Miracle-dz/UniFAR-FTUs huggingface dataset:
A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.
Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models

Large language models (LLMs) have demonstrated impressive performance on various downstream tasks without requiring fine-tuning, including ChatGPT, a chat-based model built on top of LLMs such as GPT-3.5 and GPT-4. Despite having a lower training proportion compared to English, these models also exhibit remarkable capabilities in other languages. In this study, we assess the performance of GPT-3.5 and GPT-4 models on seven distinct Arabic NLP tasks: sentiment analysis, translation, transliteration, paraphrasing, part of speech tagging, summarization, and diacritization. Our findings reveal that GPT-4 outperforms GPT-3.5 on five out of the seven tasks. Furthermore, we conduct an extensive analysis of the sentiment analysis task, providing insights into how LLMs achieve exceptional results on a challenging dialectal dataset. Additionally, we introduce a new Python interface https://github.com/ARBML/Taqyim that facilitates the evaluation of these tasks effortlessly.
Classes of Aggregation Rules for Ethical Decision Making in Automated Systems

We study a class of {\em aggregation rules} that could be applied to ethical AI decision-making. These rules yield the decisions to be made by automated systems based on the information of profiles of preferences over possible choices. We consider two different but very intuitive notions of preferences of an alternative over another one, namely {\it pairwise majority} and {\it position} dominance. Preferences are represented by permutation processes over alternatives and aggregation rules are applied to obtain results that are socially considered to be ethically correct. In this setting, we find many aggregation rules that satisfy desirable properties for an autonomous system. We also address the problem of the stability of the aggregation process, which is important when the information is variable. These results are a contribution for an AI designer that wants to justify the decisions made by an autonomous system. \\ \textit{Keywords:} Aggregation Operators; Permutation Process; Decision Analysis.
Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning

Abstract Protein–DNA interaction is critical for life activities such as replication, transcription and splicing. Identifying protein–DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Improvements in this area have the potential to drive novel applications in biotechnology and drug design. In this study, we propose a novel approach called Contrastive Learning And Pre-trained Encoder (CLAPE), which combines a pre-trained protein language model and the contrastive learning method to predict DNA binding residues. We trained the CLAPE-DB model on the protein–DNA binding sites dataset and evaluated the model performance and generalization ability through various experiments. The results showed that the area under ROC curve values of the CLAPE-DB model on the two benchmark datasets reached 0.871 and 0.881, respectively, indicating superior performance compared to other existing models. CLAPE-DB showed better generalization ability and was specific to DNA-binding sites. In addition, we trained CLAPE on different protein–ligand binding sites datasets, demonstrating that CLAPE is a general framework for binding sites prediction. To facilitate the scientific community, the benchmark datasets and codes are freely available at https://github.com/YAndrewL/clape.
2.5D magnetohydrodynamic models of circumstellar discs around FS CMa post-mergers – I. Non-stationary accretion stage

We investigate the dynamic evolution of the gaseous regions around FS CMa post-mergers. Owing to the slow rotation of the central B-type star, the dynamics is driven mainly by the magnetic field of the central star. Recent observations have allowed us to set realistic initial conditions, such as the magnetic field value ($B_\star \approx 6\times 10^{3}\, \mathrm{G}$), the mass of the central star ($M_\star =6\, \mathrm{M}_\odot$), and the initial disc density $\rho _{d0}\in [10^{-13}\, \mathrm{g\, cm^{-3}},10^{-11}\, \mathrm{g \, cm^{-3}}]$. We use the pluto code to perform 2.5D magnetohydrodynamic simulations of thin and thick disc models. Especially relevant for the interpretation of the observed properties of FS CMa post-mergers are the results for low-density discs, in which we find a jet emerging from the inner edge of the disc, as well as the formation of the so-called ‘hot plasmoid’ in the coronal region. Jets are probably detected as discrete absorption components in the resonance lines of FS CMa stars. Moreover, the magnetic field configuration in the low-density plasma region favours the appearance of magnetocentrifugal winds from the disc. The currents towards the star created by the magnetic field may explain accidentally observed material infall. The disc structure is significantly changed owing to the presence of the magnetic field. The magnetic field is also responsible for the formation of a hot corona, as observed in several FS CMa stars through the Raman lines. Our results are valid for all magnetic stars surrounded by a low-density plasma, that is, by some stars showing the B[e] phenomenon.
Emergent criticality in fully frustrated quantum magnets

