Miracle-dz/UniFAR-FTUs huggingface dataset:
REVIS: An Error Visualization Tool for Rust

Rust is a programming language that uses a concept of ownership to guarantee memory safety without the use of a garbage collector. However, some error messages related to ownership can be difficult to understand and fix, particularly those that depend on value lifetimes. To help developers fix lifetime-related errors, we developed REVIS, a VSCode extension that visualizes lifetime-related Rust compiler errors. We describe the design and implementation of the VSCode extension, along with a preliminary evaluation of its efficacy for student learners of Rust. Although the number of participants was too low to enable evaluation of the efficacy of REVIS, we gathered data regarding the prevalence and time to fix the compiler errors that the participants encountered.
Robustness for Spectral Clustering of General Graphs under Local Differential Privacy

Spectral clustering is a widely used algorithm to find clusters in networks. Several researchers have studied the stability of spectral clustering under local differential privacy with the additional assumption that the underlying networks are generated from the stochastic block model (SBM). However, we argue that this assumption is too restrictive since social networks do not originate from the SBM. Thus, delve into an analysis for general graphs in this work. Our primary focus is the edge flipping method -- a common technique for protecting local differential privacy. On a positive side, our findings suggest that even when the edges of an $n$-vertex graph satisfying some reasonable well-clustering assumptions are flipped with a probability of $O(\log n/n)$, the clustering outcomes are largely consistent. Empirical tests further corroborate these theoretical findings. Conversely, although clustering outcomes have been stable for dense and well-clustered graphs produced from the SBM, we show that in general, spectral clustering may yield highly erratic results on certain dense and well-clustered graphs when the flipping probability is $\omega(\log n/n)$. This indicates that the best privacy budget obtainable for general graphs is $\Theta(\log n)$.
Hybrid algorithm simulating non-equilibrium steady states of an open quantum system

Non-equilibrium steady states are a focal point of research in the study of open quantum systems. Previous variational algorithms for searching these steady states have suffered from resource-intensive implementations due to vectorization or purification of the system density matrix, requiring large qubit resources and long-range coupling. In this work, we present a novel variational quantum algorithm that efficiently searches for non-equilibrium steady states by simulating the operator-sum form of the Lindblad equation. By introducing the technique of random measurement, we are able to estimate the nonlinear cost function while reducing the required qubit resources by half compared to previous methods. Additionally, we prove the existence of the parameter shift rule in our variational algorithm, enabling efficient updates of circuit parameters using gradient-based classical algorithms. To demonstrate the performance of our algorithm, we conduct simulations for dissipative quantum transverse Ising and Heisenberg models, achieving highly accurate results. Our approach offers a promising solution for effectively addressing non-equilibrium steady state problems while overcoming computational limitations and implementation challenges.
Near Real-Time Gravitational Wave Data Analysis of the Massive Black Hole Binary with TianQin

Space-borne gravitational wave detectors can detect sources like the merger of massive black holes. The rapid identification and localization of the source would play a crucial role in multi-messenger observation. The geocentric orbit of the space-borne gravitational wave detector, TianQin, makes it possible to conduct real-time data transmission. In this manuscript, we develop a search and localization pipeline for massive black hole binaries with TianQin, under both regular and real-time data transmission modes. We demonstrate that with real-time data transmission, it is possible to accurately localize the massive black hole binaries on-the-fly. With the approaching of the merger, the localization rapidly shrinks, and the data analysis can be finished at a speed comparable to the data downlink speed.
Noisy Demkov-Kunike model

The Demkov-Kunike (DK) model, characterized by a time-dependent Rabi coupling $J~\text{sech}(t/T)$ and on-site detuning $\Delta_0+\Delta_1\tanh(t/T)$, has one of the most general forms of an exactly solvable two-state quantum system, and, therefore, it provides a paradigm for coherent manipulations of a qubit's quantum state. Despite its extensive applications in the noise-free cases, the exploration of the noisy DK model remains limited. Here, we extend the coherent DK model to take into account of a noisy coupling term $J\rightarrow J_{\text{noisy}}(t)$. We consider colored Markovian noise sources represented by the telegraph noise and Gaussian noise. We present exact solutions for the survival probability $Q^{\text{noisy}}_{\text{DK}}$ of the noisy DK model, namely the probability of the system to remain in its initial state. For the slow telegraph noise, we identify parameter regimes where the survival probability $Q^{\text{noisy}}_{\text{DK}}$ is suppressed rather than enhanced by noise. In contrast, for slow Gaussian noise, the noise always enhances the survival probability $Q^{\text{noisy}}_{\text{DK}}$, due to the absorption of noise quanta across the energy gap. This study not only complements the existing research on the noisy Landau-Zener model, but also provides valuable insights for the control of two-level quantum systems.
A Reinforcement Learning Approach for Robotic Unloading from Visual Observations

