Miracle-dz/UniFAR-FTUs huggingface dataset:
Verifi-Chain: A Credentials Verifier using Blockchain and IPFS

Submitting fake certificates is a common problem in Southeast Asia, which prevents qualified candidates from getting the jobs they deserve. When applying for a job, students must provide academic credentials as proof of their qualifications, acquired both inside and outside the classroom. Verifying academic documents before hiring is crucial to prevent fraud. Employing blockchain technology has the potential to address this issue. Blockchain provides an electronic certificate that is tamper-proof and non-repudiable, making it difficult for students to manipulate their academic credentials. This paper presents a prototype for an academic credential verification model that leverages the security features of blockchain and IPFS (Interplanetary File System). Certificates are temporarily stored in a database before being transferred to IPFS, where a unique hash code is generated using a hashing algorithm. This hash code serves as the certificate's unique identity and is stored in the blockchain nodes. Companies can verify an applicant's credentials by searching for the applicant and accessing their already verified certificates. Utilizing IPFS as a middleman storage platform lowers the expenses of directly storing massive data on the blockchain. To sum it up, the proposed solution would make the process of certificate verification more efficient, secure, and cost-effective. It would save time and resources that would otherwise be used to manually verify certificates.
Zeroth-order Optimization with Weak Dimension Dependency

Zeroth-order optimization is a fundamental research topic that has been a focus of various learning tasks, such as black-box adversarial attacks, bandits, and reinforcement learning. However, in theory, most complexity results assert a linear dependency on the dimension of optimization variable, which implies paralyzations of zeroth-order algorithms for high-dimensional problems and cannot explain their effectiveness in practice. In this paper, we present a novel zeroth-order optimization theory characterized by complexities that exhibit weak dependencies on dimensionality. The key contribution lies in the introduction of a new factor, denoted as $\mathrm{ED}_{\alpha}=\sup_{x\in \mathbb{R}^d}\sum_{i=1}^d\sigma_i^\alpha(\nabla^2 f(x))$ ($\alpha>0$, $\sigma_i(\cdot)$ is the $i$-th singular value in non-increasing order), which effectively functions as a measure of dimensionality. The algorithms we propose demonstrate significantly reduced complexities when measured in terms of the factor $\mathrm{ED}_{\alpha}$. Specifically, we first study a well-known zeroth-order algorithm from Nesterov and Spokoiny (2017) on quadratic objectives and show a complexity of $\mathcal{O}\left(\frac{\mathrm{ED}_1}{\sigma_d}\log(1/\epsilon)\right)$ for the strongly convex setting. Furthermore, we introduce novel algorithms that leverages the Heavy-ball mechanism. Our proposed algorithm exhibits a complexity of $\mathcal{O}\left(\frac{\mathrm{ED}_{1/2}}{\sqrt{\sigma_d}}\cdot\log{\frac{L}{\mu}}\cdot\log(1/\epsilon)\right)$. We further expand the scope of the method to encompass generic smooth optimization problems under an additional Hessian-smooth condition. The resultant algorithms demonstrate remarkable complexities which improve by an order in $d$ under appropriate conditions. Our analysis lays the foundation for zeroth-order optimization methods for smooth functions within high-dimensional settings.
Exploring the Effectiveness of LLMs in Automated Logging Generation: An Empirical Study

Automated logging statement generation techniques facilitate developers in writing appropriate logging statements that document software behaviors. Current retrieval-based and learning-based logging methods fail to provide accurate logging statements in complex software. Although existing large language models (LLMs) might be a good fit for the task due to their great success in natural language generation and programming language comprehension, their effectiveness and generalization capabilities have not been explored. To this end, this paper performs the first extensive study on applying LLMs for logging statement generation. We build LogBench, the first logging statement generation dataset. On LogBench, we evaluate the effectiveness and generalization capabilities of eight state-of-the-art LLMs, which include general-purpose and code-specific models ranging from 60M to 175B in size. Specifically, we evaluate LLM's logging effectiveness by studying 1) their ability to decide logging ingredients, 2) the impact of the internal characteristics of LLMs, and 3) the influence of external factors. We further evaluate LLM's logging generalization capabilities using unseen data derived from code transformation techniques. Our study demonstrates that existing LLMs fall short of practical requirements for generating proper logging statement texts. We also disclose the impact of internal characteristics and external factors for LLMs in automated logging. In addition, we observe that existing LLMs cannot generalize to logging unseen code, revealing their unsatisfactory generalization capabilities. Based on our findings, we further discuss three implications that can enhance logging statement generation in the future, such as developing a unified metric for logging quality, incorporating shareable code knowledge into LLMs, and devising suitable prompts.
Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.
Weighted multiscale region-level sparse representation for classification of high-spatial resolution remote sensing images