Phase transitions in condensed matter are often linked to exotic emergent properties. We study the fully frustrated bilayer Heisenberg antiferromagnet to demonstrate that an applied magnetic field creates a novel emergent criticality. The quantum phase diagram contains four states, the DS (singlets on every interlayer dimer bond), DTAF (all triplets with antiferromagnetic order), TC (a singlet-triplet checkerboard) and FM (saturated ferromagnet). The thermal phase diagram is dominated by a wall of discontinuities extending from the zero-field DTAF-DS transition to a quantum critical endpoint where the field drives the DTAF and TC into the FM. This first-order wall is terminated at finite temperatures by a line of critical points, where the Berezinskii-Kosterlitz-Thouless (BKT) transition of the DTAF and the thermal Ising transition of the TC also terminate. We demonstrate by quantum Monte Carlo simulations that the BKT transition does not change the Ising nature of the DTAF-DS critical line. By contrast, the combination of symmetries merging on the multicritical DTAF-TC line leads to a 4-state Potts universality not contained in the microscopic Hamiltonian, which we associate with the Ashkin-Teller model. Our results represent a systematic step in understanding emergent phenomena in quantum magnetic materials including the ``Shastry-Sutherland compound'' SrCu$_2$(BO$_3$)$_2$.
Understanding (Ir)rational Herding Online

Investigations of social influence in collective decision-making have become possible due to recent technologies and platforms that record interactions in much larger groups than could be studied before. Herding and its impact on decision-making are critical areas of practical interest and research study. However, despite theoretical work suggesting that it matters whether individuals choose who to imitate based on cues such as experience or whether they herd at random, there is little empirical analysis of this distinction. To demonstrate the distinction between what the literature calls “rational” and “irrational” herding, we use data on tens of thousands of loans from a well-established online peer-to-peer (p2p) lending platform. First, we employ an empirical measure of memory in complex systems to quantify herding in lending. Then, we illustrate a network-based approach to visualize herding. Finally, we model the impact of herding on collective outcomes. Our study reveals that loan performance is not solely determined by whether lenders engage in herding or not. Instead, the interplay between herding and the imitated lenders’ prior success on the platform predicts loan outcomes. In short, herding around expert lenders is associated with loans that do not default. We discuss the implications of this under-explored aspect of herding for platform designers, borrowers, and lenders. Our study advances collective intelligence theories based on a case of high-stakes group decision-making online.
Homology rings of affine grassmannians and positively multiplicative graphs

Let $\mathfrak{g}$ be an untwisted affine Lie algebra with associated Weyl group $W_a$. To any level 0 weight $\gamma$ we associate a weighted graph $\Gamma_\gamma$ that encodes the orbit of $\gamma$ under the action $W_a$. We show that the graph $\Gamma_\gamma$ encodes the periodic orientation of certain subsets of alcoves in $W_a$ and therefore can be interpreted as an automaton determining the reduced expressions in these subsets. Then, by using some relevant quotients of the homology ring of affine Grassmannians, we show that $\Gamma_\gamma$ is positively multiplicative. This allows us in particular to compute the structure constants of the homology rings using elementary linear algebra on multiplicative graphs. In another direction, the positivity of $\Gamma_\gamma$ yields the key ingredients to study a large class of central random walks on alcoves.
Recent Advances in Optimal Transport for Machine Learning

Recently, Optimal Transport has been proposed as a probabilistic framework in Machine Learning for comparing and manipulating probability distributions. This is rooted in its rich history and theory, and has offered new solutions to different problems in machine learning, such as generative modeling and transfer learning. In this survey we explore contributions of Optimal Transport for Machine Learning over the period 2012 -- 2022, focusing on four sub-fields of Machine Learning: supervised, unsupervised, transfer and reinforcement learning. We further highlight the recent development in computational Optimal Transport, and its interplay with Machine Learning practice.
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning

