Miracle-dz/UniFAR-FTUs huggingface dataset:
Rank distribution in cubic twist families of elliptic curves

Let $a$ be an integer which is not of the form $n^2$ or $-3 n^2$ for $n\in \mathbb{Z}$. Let $E_a$ be the elliptic curve with rational $3$-isogeny defined by $E_a:y^2=x^3+a$, and $K:=\mathbb{Q}(\mu_3)$. Assume that the $3$-Selmer group of $E_a$ over $K$ vanishes. It is shown that there is an explicit infinite set of cubefree integers $m$ such that the $3$-Selmer groups over $K$ of $E_{m^2 a}$ and $E_{m^4 a}$ both vanish. In particular, the ranks of these cubic twists are seen to be $0$ over $K$. Our results are proven by studying stability properties of $3$-Selmer groups in cyclic cubic extensions of $K$, via local and global Galois cohomological techniques.
OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning

Few-Shot Class-Incremental Learning (FSCIL) introduces a paradigm in which the problem space expands with limited data. FSCIL methods inherently face the challenge of catastrophic forgetting as data arrives incrementally, making models susceptible to overwriting previously acquired knowledge. Moreover, given the scarcity of labeled samples available at any given time, models may be prone to overfitting and find it challenging to strike a balance between extensive pretraining and the limited incremental data. To address these challenges, we propose the OrCo framework built on two core principles: features' orthogonality in the representation space, and contrastive learning. In particular, we improve the generalization of the embedding space by employing a combination of supervised and self-supervised contrastive losses during the pretraining phase. Additionally, we introduce OrCo loss to address challenges arising from data limitations during incremental sessions. Through feature space perturbations and orthogonality between classes, the OrCo loss maximizes margins and reserves space for the following incremental data. This, in turn, ensures the accommodation of incoming classes in the feature space without compromising previously acquired knowledge. Our experimental results showcase state-of-the-art performance across three benchmark datasets, including mini-ImageNet, CIFAR100, and CUB datasets. Code is available at https://github.com/noorahmedds/OrCo
BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models

Large Language Models (LLMs) like ChatGPT and GPT-4 are versatile and capable of addressing a diverse range of tasks. However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge essential for tasks in vertical domains, such as legal, medical, etc. To address this issue, previous approaches either conduct continuous pre-training with domain-specific data or employ retrieval augmentation to support general LLMs. Unfortunately, these strategies are either cost-intensive or unreliable in practical applications. To this end, we present a novel framework named BLADE, which enhances Black-box LArge language models with small Domain-spEcific models. BLADE consists of a black-box LLM and a small domain-specific LM. The small LM preserves domain-specific knowledge and offers specialized insights, while the general LLM contributes robust language comprehension and reasoning capabilities. Specifically, our method involves three steps: 1) pre-training the small LM with domain-specific data, 2) fine-tuning this model using knowledge instruction data, and 3) joint Bayesian optimization of the general LLM and the small LM. Extensive experiments conducted on public legal and medical benchmarks reveal that BLADE significantly outperforms existing approaches. This shows the potential of BLADE as an effective and cost-efficient solution in adapting general LLMs for vertical domains.
An Experimentally Validated Feasible Quantum Protocol for Identity-Based Signature with Application to Secure Email Communication

Digital signatures are one of the simplest cryptographic building blocks that provide appealing security characteristics such as authenticity, unforgeability, and undeniability. In 1984, Shamir developed the first Identity-based signature (IBS) to simplify public key infrastructure and circumvent the need for certificates. It makes the process uncomplicated by enabling users to verify digital signatures using only the identifiers of signers, such as email, phone number, etc. Nearly all existing IBS protocols rely on several theoretical assumption-based hard problems. Unfortunately, these hard problems are unsafe and pose a hazard in the quantum realm. Thus, designing IBS algorithms that can withstand quantum attacks and ensure long-term security is an important direction for future research. Quantum cryptography (QC) is one such approach. In this paper, we propose an IBS based on QC. Our scheme's security is based on the laws of quantum mechanics. It thereby achieves long-term security and provides resistance against quantum attacks. We verify the proposed design's correctness and feasibility by simulating it in a prototype quantum device and the IBM Qiskit quantum simulator. The implementation code in qiskit with Jupyternotebook is provided in the Annexure. Moreover, we discuss the application of our design in secure email communication.
Performance Monitoring and Visualization of Large-Sized and Multi-Threaded Applications with the Paje Framework

Performance is a critical issue in the context of massively parallel programs. In practice, it is almost impossible to observe and understand the behavior of such programs without the assistance of automated tools which offer the program developer insights into the execution behavior of complex applications. Most of these tools are based on event-based and tracing techniques. As the size of the parallel system grows, it generates a huge size of events. Visualizing and animating gathered traces often produce a complex and non understandable diagrams and displays. Recently, the Paje visualization frameworks have been developed in our laboratory ID (Informatique et Distribution). This framework provides interactive and scalable behavioral visualizations of parallel and distributed applications. This papers presents an empirical performance monitoring and visualization study with two large scale applications (JonAS (200.000 LOCI) and Jboss (400.000 LOC)). We have developed an event-based tracing framework for large scale applications monitoring. We use Paje framework to observe and visualize a large amount of harvested events.
How is Testing Related to Single Statement Bugs?

