Miracle-dz/UniFAR-FTUs huggingface dataset:
NISQ-compatible approximate quantum algorithm for unconstrained and constrained discrete optimization

Quantum algorithms are getting extremely popular due to their potential to significantly outperform classical algorithms. Yet, applying quantum algorithms to optimization problems meets challenges related to the efficiency of quantum algorithms training, the shape of their cost landscape, the accuracy of their output, and their ability to scale to large-size problems. Here, we present an approximate gradient-based quantum algorithm for hardware-efficient circuits with amplitude encoding. We show how simple linear constraints can be directly incorporated into the circuit without additional modification of the objective function with penalty terms. We employ numerical simulations to test it on MaxCut problems with complete weighted graphs with thousands of nodes and run the algorithm on a superconducting quantum processor. We find that for unconstrained MaxCut problems with more than 1000 nodes, the hybrid approach combining our algorithm with a classical solver called CPLEX can find a better solution than CPLEX alone. This demonstrates that hybrid optimization is one of the leading use cases for modern quantum devices.
Better Low-Resource Entity Recognition Through Translation and Annotation Fusion

Pre-trained multilingual language models have enabled significant advancements in cross-lingual transfer. However, these models often exhibit a performance disparity when transferring from high-resource languages to low-resource languages, especially for languages that are underrepresented or not in the pre-training data. Motivated by the superior performance of these models on high-resource languages compared to low-resource languages, we introduce a Translation-and-fusion framework, which translates low-resource language text into a high-resource language for annotation using fully supervised models before fusing the annotations back into the low-resource language. Based on this framework, we present TransFusion, a model trained to fuse predictions from a high-resource language to make robust predictions on low-resource languages. We evaluate our methods on two low-resource named entity recognition (NER) datasets, MasakhaNER2.0 and LORELEI NER, covering 25 languages, and show consistent improvement up to +16 F$_1$ over English fine-tuning systems, achieving state-of-the-art performance compared to Translate-train systems. Our analysis depicts the unique advantages of the TransFusion method which is robust to translation errors and source language prediction errors, and complimentary to adapted multilingual language models.
Sifting for better features to track: exploiting time and space

Feature detection and the matching and tracking of those features are common tasks in computer vision systems. Qualities like invariance to scale, rotation, and other transforms, robustness to varying lighting conditions etc. have all been identified as important feature properties. In this work, we define three new and important feature properties which expand upon the usual local feature information. These are temporal consistency, a distributivity quality, and a distinctiveness quality. Temporal consistency is a quality of a feature that quantifies how consistently a feature has been tracked in prior frames, and can be a simple database field that stores number of frames tracked or can include more sophisticated smoothness of motion in its consistency measure. Distributivity is a quality of a feature that quantifies physical distance (number of pixels) from other features in the same image (frame). Distinctiveness is a measure of how unique a feature is among other features in the same image (frame). In this work we compare and extend two different kinds of local feature detectors, Shi's and Tomasi's [ST94] Good Features To Track (GFTT) and Lowe's Scale Invariant Feature Transform (SIFT) [Low04], which represent two of the most widely used and well known approaches to feature detection. We use them as the local feature information, and add the new properties to improve overall performance. With this we define two new "types" of better features to track, temporally consistent features and distributive features. We compare SIFT, GFTT, combined SIFT/GFTT, temporally consistent SIFT, temporally consistent GFTT, and temporally consistent combined SIFT/GFTT for matching accuracy in several video sequences. We find that temporal consistency provides better match correctness than non-temporally consistent feature matches, regardless of the local feature detector. We utilize distributed GFTT with optical flow to help build what we call a tubular mosaic. We show that distributed features allow a more accurate mosaic to be built than non-distributed features in scenes with low contrast. We improve SIFT's match correctness ratio - where SIFT determines how alike two features are - by using different distance measures besides the standard Euclidean distance. Euclidean distance is optimal when any noise in the data is Gaussian. However, we expect that there are outliers in the data, not noise. Therefore we chose to experiment with robust distance measures. A robust simple M-estimator distance measure as well as L1-norm distance measure were implemented, tested and shown to improve the percentage of correct matches for SIFT. We began initial work exploring contrast enhancement which shows some promise of finding features in low contrast areas and matching them with reasonable accuracy. Temporal consistency and distributivity are shown to be useful qualities in this work and should enhance a large class of feature detectors which, in turn, should expand their application. As an example, Tubular Mosaics is described and shown to be improved by the results of this research.
On the Limitations of Simulating Active Learning

Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects informative unlabeled data for human annotation, aiming to improve over random sampling. However, performing AL experiments with human annotations on-the-fly is a laborious and expensive process, thus unrealistic for academic research. An easy fix to this impediment is to simulate AL, by treating an already labeled and publicly available dataset as the pool of unlabeled data. In this position paper, we first survey recent literature and highlight the challenges across all different steps within the AL loop. We further unveil neglected caveats in the experimental setup that can significantly affect the quality of AL research. We continue with an exploration of how the simulation setting can govern empirical findings, arguing that it might be one of the answers behind the ever posed question ``why do active learning algorithms sometimes fail to outperform random sampling? ''. We argue that evaluating AL algorithms on available labeled datasets might provide a lower bound as to their effectiveness in real data. We believe it is essential to collectively shape the best practices for AL research, particularly as engineering advancements in LLMs push the research focus towards data-driven approaches (e.g., data efficiency, alignment, fairness). In light of this, we have developed guidelines for future work. Our aim is to draw attention to these limitations within the community, in the hope of finding ways to address them.
Learning relevant contextual variables within Bayesian Optimization

Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions. However, the relevance of contextual variables is not necessarily known beforehand. Moreover, contextual variables can sometimes be optimized themselves at additional cost, a setting overlooked by current CBO algorithms. Cost-sensitive CBO would simply include optimizable contextual variables as part of the design variables based on their cost. Instead, we adaptively select a subset of contextual variables to include in the optimization, based on the trade-off between their \emph{relevance} and the additional cost incurred by optimizing them compared to leaving them to be determined by the environment. We learn the relevance of contextual variables by sensitivity analysis of the posterior surrogate model while minimizing the cost of optimization by leveraging recent developments on early stopping for BO. We empirically evaluate our proposed Sensitivity-Analysis-Driven Contextual BO (SADCBO) method against alternatives on both synthetic and real-world experiments, together with extensive ablation studies, and demonstrate a consistent improvement across examples.
ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns

Many approaches to draping individual garments on human body models are realistic, fast, and yield outputs that are differentiable with respect to the body shape on which they are draped. However, they are either unable to handle multi-layered clothing, which is prevalent in everyday dress, or restricted to bodies in T-pose. In this paper, we introduce a parametric garment representation model that addresses these limitations. As in models used by clothing designers, each garment consists of individual 2D panels. Their 2D shape is defined by a Signed Distance Function and 3D shape by a 2D to 3D mapping. The 2D parameterization enables easy detection of potential collisions and the 3D parameterization handles complex shapes effectively. We show that this combination is faster and yields higher quality reconstructions than purely implicit surface representations, and makes the recovery of layered garments from images possible thanks to its differentiability. Furthermore, it supports rapid editing of garment shapes and texture by modifying individual 2D panels.
On the Energy-Efficiency Trade-off Between Active and Passive Communications with RIS-based Symbiotic Radio

Symbiotic radio (SR) is a promising technology of spectrum- and energy-efficient wireless systems, for which the key idea is to use cognitive backscattering communication to achieve mutualistic spectrum and energy sharing with passive backscatter devices (BDs). In this paper, a reconfigurable intelligent surface (RIS) based SR system is considered, where the RIS is used not only to assist the primary active communication, but also for passive communication to transmit its own information. For the considered system, we investigate the EE trade-off between active and passive communications, by characterizing the EE region. To gain some insights, we first derive the maximum achievable individual EEs of the primary transmitter (PT) and RIS, respectively, and then analyze the asymptotic performance by exploiting the channel hardening effect. To characterize the non-trivial EE trade-off, we formulate an optimization problem to find the Pareto boundary of the EE region by jointly optimizing the transmit beamforming, power allocation and the passive beamforming of RIS. The formulated problem is non-convex, and an efficient algorithm is proposed by decomposing it into a series of subproblems by using alternating optimization (AO) and successive convex approximation (SCA) techniques. Finally, simulation results are presented to validate the effectiveness of the proposed algorithm.
Towards Optimal Serverless Function Scaling in Edge Computing Network