In this work, we focus on a robotic unloading problem from visual observations, where robots are required to autonomously unload stacks of parcels using RGB-D images as their primary input source. While supervised and imitation learning have accomplished good results in these types of tasks, they heavily rely on labeled data, which are challenging to obtain in realistic scenarios. Our study aims to develop a sample efficient controller framework that can learn unloading tasks without the need for labeled data during the learning process. To tackle this challenge, we propose a hierarchical controller structure that combines a high-level decision-making module with classical motion control. The high-level module is trained using Deep Reinforcement Learning (DRL), wherein we incorporate a safety bias mechanism and design a reward function tailored to this task. Our experiments demonstrate that both these elements play a crucial role in achieving improved learning performance. Furthermore, to ensure reproducibility and establish a benchmark for future research, we provide free access to our code and simulation.
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model

Sentence Representation Learning (SRL) is a fundamental task in Natural Language Processing (NLP), with the Contrastive Learning of Sentence Embeddings (CSE) being the mainstream technique due to its superior performance. An intriguing phenomenon in CSE is the significant performance gap between supervised and unsupervised methods, with their only difference lying in the training data. Previous works attribute this performance gap to differences in two representation properties (alignment and uniformity). However, since alignment and uniformity only measure the results, they fail to answer "What aspects of the training data contribute to the performance gap? " and "How can the performance gap be narrowed? ", In this paper, we conduct empirical experiments to answer these "What" and "How" questions. We first answer the "What" question by thoroughly comparing the behavior of supervised and unsupervised CSE during their respective training processes. From the comparison, we identify the similarity pattern as a key factor to the performance gap, and introduce a metric, called Relative Fitting Difficulty (RFD), to measure the complexity of the similarity pattern. Then, based on the insights gained from the "What" question, we tackle the "How" question by increasing the pattern complexity of the training data. We achieve this by leveraging the In-Context Learning (ICL) capability of the Large Language Model (LLM) to generate data that simulates complex patterns. By utilizing the hierarchical patterns in the LLM-generated data, we effectively narrow the gap between supervised and unsupervised CSE. We release our codes and appendix at https://github.com/BDBC-KG-NLP/NGCSE.
Modelling individual motion sickness accumulation in vehicles and driving simulators

Users of automated vehicles will move away from being drivers to passengers, preferably engaged in other activities such as reading or using laptops and smartphones, which will strongly increase susceptibility to motion sickness. Similarly, in driving simulators, the presented visual motion with scaled or even without any physical motion causes an illusion of passive motion, creating a conflict between perceived and expected motion, and eliciting motion sickness. Given the very large differences in sickness susceptibility between individuals, we need to consider sickness at an individual level. This paper combines a group-averaged sensory conflict model with an individualized accumulation model to capture individual differences in motion sickness susceptibility across various vision conditions. The model framework can be used to develop personalized models for users of automated vehicles and improve the design of new motion cueing algorithms for simulators. The feasibility and accuracy of this model framework are verified using two existing datasets with sickening. Both datasets involve passive motion, representative of being driven by an automated vehicle. The model is able to fit an individuals motion sickness responses using only 2 parameters (gain K1 and time constant T1), as opposed to the 5 parameters in the original model. This ensures unique parameters for each individual. Better fits, on average by a factor of 1.7 of an individuals motion sickness levels, are achieved as compared to using only the group-averaged model. Thus, we find that models predicting group-averaged sickness incidence cannot be used to predict sickness at an individual level. On the other hand, the proposed combined model approach predicts individual motion sickness levels and thus can be used to control sickness.
The ecology and conservation of bryophytes in Tasmanian wet eucalypt forest