In this study, we analyzed the correlation between unit test coverage and the occurrence of Single Statement Bugs (SSBs) in open-source Java projects. We analyzed data from the top 100 Maven-based projects on GitHub, which includes 7824 SSBs. Our preliminary findings suggest a weak to moderate correlation, indicating that increased test coverage is somewhat reduce the occurrence of SSBs. However, this relationship is not very strong, emphasizing the need for better tests. Our study contributes to the ongoing discussion on enhancing software quality and provides a basis for future research into effective testing practices aimed at mitigating SSBs.
Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework

genome are dramatically reshaping the research and development pathways for drugs, vaccines, and diagnostics. The growth in the number of molecular entities entering the drug development pipeline has accelerated as a consequence of powerful discovery and screening technologies such as combinatorial chemistry, mass spectrometry, high throughput screening, celland tissue-based DNA microarrays, and proteomic approaches.1 As a consequence, there is an escalating number of therapeutic candidates, which has caused the need for new technologies and strategies to streamline the process to make safe and effective therapies available to patients. One approach to the achievement of more expeditious and informative therapeutic research is the use of precise clinical measurement tools to determine disease progression and the effects of interventions (drugs, surgery, and vaccines). For example, gene-based approaches such as single nucleotide polymorphism maps are now being developed to distinguish the molecular and cellular basis for variations in clinical response to therapy.2 Another approach is the use of a wide array of analytical tools to assess biological parameters, which are referred to as biomarkers. Biomarker measurements can help explain empirical results of clinical trials by relating the effects of interventions on molecular and cellular pathways to clinical responses. In doing so, biomarkers provide an avenue for researchers to gain a mechanistic understanding of the differences in clinical response that may be influenced by uncontrolled variables (for example, drug metabolism). There are a variety of ways that biomarker measurements can aid in the development and evaluation of COMMENTARY
Statistical Inference of Optimal Allocations I: Regularities and their Implications

In this paper, we develp a functional differentiability approach for solving statistical optimal allocation problems. We first derive Hadamard differentiability of the value function through a detailed analysis of the general properties of the sorting operator. Central to our framework are the concept of Hausdorff measure and the area and coarea integration formulas from geometric measure theory. Building on our Hadamard differentiability results, we demonstrate how the functional delta method can be used to directly derive the asymptotic properties of the value function process for binary constrained optimal allocation problems, as well as the two-step ROC curve estimator. Moreover, leveraging profound insights from geometric functional analysis on convex and local Lipschitz functionals, we obtain additional generic Fr\'echet differentiability results for the value functions of optimal allocation problems. These compelling findings motivate us to study carefully the first order approximation of the optimal social welfare. In this paper, we then present a double / debiased estimator for the value functions. Importantly, the conditions outlined in the Hadamard differentiability section validate the margin assumption from the statistical classification literature employing plug-in methods that justifies a faster convergence rate.
High-Speed General Purpose Genetic Algorithm Processor

In this paper, an ultrafast steady-state genetic algorithm processor (GAP) is presented. Due to the heavy computational load of genetic algorithms (GAs), they usually take a long time to find optimum solutions. Hardware implementation is a significant approach to overcome the problem by speeding up the GAs procedure. Hence, we designed a digital CMOS implementation of GA in 0.18 μm process. The proposed processor is not bounded to a specific application. Indeed, it is a general-purpose processor, which is capable of performing optimization in any possible application. Utilizing speed-boosting techniques, such as pipeline scheme, parallel coarse-grained processing, parallel fitness computation, parallel selection of parents, dual-population scheme, and support for pipelined fitness computation, the proposed processor significantly reduces the processing time. Furthermore, by relying on a built-in discard operator the proposed hardware may be used in constrained problems that are very common in control applications. In the proposed design, a large search space is achievable through the bit string length extension of individuals in the genetic population by connecting the 32-bit GAPs. In addition, the proposed processor supports parallel processing, in which the GAs procedure can be run on several connected processors simultaneously.
Full counting statistics of 1d short-range Riesz gases in confinement