Serverless computing has emerged as a new execution model which gained a lot of attention in cloud computing thanks to the latest advances in containerization technologies. Recently, serverless has been adopted at the edge, where it can help overcome heterogeneity issues, constrained nature and dynamicity of edge devices. Due to the distributed nature of edge devices, however, the scaling of serverless functions presents a major challenge. We address this challenge by studying the optimality of serverless function scaling. To this end, we propose Semi-Markov Decision Process-based (SMDP) theoretical model, which yields optimal solutions by solving the serverless function scaling problem as a decision making problem. We compare the SMDP solution with practical, monitoring-based heuristics. We show that SMDP can be effectively used in edge computing networks, and in combination with monitoring-based approaches also in real-world implementations.
Temporally Causal Discovery Tests for Discrete Time Series and Neural Spike Trains

We consider the problem of detecting causal relationships between discrete time series, in the presence of potential confounders. A hypothesis test is introduced for identifying the temporally causal influence of <inline-formula><tex-math notation="LaTeX">$(x_{n})$</tex-math></inline-formula> on <inline-formula><tex-math notation="LaTeX">$(y_{n})$</tex-math></inline-formula>, causally conditioned on a possibly confounding third time series <inline-formula><tex-math notation="LaTeX">$(z_{n})$</tex-math></inline-formula>. Under natural Markovian modeling assumptions, it is shown that the null hypothesis, corresponding to the absence of temporally causal influence, is equivalent to the underlying ‘causal conditional directed information rate’ being equal to zero. The plug-in estimator for this functional is identified with the log-likelihood ratio test statistic for the desired test. This statistic is shown to be asymptotically normal under the alternative hypothesis and asymptotically <inline-formula><tex-math notation="LaTeX">$\chi^{2}$</tex-math></inline-formula> distributed under the null, facilitating the computation of <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-values when used on empirical data. The effectiveness of the resulting hypothesis test is illustrated on simulated data, validating the underlying theory. The test is also employed in the analysis of spike train data recorded from neurons in the V4 and FEF brain regions of behaving animals during a visual attention task. There, the test results are seen to identify interesting and biologically relevant information.
ESTIMATING THE ARCH PARAMETERS BY SOLVING LINEAR EQUATIONS

Abstract. This paper discusses the asymptotics of two‐stage least squares estimator of the parameters of ARCH models. The estimator is easy to obtain since it involves solving two sets of linear equations. At the same time, the estimator has the same asymptotic efficiency as that of the widely used quasi‐maximum likelihood estimator. Simulation results show that, even for small sample size, the performance of our estimator compared to the quasi‐maximum likelihood estimator is better.
Multi-Granularity Detector for Vulnerability Fixes

With the increasing reliance on Open Source Software, users are exposed to third-party library vulnerabilities. Software Composition Analysis (SCA) tools have been created to alert users of such vulnerabilities. SCA requires the identification of vulnerability-fixing commits. Prior works have proposed methods that can automatically identify such vulnerability-fixing commits. However, identifying such commits is highly challenging, as only a very small minority of commits are vulnerability fixing. Moreover, code changes can be noisy and difficult to analyze. We observe that noise can occur at different levels of detail, making it challenging to detect vulnerability fixes accurately. To address these challenges and boost the effectiveness of prior works, we propose MiDas (Multi-Granularity Detector for Vulnerability Fixes). Unique from prior works, MiDas constructs different neural networks for each level of code change granularity, corresponding to commit-level, file-level, hunk-level, and line-level, following their natural organization and then use an ensemble model combining all base models to output the final prediction. This design allows MiDas to better cope with the noisy and highly-imbalanced nature of vulnerability-fixing commit data. In addition, to reduce the human effort required to inspect code changes, we have designed an effort-aware adjustment for MiDas's outputs based on commit length. The evaluation result demonstrates that MiDas outperforms the current state-of-the-art baseline on both Java and Python-based datasets in terms of AUC by 4.9% and 13.7%, respectively. Furthermore, in terms of two effort-aware metrics, i.e., EffortCost@L and Popt@L, MiDas also performs better than the state-of-the-art baseline up to 28.2% and 15.9% on Java, 60% and 51.4% on Python, respectively.
Hardware Trojans in Power Conversion Circuits