Despite the impressive performance recently achieved by automatic speech recognition (ASR), we observe two primary challenges that hinder its broader applications: (1) The difficulty of introducing scalability into the model to support more languages with limited training, inference, and storage overhead; (2) The low-resource adaptation ability that enables effective low-resource adaptation while avoiding over-fitting and catastrophic forgetting issues. Inspired by recent findings, we hypothesize that we can address the above challenges with modules widely shared across languages. To this end, we propose an ASR framework, dubbed \METHODNS, that, \textit{for the first time}, simultaneously achieves strong multilingual scalability and low-resource adaptation ability thanks to its modularize-then-assemble strategy. Specifically, \METHOD learns a small set of generalizable sub-modules and adaptively assembles them for different languages to reduce the multilingual overhead and enable effective knowledge transfer for low-resource adaptation. Extensive experiments and visualizations demonstrate that \METHOD can effectively discover language similarity and improve multilingual and low-resource ASR performance over state-of-the-art (SOTA) methods, e.g., under multilingual-ASR, our framework achieves a 0.13$\sim$2.41 lower character error rate (CER) with 30\% smaller inference overhead over SOTA solutions on multilingual ASR and a comparable CER, with nearly 50 times fewer trainable parameters over SOTA solutions on low-resource tuning, respectively.
Approximation and Homogenization of Thermoelastic wave model

This paper deals with the approximation and homogenization of thermoelastic wave model. First, we study the homogenization problem of a weakly coupled thermoelastic wave model with rapidly varying coefficients, using a semigroup approach, two-scale convergence method and some variational techniques. We show that the limit semigroup can be obtained by using a weak version of the Trotter Kato convergence Theorem. Secondly, we consider the approximation of two thermoelastic wave model, one with exponential decay and the other one with polynomial decay. the numerical experiments indicate that the two discrete systems show different behavior of the spectra. Moreover, their discrete energies inherit the same behavior of the continuous ones. Finally we show numerically how the smoothness of data can impact the rate of decay of the energy associated the weakly coupled thermoelastic wave model.
The Effects of Rotation, Metallicity, and Magnetic Field on the Islands of Failed Supernovae

Failed supernovae (FSN) are a possible channel for the formation of heavy stellar-mass black holes (M BH > ∼30 M ⊙). However, the effects of metallicity, rotation, and magnetic field on the islands of explodabilty of massive stars are not clear. Here, we simulate the stellar structure and evolution in the mass range between 6 and 55 M ⊙ with different initial rotational velocities, metallicities, and magnetic fields from zero-age main sequence (ZAMS) to pre-collapse. We find that the rapid rotating stars can remain lower 12C mass fraction at the time of C ignition, which allows the transition, from convective carbon burning to radiative burning, to occur at lower M ZAMS than those from stars without rotation. However, the rapid rotation is unfavorable for FSN occurring but is conducive to long gamma-ray bursts (lGRBs) because it results in the specific angular momentum in the CO core being greater than the last stable orbit at core collapse. The increasing metallicity does not affect FSN islands, but high metallicity inhibits rotational mixing and is unfavorable for producing lGRBs. A magnetic field can constrain the mass-loss rate even for rapid rotating stars, resulting in higher mass at pre-collapse. The magnetic braking triggered by the magnetic field can reduce the rotation velocity for high-metallicity models, which decreases the specific angular momentum in the CO core and is favorable for FSN occurring. We suggest that the heavy-mass black holes detected by LIGO may originate from rapidly rotating massive stars with strong magnetic fields, rather than those with very low metallicity.
The NANOGrav 15 yr Data Set: Constraints on Supermassive Black Hole Binaries from the Gravitational-wave Background

The NANOGrav 15 yr data set shows evidence for the presence of a low-frequency gravitational-wave background (GWB). While many physical processes can source such low-frequency gravitational waves, here we analyze the signal as coming from a population of supermassive black hole (SMBH) binaries distributed throughout the Universe. We show that astrophysically motivated models of SMBH binary populations are able to reproduce both the amplitude and shape of the observed low-frequency gravitational-wave spectrum. While multiple model variations are able to reproduce the GWB spectrum at our current measurement precision, our results highlight the importance of accurately modeling binary evolution for producing realistic GWB spectra. Additionally, while reasonable parameters are able to reproduce the 15 yr observations, the implied GWB amplitude necessitates either a large number of parameters to be at the edges of expected values or a small number of parameters to be notably different from standard expectations. While we are not yet able to definitively establish the origin of the inferred GWB signal, the consistency of the signal with astrophysical expectations offers a tantalizing prospect for confirming that SMBH binaries are able to form, reach subparsec separations, and eventually coalesce. As the significance grows over time, higher-order features of the GWB spectrum will definitively determine the nature of the GWB and allow for novel constraints on SMBH populations.
cuSLINK: Single-linkage Agglomerative Clustering on the GPU