We investigate the full counting statistics (FCS) of a harmonically confined 1d short-range Riesz gas consisting of $N$ particles in equilibrium at finite temperature. The particles interact with each other through a repulsive power-law interaction with an exponent $k>1$ which includes the Calogero-Moser model for $k=2$. We examine the probability distribution of the number of particles in a finite domain $[-W, W]$ called number distribution, denoted by $\mathcal{N}(W, N)$. We analyze the probability distribution of $\mathcal{N}(W, N)$ and show that it exhibits a large deviation form for large $N$ characterised by a speed $N^{\frac{3k+2}{k+2}}$ and by a large deviation function of the fraction $c = \mathcal{N}(W, N)/N$ of the particles inside the domain and $W$. We show that the density profiles that create the large deviations display interesting shape transitions as one varies $c$ and $W$. This is manifested by a third-order phase transition exhibited by the large deviation function that has discontinuous third derivatives. Monte-Carlo (MC) simulations show good agreement with our analytical expressions for the corresponding density profiles. We find that the typical fluctuations of $\mathcal{N}(W, N)$, obtained from our field theoretic calculations are Gaussian distributed with a variance that scales as $N^{\nu_k}$, with $\nu_k = (2-k)/(2+k)$. We also present some numerical findings on the mean and the variance. Furthermore, we adapt our formalism to study the index distribution (where the domain is semi-infinite $(-\infty, W])$, linear statistics (the variance), thermodynamic pressure and bulk modulus.
Familial Lipoprotein Disorders in Patients With Premature Coronary Artery Disease

BackgroundGenetic lipoprotein disorders have been associated with premature coronary artery disease (CAD). Methods and ResultsThe prevalence of such disorders was determined in 102 kindreds (n=603 subjects) in whom the proband had significant CAD documented by angiography before the age of 60 years. Fasting plasma cholesterol, triglyceride, low density lipoprotein (LDL) cholesterol, apolipoprotein (apo) B, and lipoprotein (a) [Lp(a)] values above the 90th percentile and high density lipoprotein (HDL) cholesterol and apo A-I below the 10th percentile of age- and sex-specific norms were defined as abnormal. An abnormality was noted in 73.5% of probands compared with 38.2% in age-matched controls (p<0.001), with a low HDL cholesterol level (hypoalphalipoproteinemia) being the most common abnormality (39.2% of cases). In these kindreds, 54% had a defined phenotypic familial lipoprotein or apolipoprotein disorder. The following frequencies were observed: Lp(a) excess, 18.6% (includes 12.7% with no other dyslipidemias); hypertriglyceridemia with hypoalphalipoproteinemia, 14.7%; combined hyperlipidemia, 13.7% (11.7% with and 2.0% without hypoalphalipoproteinemia); hyperapobetalipoproteinemia (elevated apo B only), 5%; hypoalphalipoproteinemia, 4%; hypercholesterolemia (elevated LDL only), 3%; hypertriglyceridemia, 1%; decreased apo A-I only, 1%. Overall, 54% of the probands had a familial dyslipidemia; unclassifiable lipid disorders (spouse also affected) were found in 3%. No identifiable familial dyslipidemia was noted in 43% of kindreds of those; nearly half (45%) had a sporadic lipid disorder. Parent-offspring and proband-spouse correlations for these biochemical variables revealed that lipoprotein and apolipoprotein levels are in part genetically determined, with Lp(a) showing the highest degree of parent-offspring correlation. ConclusionsOur data indicate that more than half of patients with premature CAD have a familial lipoprotein disorder, with Lp(a) excess, hypertriglyceridemia with hypoalphalipoproteinemia, and combined hyperlipidemia with hypoalphalipoproteinemia being the most common abnormalities.
Inactivation Kinetics of a New Target of β-Lactam Antibiotics*

Peptidoglycan is predominantly cross-linked by serine dd-transpeptidases in most bacterial species. The enzymes are the essential targets of β-lactam antibiotics. However, unrelated cysteine ld-transpeptidases have been recently recognized as a predominant mode of peptidoglycan cross-linking in Mycobacterium tuberculosis and as a bypass mechanism conferring resistance to all β-lactams, except carbapenems such as imipenem, in Enterococcus faecium. Investigation of the mechanism of inhibition of this new β-lactam target showed that acylation of the E. faecium enzyme (Ldtfm) by imipenem is irreversible. Using fluorescence kinetics, an original approach was developed to independently determine the catalytic constants for imipenem binding (k1 = 0.061 μm−1 min−1) and acylation (kinact = 4.5 min−1). The binding step was limiting at the minimal drug concentration required for bacterial growth inhibition. The Michaelis complex was committed to acylation because its dissociation was negligible. The emergence of imipenem resistance involved substitutions in Ldtfm that reduced the rate of formation of the non-covalent complex but only marginally affected the efficiency of the acylation step. The methods described in this study will facilitate development of new carbapenems active on extensively resistant M. tuberculosis.
Generalized convergence of the deep BSDE method: a step towards fully-coupled FBSDEs and applications in stochastic control