Abstract. Region segmentation is a basic and important procedure in region-based classification of remote sensing images. Compared with single-scale segmentation, multiscale segmentation can obtain different structure information and shows good performance in classification. Nonetheless, in the previous multiscale algorithms, different scales are regarded as having equal contributions to classification, thus, multiscale segmentation can contain some unsuitable scales, which affects the classification accuracy. To overcome this drawback and sufficiently utilize structural information, a weighted multiscale region-level sparse representation classification (WMRSRC) algorithm is proposed. In the WMRSRC algorithm, different weights are utilized for the region-level features of different scales. The weights are determined by the segmentation quality, which is evaluated by interregion and intraregion heterogeneity measures (Local Moran’s I and variance, respectively). Once the region weights on different scales are determined, the weighted joint sparse representation model is used to classify the multiscale regions. Through three classification experiments based on high-spatial resolution remote sensing images, we find that the proposed WMRSRC algorithm gives better results than other state-of-the-art algorithms.
Reducing cartel recruitment is the only way to lower violence in Mexico

Mexican cartels lose many members as a result of conflict with other cartels and incarcerations. Yet, despite their losses, cartels manage to increase violence for years. We address this puzzle by leveraging data on homicides, missing persons, and incarcerations in Mexico for the past decade along with information on cartel interactions. We model recruitment, state incapacitation, conflict, and saturation as sources of cartel size variation. Results show that by 2022, cartels counted 160,000 to 185,000 units, becoming one of the country’s top employers. Recruiting between 350 and 370 people per week is essential to avoid their collapse because of aggregate losses. Furthermore, we show that increasing incapacitation would increase both homicides and cartel members. Conversely, reducing recruitment could substantially curtail violence and lower cartel size. Description Editor’s summary Homicides in Latin America are driven by violent cartels. The impact of Mexican cartels is especially far reaching because they prey upon undocumented migrants along the US-Mexico border, violate human rights, and weaken political and economic institutions. However, cartels remain mysterious despite being a major employer. Because understanding how Mexican cartels function is essential to attenuating their power, Prieto-Curiel et al. conducted a sophisticated analysis that estimated their population size and examined factors driving cartel growth and shrinkage (see the Policy Forum by Caulkins et al.). Factors included “recruitment” (new cartel members join), “incapacitation” (police incarcerate or arrest members), “conflict” (cartels fight other cartels), and “saturation” (members leave). Findings suggest that reducing “recruitment” instead of increasing “incapacitation” is a much more effective policy to decrease violence. —Ekeoma Uzogara Reducing recruitment of new members is key to weakening Mexico’s cartels.
Safety and Liveness of Quantitative Properties and Automata

Safety and liveness stand as fundamental concepts in formal languages, playing a key role in verification. The safety-liveness classification of boolean properties characterizes whether a given property can be falsified by observing a finite prefix of an infinite computation trace (always for safety, never for liveness). In the quantitative setting, properties are arbitrary functions from infinite words to partially-ordered domains. Extending this paradigm to the quantitative domain, where properties are arbitrary functions mapping infinite words to partially-ordered domains, we introduce and study the notions of quantitative safety and liveness. First, we formally define quantitative safety and liveness, and prove that our definitions induce conservative quantitative generalizations of both the safety-progress hierarchy and the safety-liveness decomposition of boolean properties. Consequently, like their boolean counterparts, quantitative properties can be min-decomposed into safety and liveness parts, or alternatively, max-decomposed into co-safety and co-liveness parts. Second, we instantiate our framework with the specific classes of quantitative properties expressed by automata. These quantitative automata contain finitely many states and rational-valued transition weights, and their common value functions Inf, Sup, LimInf, LimSup, LimInfAvg, LimSupAvg, and DSum map infinite words into the totally-ordered domain of real numbers. In this automata-theoretic setting, we establish a connection between quantitative safety and topological continuity and provide alternative characterizations of quantitative safety and liveness in terms of their boolean analogs. For all common value functions, we provide a procedure for deciding whether a given automaton is safe or live, we show how to construct its safety closure, and we present a min-decomposition into safe and live automata.
Making the Nyström method highly accurate for low-rank approximations