In this paper, we propose cuSLINK, a novel and state-of-the-art reformulation of the SLINK algorithm on the GPU which requires only $O(Nk)$ space and uses a parameter $k$ to trade off space and time. We also propose a set of novel and reusable building blocks that compose cuSLINK. These building blocks include highly optimized computational patterns for $k$-NN graph construction, spanning trees, and dendrogram cluster extraction. We show how we used our primitives to implement cuSLINK end-to-end on the GPU, further enabling a wide range of real-world data mining and machine learning applications that were once intractable. In addition to being a primary computational bottleneck in the popular HDBSCAN algorithm, the impact of our end-to-end cuSLINK algorithm spans a large range of important applications, including cluster analysis in social and computer networks, natural language processing, and computer vision. Users can obtain cuSLINK at https://docs.rapids. ai/api/cuml/latest/api/#agglomerative-clustering
Towards Measuring the Representation of Subjective Global Opinions in Language Models

Large language models (LLMs) may not equitably represent diverse global perspectives on societal issues. In this paper, we develop a quantitative framework to evaluate whose opinions model-generated responses are more similar to. We first build a dataset, GlobalOpinionQA, comprised of questions and answers from cross-national surveys designed to capture diverse opinions on global issues across different countries. Next, we define a metric that quantifies the similarity between LLM-generated survey responses and human responses, conditioned on country. With our framework, we run three experiments on an LLM trained to be helpful, honest, and harmless with Constitutional AI. By default, LLM responses tend to be more similar to the opinions of certain populations, such as those from the USA, and some European and South American countries, highlighting the potential for biases. When we prompt the model to consider a particular country's perspective, responses shift to be more similar to the opinions of the prompted populations, but can reflect harmful cultural stereotypes. When we translate GlobalOpinionQA questions to a target language, the model's responses do not necessarily become the most similar to the opinions of speakers of those languages. We release our dataset for others to use and build on. Our data is at https://huggingface.co/datasets/Anthropic/llm_global_opinions. We also provide an interactive visualization at https://llmglobalvalues.anthropic. com.
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale

Large-scale generative models such as GPT and DALL-E have revolutionized the research community. These models not only generate high fidelity outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are not filtered or enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation. In particular, Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to 20 times faster. Audio samples can be found in \url{https://voicebox.metademolab. com}.
Application of a New Designed ROADM to Improve the Performance of Elastic Optical Network

The quick rise of emerging technologies like spectrally efficient multicarrier with a higher order modulation and bandwidth variable wavelength selective switches have caused a shift of the optical network architecture from fixed to the flexible elastic optical network (EON), in terms of bit rate, center frequency spacing, modulation format, and optical reach. By using bandwidth-variable transceivers, colorless flexible-grid reconfigurable optical add-drop multiplexers (ROADM), as well as a choice of different optical source types, operators can provide a range of service types and improve network efficiency. Implementation of the EON network requires careful consideration, especially in terms of the ROADM structure to achieve optimal performance. We propose a low-cost ROADM structure with an order-based connection method that includes a high degree with a blocking probability of less than 10-4. Then, we applied the new design of ROADM in EON that transported 1 Tb/s. In the final step, we conclude that our system accommodated 20 percent more traffic demand over an average of the low-rate wavelength approach.
Holographic Batteries

We study a three-dimensional holographic CFT under the influence of a background electric field on a spacetime containing two black hole horizons. The electric background is fixed such that there is potential difference between the two boundary black holes, inducing a conserved current. By constructing the holographic duals to this set-up, which are solutions to the Einstein-Maxwell equations with a negative cosmological constant in four dimensions, we calculate, to a fully non-linear level, the conductivity of the CFT in this background. Interestingly, we find that the conductivity depends non-trivially on the potential difference. The bulk solutions are flowing geometries containing black hole horizons which are non-Killing and have non-zero expansion. We find a novel property that the past boundary of the future horizon lies deep in the bulk and show this property remains present after small perturbations of the temperature difference of the boundary black holes.