints. Finally, we numerically illustrate our predictions and explore cases in which no explicit solution is known. For this, we use a Generalized Hybrid Monte Carlo algorithm for sampling from the conditioned distribution for a finite but large system.
Spicy In Means Spicy Out: Capsaicin-Associated Anal Burn (CAAB) in Three US Metropolitan Populations, a New Proposed Classification System

4]. One of the significant pathophysiologies of IBS is visceral hypersensitivity, which has been known to have both mechanical and chemical causes. Indeed, sensory neurons have been linked to IBS [5]. Because postprandial gastrointestinal symptoms have been reported to develop in a significant number of patients following consumption of spicy food, Gonlachanvit et al. [2] conducted a randomized, crossover study feeding subjects with and without IBS subjects and found that IBS patients were significantly more likely to exhibit hypersensitivity when chili, both in pill form and in food form, was consumed [2]. This observation has been further supported by recent mechanistic studies involving capsaicin, the active component of chilies. Akbar et al. [6] performed rectosigmoid biopsies of 23 IBS patients in addition to 22 controls and found a 3.5-fold increase in the median number of TRPV1-expressing (the capsaicin receptor) nerve fibers in IBS patients compared to non-IBS controls. Although study has been done focusing on direct rectal application of capsaicin in IBS patients [7], a regularized system for understanding its effects of oral capsaicin consumption on visceral hypersensitivity, in both IBS and non-IBS patients, has not been performed.
Graphical encoding for information visualization: using icon color, shape, and size to convey nominal and quantitative data

In producing a user interface design to visualize search results for a digital library called Envision [Nowell, France, Hix, Heath, & Fox, 1996] [Fox, Hix, Nowell, et al., 1993] [Nowell & Hix, 1993], we found that choosing graphical devices and document attributes to be encoded with each graphical device is a surprisingly difficult task. By graphical devices we mean those visual display elements (e.g., color, shape, size, position, etc.) used to convey encoded, semantic information. Research in the areas of psychophysics of visual search and identification tasks, graphical perception, and graphical language development provides scientific guidance for design and evaluation of graphical encodings which might otherwise be reduced to opinion and personal taste. However, literature offers inconclusive and often conflicting viewpoints, suggesting a need for further research. The goal of this research was to determine empirically the effectiveness of graphical devices for encoding nominal and quantitative information in complex visualization displays. Using the Envision Graphic View, we conducted a within-subjects empirical investigation of the effectiveness of three graphical devices N icon color, icon shape, and icon size N in communicating nominal (document type) and quantitative (document relevance) data. Our study provides empirical evidence regarding the relative effectiveness of icon color, shape, and size for conveying both nominal and quantitative data. While our studies consistently rank color as most effective, the rankings differ for shape and size. For nominal data, icon shape ranks ahead of icon size by all measures except time for task completion, which places shape behind size. For quantitative data, we found, by all measures, that encodings with icon shape are more effective than with icon size. We conclude that the nature of tasks performed and the relative importance of measures of effectiveness are more significant than the type of data represented for designers choosing among rankings.