The aim of this study was to determine the factors that affect bryophyte richness and species composition in wet eucalypt forest, including old growth and forest disturbed by wildfire or silvicultural practice. Approximately one third of the total bryophyte flora for Tasmania was recorded in old growth mixed forest, with more liverwort than moss species found. Bryophyte species composition was significantly different between groups of sites of forest from the northwest, central and southern areas of the state. Mean annual temperature, altitude, rainfall of the driest month and aspect were most significant in predicting variation in bryophyte species composition. The use of vascular plants as surrogates for the conservation of bryophyte species was examined. Vascular plant and fern species richness were significant but poor predictors of bryophyte species richness. A minimum set of 31 sites reserved all vascular species and a large percentage (82.9%) of bryophyte species at least once. Thus, reserves selected using vascular plants are likely to reserve a large proportion of bryophyte species. The reserve sets included more sites of regenerating forest than old growth forest indicating the importance for conservation of multi-aged wet eucalypt forest. Many species preferentially occurred on a substrate type within a particular forest age class. The bryophyte species composition on old growth Nothofagus cunninghamii and Atherosperma moschatum trees were significantly dissimilar to a large number of other substrate/age class groups. Consistent with previous literature, bark type affected species composition. Comparisons of bryophytes in sites disturbed by wildfire and logging found four moss species occurred more frequently in logging than wildfire regeneration, whereas six of the seven bryophytes species that occurred more frequently after wildfire than logging disturbance were liverworts. Overall, little difference in bryophyte and vascular species composition was found between logging and wildfire regeneration. When sites were separated into regions, bryophyte species composition differed between logging and wildfire only in the forests of central Tasmania, where Eucalyptus regnans is dominant. Successional stages of bryophytes species occurrence after disturbance were documented. Species occurring frequently in primary succession did not survive into later successional stages. Many species that established in post-primary successional forest persisted into late successional forest. Liverwort species dominated in late successional forest. The exclusive occurrence of the epiphytic mosses Neckera pennata and Calyptopogon mnioides in regenerating forest is strongly associated with the presence of Pomaderris apetala and Acacia dealbata trees.
MCQUIC: Multicast and unicast in a single transport protocol

Multicast enables efficient one-to-many communications. Several applications benefit from its scalability properties, e.g., live-streaming and large-scale software updates. Historically, multicast applications have used specialized transport protocols. The flexibility of the recently standardized QUIC protocol opens the possibility of providing both unicast and multicast services to applications with a single transport protocol. We present MCQUIC, an extended version of the QUIC protocol that supports multicast communications. We show how QUIC features and built-in security can be leveraged for multicast transport. We present the design of MCQUIC and implement it in Cloudflare quiche. We assess its performance through benchmarks and in emulated networks under realistic scenarios. We also demonstrate MCQUIC in a campus network. By coupling QUIC with our multicast extension, applications can rely on multicast for efficiency with the possibility to fall back on unicast in case of incompatible network conditions.
Hardening RGB-D Object Recognition Systems against Adversarial Patch Attacks

RGB-D object recognition systems improve their predictive performances by fusing color and depth information, outperforming neural network architectures that rely solely on colors. While RGB-D systems are expected to be more robust to adversarial examples than RGB-only systems, they have also been proven to be highly vulnerable. Their robustness is similar even when the adversarial examples are generated by altering only the original images' colors. Different works highlighted the vulnerability of RGB-D systems; however, there is a lacking of technical explanations for this weakness. Hence, in our work, we bridge this gap by investigating the learned deep representation of RGB-D systems, discovering that color features make the function learned by the network more complex and, thus, more sensitive to small perturbations. To mitigate this problem, we propose a defense based on a detection mechanism that makes RGB-D systems more robust against adversarial examples. We empirically show that this defense improves the performances of RGB-D systems against adversarial examples even when they are computed ad-hoc to circumvent this detection mechanism, and that is also more effective than adversarial training.
Learning topological operations on meshes with application to block decomposition of polygons

We present a learning based framework for mesh quality improvement on unstructured triangular and quadrilateral meshes. Our model learns to improve mesh quality according to a prescribed objective function purely via self-play reinforcement learning with no prior heuristics. The actions performed on the mesh are standard local and global element operations. The goal is to minimize the deviation of the node degrees from their ideal values, which in the case of interior vertices leads to a minimization of irregular nodes.
Unsupervised Contrast-Consistent Ranking with Language Models

Language models contain ranking-based knowledge and are powerful solvers of in-context ranking tasks. For instance, they may have parametric knowledge about the ordering of countries by size or may be able to rank product reviews by sentiment. We compare pairwise, pointwise and listwise prompting techniques to elicit a language model’s ranking knowledge. However, we find that even with careful calibration and constrained decoding, prompting-based techniques may not always be self-consistent in the rankings they produce. This motivates us to explore an alternative approach that is inspired by an unsupervised probing method called Contrast-Consistent Search (CCS). The idea is to train a probe guided by a logical constraint: a language model’s representation of a statement and its negation must be mapped to contrastive true-false poles consistently across multiple statements. We hypothesize that similar constraints apply to ranking tasks where all items are related via consistent, pairwise or listwise comparisons. To this end, we extend the binary CCS method to Contrast-Consistent Ranking (CCR) by adapting existing ranking methods such as the Max-Margin Loss, Triplet Loss and an Ordinal Regression objective. Across different models and datasets, our results confirm that CCR probing performs better or, at least, on a par with prompting.
Détermination des températures profondes du Bassin du Sud-Est de la France et relations entre anomalies thermiques, géologie et circulations hydrothermales par modélisation 3D