The Nystr\"om method is a convenient heuristic method to obtain low-rank approximations to kernel matrices in nearly linear complexity. Existing studies typically use the method to approximate positive semidefinite matrices with low or modest accuracies. In this work, we propose a series of heuristic strategies to make the Nystr\"om method reach high accuracies for nonsymmetric and/or rectangular matrices. The resulting methods (called high-accuracy Nystr\"om methods) treat the Nystr\"om method and a skinny rank-revealing factorization as a fast pivoting strategy in a progressive alternating direction refinement process. Two refinement mechanisms are used: alternating the row and column pivoting starting from a small set of randomly chosen columns, and adaptively increasing the number of samples until a desired rank or accuracy is reached. A fast subset update strategy based on the progressive sampling of Schur complements is further proposed to accelerate the refinement process. Efficient randomized accuracy control is also provided. Relevant accuracy and singular value analysis is given to support some of the heuristics. Extensive tests with various kernel functions and data sets show how the methods can quickly reach prespecified high accuracies in practice, sometimes with quality close to SVDs, using only small numbers of progressive sampling steps.
Extending relax-and-round combinatorial optimization solvers with quantum correlations

We introduce a relax-and-round approach embedding the quantum approximate optimization algorithm (QAOA) with $p\geq 1$ layers. We show for many problems, including Sherrington-Kirkpatrick spin glasses, that at $p=1$, it is as accurate as its classical counterpart, and maintains the infinite-depth optimal performance guarantee of the QAOA. Employing a different rounding scheme, we prove the method shares the performance of the Goemans-Williamson algorithm for the maximum cut problem on certain graphs. We pave the way for an overarching quantum relax-and-round framework with performance on par with some of the best classical algorithms.
Progress in Particle and Nuclear Physics Vol 1 : The Mesonic Interface between Nuclear Structure and Particle Physics – Proceedings of the International School of Nuclear Physics, Erice 2–14 September 1976

D Wilkinson (ed) 1978 Oxford: Pergamon vi + 284 pp price £25 This volume consists of ten lectures arranged by Sir Denys Wilkinson, whose interest in the interrelation of nuclear physics and elementary particle physics is of long standing. The lectures are concerned primarily with those effects involving nuclei which are due, at least in some sense, to internal or external pion fields.
EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy

In the post-Moore's Law era, relying solely on hardware advancements for automatic performance gains is no longer feasible without increased energy consumption, due to the end of Dennard scaling. Consequently, computing accounts for an increasing amount of global energy usage, contradicting the objective of sustainable computing. The lack of hardware support and the absence of a standardized, software-centric method for the precise tracing of energy provenance exacerbates the issue. Aiming to overcome this challenge, we argue that fine-grained software energy attribution is attainable, even with limited hardware support. To support our position, we present a thread-level, NUMA-aware energy attribution method for CPU and DRAM in multi-tenant environments. The evaluation of our prototype implementation, EnergAt, demonstrates the validity, effectiveness, and robustness of our theoretical model, even in the presence of the noisy-neighbor effect. We envisage a sustainable cloud environment and emphasize the importance of collective efforts to improve software energy efficiency.
Dispersion of traffic exhausts emitted from a stationary line source versus individual moving cars – a numerical comparison

A three-dimensional microscale model was used to study the effects of moving vehicles on air pollution in the close vicinity of a road. The numerical results are compared to general findings from wind tunnel experiments and field observations. It was found that the model is suitable to capture the main flow characteristics within an urban street canyon, in particular the modifications relating to running traffic. A comparison of the results for a stationary line source approach and for multiple single moving sources demonstrates significant differences. For a street in a flat terrain, the near-road concentrations are underestimated by up to a factor of two if the emissions are approximated by a stationary line source. This underestimation decreases with increasing distance, and becomes negligible 30–50 m away from the road. For an urban canyon situation, the line source assumption is a conservative approximation for the concentrations at the leeside of the street, while on the opposite pavement and wall, a systematic underestimation was found. Also, the effects of different traffic situations have been studied and discussed.
A DeepLearning Framework for Dynamic Estimation of Origin-Destination Sequence

OD matrix estimation is a critical problem in the transportation domain. The principle method uses the traffic sensor measured information such as traffic counts to estimate the traffic demand represented by the OD matrix. The problem is divided into two categories: static OD matrix estimation and dynamic OD matrices sequence(OD sequence for short) estimation. The above two face the underdetermination problem caused by abundant estimated parameters and insufficient constraint information. In addition, OD sequence estimation also faces the lag challenge: due to different traffic conditions such as congestion, identical vehicle will appear on different road sections during the same observation period, resulting in identical OD demands correspond to different trips. To this end, this paper proposes an integrated method, which uses deep learning methods to infer the structure of OD sequence and uses structural constraints to guide traditional numerical optimization. Our experiments show that the neural network(NN) can effectively infer the structure of the OD sequence and provide practical constraints for numerical optimization to obtain better results. Moreover, the experiments show that provided structural information contains not only constraints on the spatial structure of OD matrices but also provides constraints on the temporal structure of OD sequence, which solve the effect of the lagging problem well.
Contrastive Learning for Conversion Rate Prediction