We are concerned with high-dimensional coupled FBSDE systems approximated by the deep BSDE method of Han et al. (2018). It was shown by Han and Long (2020) that the errors induced by the deep BSDE method admit a posteriori estimate depending on the loss function, whenever the backward equation only couples into the forward diffusion through the Y process. We generalize this result to fully-coupled drift coefficients, and give sufficient conditions for convergence under standard assumptions. The resulting conditions are directly verifiable for any equation. Consequently, unlike in earlier theory, our convergence analysis enables the treatment of FBSDEs stemming from stochastic optimal control problems. In particular, we provide a theoretical justification for the non-convergence of the deep BSDE method observed in recent literature, and present direct guidelines for when convergence can be guaranteed in practice. Our theoretical findings are supported by several numerical experiments in high-dimensional settings.
SplatFace: Gaussian Splat Face Reconstruction Leveraging an Optimizable Surface

We present SplatFace, a novel Gaussian splatting framework designed for 3D human face reconstruction without reliance on accurate pre-determined geometry. Our method is designed to simultaneously deliver both high-quality novel view rendering and accurate 3D mesh reconstructions. We incorporate a generic 3D Morphable Model (3DMM) to provide a surface geometric structure, making it possible to reconstruct faces with a limited set of input images. We introduce a joint optimization strategy that refines both the Gaussians and the morphable surface through a synergistic non-rigid alignment process. A novel distance metric, splat-to-surface, is proposed to improve alignment by considering both the Gaussian position and covariance. The surface information is also utilized to incorporate a world-space densification process, resulting in superior reconstruction quality. Our experimental analysis demonstrates that the proposed method is competitive with both other Gaussian splatting techniques in novel view synthesis and other 3D reconstruction methods in producing 3D face meshes with high geometric precision.
An Efficient Hybrid Rule Based Inference Engine with Explanation Capability

An inference engine for a hybrid representation scheme based on neurules is presented. Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist representation (adaline unit). The inference engine uses a connectionist technique, which is based on the 'firing potential', a measurement of the firing tendency of a neurule, and symbolic pattern matching. It is proved to be more efficient and natural than pure connectionist inference engines. Explanation of 'how' type can be provided in the form of if-then symbolic rules.
Generating Diverse Agricultural Data for Vision-Based Farming Applications

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. This model is capable of simulating distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture, such as semantic segmentation for autonomous weed control. We validate our model's effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.
Reduced cortical folding of the anterior cingulate cortex in obsessive-compulsive disorder.

BACKGROUND Anterior cingulate cortex (ACC) abnormalities have been implicated consistently in the pathophysiology of obsessive-compulsive disorder (OCD), yet it remains unclear whether these abnormalities originated during early neurodevelopment. In this study, we examined the ACC sulcal/gyral patterns to investigate whether neurodevelopmental anomalies of the ACC were present in patients with OCD. We hypothesized that patients with OCD would show reduced cortical folding of the ACC compared with controls. METHODS We used magnetic resonance imaging (MRI) of 169 healthy volunteers and 110 patients with OCD to examine the paracingulate sulcus and cingulate sulcus. We assessed cortical folding patterns according to established classification criteria and constructed 3 categories of paracingulate sulcus morphology according to its presence and anteroposterior extent: "prominent," "present" and "absent. " We classified the cingulate sulcus as "interrupted" or "continuous" according to the interruptions in its course. In addition, we evaluated ACC sulcal asymmetry based on interhemispheric comparisons of paracingulate sulcus morphology. RESULTS Analyses revealed that patients with OCD were significantly less likely than controls to show a well-developed left paracingulate sulcus: 50.0% of patients and 65.1% of controls showed a "prominent" or "present" paracingulate sulcus in the left hemisphere. However, there were no differences in regard to cingulate sulcus continuity, and patients also showed the same leftward ACC sulcal asymmetry as controls. LIMITATIONS Our study was limited by the fact that we obtained the MRI scans from 2 different scanners, and we did not calculate cerebral fissurization as our study was restricted to 1 specific brain region. Moreover, patients and controls differed significantly in terms of sex ratio and IQ, although we controlled these variables as covariates. CONCLUSION Our findings imply a subtle deviation in the early neurodevelopment of the ACC in patients with OCD, but the extent to which these anomalies contributed to the pathogenesis of OCD remains unclear. Further studies that link the ACC morphologic anomalies to the pathophysiology of OCD are recommended.
Absolute Conservation Law for Black Holes

The spherically reduction of the 4d Einstein-Hilbert action reduces to a 2d dilaton theory[1], which can be shown to be equivalent to a first order action (with nonvanishing torsion)[2] being a special case of a Poisson-σ model[3].