Les precedentes estimations des temperatures profondes en France mettent en evidence trois anomalies thermiques positives principales dont une est centree entre Marseille et Montpellier. Ce travail de these presente l'etude du regime thermique du Bassin du Sud-Est a partir d'une reevaluation des temperatures profondes corrigees des effets thermiques transitoires generes lors des mesures en forage petrolier. 203 donnees BHT corrigees et 10 DST (mesures stables) issues de 103 forages ont ainsi ete obtenues. Le gradient thermique moyen du Bassin (31,3°C/km) est proche de la moyenne francaise (32,5°C/km) mais les gradients locaux varient d'environ 20 a 45°C/km. Une analyse geostatistique nous a permis de construire un modele thermique de representation 3D de la surface a 6km de profondeur duquel nous avons extrait des coupes et cartes thermiques a differentes profondeurs. De nouvelles anomalies thermiques de plus petite echelle ont ainsi pu etre identifiees, certaines etant positives (du cote de Montpellier, Lodeve, Gap et La Drome) et d'autres negatives (dans les Cevennes et la region d'Aix-en-Provence). Une compilation de donnees geophysiques nous a permis de discuter ces anomalies en termes de geologie locale et de phenomenes a grande echelle (variations de profondeur du Moho, epaisseur des sediments), mais il semble que des circulations de fluides puissent mieux expliquer la localisation, l'amplitude et les longueurs d'ondes des signaux thermiques qui suivent la direction des failles majeures en surface. Le role de la circulation de fluides dans la mise en place et l'evolution des anomalies thermiques au sein des zones de faille est illustre par des modeles numeriques en regime stationnaire et transitoire. Ces modeles permettent de reproduire des anomalies de plusieurs dizaines de degres. Ce phenomene est amplifie lorsqu'une anisotropie de permeabilite est prise en compte. De plus, la dependance de la permeabilite avec la profondeur permet d'obtenir des superpositions d'anomalies positives et negatives sur un meme profil vertical comme nous l'observons sur les coupes thermiques issues des mesures. Loin des zones permeables, les anomalies peuvent s'expliquer par des processus conductifs au sein d'objets dans lesquels les contrastes de conductivite thermique peuvent etre importants. L'anisotropie de conductivite dans les formations argileuses peut etre responsable du developpement d'anomalies proches de celles observees entre les zones permeables. Enfin, les circulations hydrothermales dans les zones de faille associees a des sediments anisotropes pourraient expliquer la forme, l'evolution en profondeur et l'intensite des anomalies thermiques identifiees.
Defensive Alliances in Signed Networks

The analysis of (social) networks and multi-agent systems is a central theme in Artificial Intelligence. Some line of research deals with finding groups of agents that could work together to achieve a certain goal. To this end, different notions of so-called clusters or communities have been introduced in the literature of graphs and networks. Among these, defensive alliance is a kind of quantitative group structure. However, all studies on the alliance so for have ignored one aspect that is central to the formation of alliances on a very intuitive level, assuming that the agents are preconditioned concerning their attitude towards other agents: they prefer to be in some group (alliance) together with the agents they like, so that they are happy to help each other towards their common aim, possibly then working against the agents outside of their group that they dislike. Signed networks were introduced in the psychology literature to model liking and disliking between agents, generalizing graphs in a natural way. Hence, we propose the novel notion of a defensive alliance in the context of signed networks. We then investigate several natural algorithmic questions related to this notion. These, and also combinatorial findings, connect our notion to that of correlation clustering, which is a well-established idea of finding groups of agents within a signed network. Also, we introduce a new structural parameter for signed graphs, signed neighborhood diversity snd, and exhibit a parameterized algorithm that finds a smallest defensive alliance in a signed graph.
Distributed Learning Dynamics for Coalitional Games

In the framework of transferable utility coalitional games, a scoring (characteristic) function determines the value of any subset/coalition of agents. Agents decide on both which coalitions to form and the allocations of the values of the formed coalitions among their members. An important concept in coalitional games is that of a core solution, which is a partitioning of agents into coalitions and an associated allocation to each agent under which no group of agents can get a higher allocation by forming an alternative coalition. We present distributed learning dynamics for coalitional games that converge to a core solution whenever one exists. In these dynamics, an agent maintains a state consisting of (i) an aspiration level for its allocation and (ii) the coalition, if any, to which it belongs. In each stage, a randomly activated agent proposes to form a new coalition and changes its aspiration based on the success or failure of its proposal. The coalition membership structure is changed, accordingly, whenever the proposal succeeds. Required communications are that: (i) agents in the proposed new coalition need to reveal their current aspirations to the proposing agent, and (ii) agents are informed if they are joining the proposed coalition or if their existing coalition is broken. The proposing agent computes the feasibility of forming the coalition. We show that the dynamics hit an absorbing state whenever a core solution is reached. We further illustrate the distributed learning dynamics on a multi-agent task allocation setting.