Conversion rate (CVR) prediction plays an important role in advertising systems. Recently, supervised deep neural network-based models have shown promising performance in CVR prediction. However, they are data hungry and require an enormous amount of training data. In online advertising systems, although there are millions to billions of ads, users tend to click only a small set of them and to convert on an even smaller set. This data sparsity issue restricts the power of these deep models. In this paper, we propose the Contrastive Learning for CVR prediction (CL4CVR) framework. It associates the supervised CVR prediction task with a contrastive learning task, which can learn better data representations exploiting abundant unlabeled data and improve the CVR prediction performance. To tailor the contrastive learning task to the CVR prediction problem, we propose embedding masking (EM), rather than feature masking, to create two views of augmented samples. We also propose a false negative elimination (FNE) component to eliminate samples with the same feature as the anchor sample, to account for the natural property in user behavior data. We further propose a supervised positive inclusion (SPI) component to include additional positive samples for each anchor sample, in order to make full use of sparse but precious user conversion events. Experimental results on two real-world conversion datasets demonstrate the superior performance of CL4CVR. The source code is available at https://github.com/DongRuiHust/CL4CVR.
E-optimal and highly E-efficient designs with (mod 4) correlated observations

The present paper analyses the linear model with design matrices with elements in or in assuming the existence of correlated errors with a known correlation structure. This model corresponds to the model of three- or two-level factorial design or to the model of chemical balance weighing design. The most difficult case when the number of observations is considered. The E-optimal and the highly E-efficient designs and their constructions are presented separately for 1, 2, 3 and more than three parameters. It is also shown that these designs are not A- and D-optimal for many values of the number of observations and the number of parameters.
Subtraction of the confusion foreground and parameter uncertainty of resolvable galactic binaries on the networks of space-based gravitational-wave detectors

There are tens of millions of compact binary systems in the Milky Way, called galactic binaries (GBs), most of which are unresolved, and the Gravitational waves (GWs) emitted overlap to form foreground confusion. By simulating such foreground confusion, we have studied how LISA, Taiji and TianQin, including their alternative orbital configurations, subtract resolvable GBs when they combine as some networks. The results of our research indicate that the order of detected number for a single detector from high to low is: Taiji-m, Taiji-p (c), LISA, TianQin I, TianQin II. For detector combinations on the network, the foreground confusion is effectively reduced as the number of detectors grows, and the optimal combinations with different numbers are: Taiji-m, LISA+Taiji-m, LISA+Taiji-m+TianQin I, and LISA+Taiji-m+TianQin I+II. The sensitivity curve is optimized as the number of detectors increases, which renders it possible to detect other gravitational wave sources more precisely and decrease the resolvable GBs parameter uncertainty. Based on this, we discuss the parameter uncertainty of resolvable GBs detected by the combinations above and find that GW detection can promote electromagnetic (EM) detection. On the contrary, we discovered that by utilizing EM detection, determining the inclination angle can reduce the uncertainty of GW strain amplitude by $\sim$93%, and determining the sky position can reduce the uncertainty of the phase by $\sim$30%, further strengthening the connection between GW detection and EM detection, and contributing to the research of Multi-messenger astronomy.
SepVAE: a contrastive VAE to separate pathological patterns from healthy ones

Contrastive Analysis VAE (CA-VAEs) is a family of Variational auto-encoders (VAEs) that aims at separating the common factors of variation between a background dataset (BG) (i.e., healthy subjects) and a target dataset (TG) (i.e., patients) from the ones that only exist in the target dataset. To do so, these methods separate the latent space into a set of salient features (i.e., proper to the target dataset) and a set of common features (i.e., exist in both datasets). Currently, all models fail to prevent the sharing of information between latent spaces effectively and to capture all salient factors of variation. To this end, we introduce two crucial regularization losses: a disentangling term between common and salient representations and a classification term between background and target samples in the salient space. We show a better performance than previous CA-VAEs methods on three medical applications and a natural images dataset (CelebA). Code and datasets are available on GitHub https://github.com/neurospin-projects/2023_rlouiset_sepvae.