This report investigates the potential impact of a Trojan attack on power conversion circuits, specifically a switching signal attack designed to trigger a locking of the pulse width modulation (PWM) signal that goes to a power field-effect transistor (FET). The first simulation shows that this type of attack can cause severe overvoltage, potentially leading to functional failure. The report proposes a solution using a large bypass capacitor to force signal parity, effectively negating the Trojan circuit. The simulation results demonstrate that the proposed solution can effectively thwart the Trojan attack. However, several caveats must be considered, such as the size of the capacitor, possible current leakage, and the possibility that the solution can be circumvented by an adversary with knowledge of the protection strategy. Overall, the findings suggest that proper protection mechanisms, such as the proposed signal-parity solution, must be considered when designing power conversion circuits to mitigate the risk of Trojan attacks.
Comments on “A New Look at the Israeli Cloud Seeding Experiments”

Rangno and Hobbs (1995, hereafter RH95) note that for years many meteorologists believed that Israel I and Israel II were the only experiments providing confirmatory evidence of the ability of cloud seeding to stimulate rainfall and that Israeli winter clouds were seedable due to some unique characteristics, namely, a continental drop size distribution, with an attendant lack of a coalescence process, and a scarcity of natural ice crystals at temperatures above 2158C or so. RH95 provide convincing evidence that Israeli winter clouds sometimes produce drizzle or rain through coalescence and that significant ice particle concentrations exist in some of them at temperatures as high as 2108C. RH95 therefore conclude that they are ‘‘not as conducive to rainfall enhancement by artificial seeding as previously believed. ’’ In doing so, RH95 mimic, albeit in a reverse sense, the scientists whose work they are criticizing. They judge the seedability of clouds in Israel with reference to a single, very simple concept of seedability and then allow that judgment to sway their interpretation of the Israeli statistical results. The early descriptions of seedable orographic clouds (e.g., Mason 1971) concerned stratified orographic clouds with low cloud water concentrations and precipitation growth solely by the deposition of water vapor on ice crystals. They are inadequate for orographic clouds, including those in Israel, that contain embedded convective cells. The presence of ice particles in a convective cloud does not guarantee absence of seedability. Substantial concentrations of supercooled cloud water can exist in the presence of ice particle concentrations of 10 or even 100 L21, provided that air is ascending to provide a fresh supply of condensate. The critical concept is one of production versus consumption of supercooled cloud liquid. Elementary calculations and many modeling results have shown that even very large amounts of graupel, snow, and smaller cloud ice particles cannot deplete
Formulation of the Generalized Backward-Projection Method for Acoustical Imaging

Absmct-Reconstruction algorithms have become a major component in acoustical imaging. Special-purpose methods have been developed and successfully applied to various systems. They are similar in terms of concepts and approaches, but they differ in terms of objectives and structures. This paper introduces the formulation of the generalized backward-projection method which is applicable to all linear imaging systems. Both the analog and discrete forms of the algorithm are pre sented for theoretical analysis and practical computation considerations. The main purpose of the paper is to provide a fundamental structure and to serve as a reference for future development of algorithms in acoustic image reconstruction.
Study on varing-mass body dynamics general theorem of human body system

Based on the study of varing-mass block mechanical model of human body system and varing-mass body dynamics, the varing-mass body dynamics general theorem of human body system is developed. The contents are momentum theorem body of human body varing-mass, momentum moment theorem body to the origin of fixed coordinate system of human body varing-mass and kinetic energy theorem of body of human body varing-mass. It provides the theoretical basis for dynamics, dynamics model of human body varing-mass and study and application of digital human body.
Use of an Automated Bilingual Digital Health Tool to Reduce Unhealthy Alcohol Use Among Latino Emergency Department Patients

Key Points Question Is a brief automated bilingual computerized alcohol screening and intervention (AB-CASI) health information technology tool superior to standard care in reducing alcohol consumption and alcohol-related adverse health behaviors and consequences among US Latino emergency department (ED) patients? Findings In this randomized clinical trial of 840 self-identified adult Latino ED patients with unhealthy drinking, the number of binge drinking episodes within the last 28 days was significantly lower in the AB-CASI group compared with the standard care group (3.2 episodes vs 4.0; 7.7 episodes at baseline in both groups) at 12 months after randomization. Meaning These findings suggest that AB-CASI is a viable approach to addressing alcohol-related health disparities in the ED, a setting that serves as a national health care front line and safety net.