Miracle-dz/UniFAR-FTUs huggingface dataset:
Relations between source parameters for large Persian earthquakes

Empirical relationships for magnitude scales and fault parameters were produced using 436 Iranian intraplate earthquakes of recently regional databases since the continental events represent a large portion of total seismicity of Iran. The relations between different source parameters of the earthquakes were derived using input information which has usefully been provided from the databases after 1900. Suggested equations for magnitude scales relate the body-wave, surface-wave as well as local magnitude scales to scalar moment of the earthquakes. Also, dependence of source parameters as surface and subsurface rupture length and maximum surface displacement on the moment magnitude for some well documented earthquakes was investigated. For meeting this aim, ordinary linear regression procedures were employed for all relations. Our evaluations reveal a fair agreement between obtained relations and equations described in other worldwide and regional works in literature. The M 0 -m b and M 0 -M S equations are correlated well to the worldwide relations. Also, both M 0 -M S and M 0 -M L relations have a good agreement with regional studies in Taiwan. The equations derived from this study mainly confirm the results of the global investigations about rupture length of historical and instrumental events. However, some relations like M W -M N and M N -M L which are remarkably unlike to available regional works (e.g., American and Canadian) were also found.
Collaborative Adaptation: Learning to Recover from Unforeseen Malfunctions in Multi-Robot Teams

Cooperative multi-agent reinforcement learning (MARL) approaches tackle the challenge of finding effective multi-agent cooperation strategies for accomplishing individual or shared objectives in multi-agent teams. In real-world scenarios, however, agents may encounter unforeseen failures due to constraints like battery depletion or mechanical issues. Existing state-of-the-art methods in MARL often recover slowly -- if at all -- from such malfunctions once agents have already converged on a cooperation strategy. To address this gap, we present the Collaborative Adaptation (CA) framework. CA introduces a mechanism that guides collaboration and accelerates adaptation from unforeseen failures by leveraging inter-agent relationships. Our findings demonstrate that CA enables agents to act on the knowledge of inter-agent relations, recovering from unforeseen agent failures and selecting appropriate cooperative strategies.
Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey

The recent enthusiasm for open-world vision systems show the high interest of the community to perform perception tasks outside of the closed-vocabulary benchmark setups which have been so popular until now. Being able to discover objects in images/videos without knowing in advance what objects populate the dataset is an exciting prospect. But how to find objects without knowing anything about them? Recent works show that it is possible to perform class-agnostic unsupervised object localization by exploiting self-supervised pre-trained features. We propose here a survey of unsupervised object localization methods that discover objects in images without requiring any manual annotation in the era of self-supervised ViTs. We gather links of discussed methods in the repository https://github.com/valeoai/Awesome-Unsupervised-Object-Localization.
Optimal estimation of high-dimensional unitary transformations

We propose an estimation procedure for $d$-dimensional unitary transformations. For $d>2$, the unitary transformations close to the identity are estimated saturating the quantum Cram\'er-Rao bound. For $d=2$, the estimation of all unitary transformations is also optimal with some prior information. We show through numerical simulations that, even in the absence of prior information, two-dimensional unitary transformations can be estimated with greater precision than by means of standard quantum process tomography.
Large Language Models for Code Analysis: Do LLMs Really Do Their Job?

Large language models (LLMs) have demonstrated significant potential in the realm of natural language understanding and programming code processing tasks. Their capacity to comprehend and generate human-like code has spurred research into harnessing LLMs for code analysis purposes. However, the existing body of literature falls short in delivering a systematic evaluation and assessment of LLMs' effectiveness in code analysis, particularly in the context of obfuscated code. This paper seeks to bridge this gap by offering a comprehensive evaluation of LLMs' capabilities in performing code analysis tasks. Additionally, it presents real-world case studies that employ LLMs for code analysis. Our findings indicate that LLMs can indeed serve as valuable tools for automating code analysis, albeit with certain limitations. Through meticulous exploration, this research contributes to a deeper understanding of the potential and constraints associated with utilizing LLMs in code analysis, paving the way for enhanced applications in this critical domain.
Benchmarking Function Hook Latency in Cloud-Native Environments

Researchers and engineers are increasingly adopting cloud-native technologies for application development and performance evaluation. While this has improved the reproducibility of benchmarks in the cloud, the complexity of cloud-native environments makes it difficult to run benchmarks reliably. Cloud-native applications are often instrumented or altered at runtime, by dynamically patching or hooking them, which introduces a significant performance overhead. Our work discusses the benchmarking-related pitfalls of the dominant cloud-native technology, Kubernetes, and how they affect performance measurements of dynamically patched or hooked applications. We present recommendations to mitigate these risks and demonstrate how an improper experimental setup can negatively impact latency measurements.
Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity

Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitations in computational efficiency, balanced matching, and participation incentives. To address these challenges, we propose a graph-based allocation framework optimized for massive heterogeneous spatial data. The framework first clusters similar tasks and workers separately to reduce allocation scale. Next, it constructs novel non-crossing graph structures to model balanced adjacencies between unevenly distributed tasks and workers. Based on the graphs, a bidirectional worker-task matching scheme is designed to produce allocations optimized for mutual interests. Extensive experiments on real-world datasets analyze the performance under various parameter settings.
Deep Incremental Learning of Imbalanced Data for Just-In-Time Software Defect Prediction

This work stems from three observations on prior Just-In-Time Software Defect Prediction (JIT-SDP) models. First, prior studies treat the JIT-SDP problem solely as a classification problem. Second, prior JIT-SDP studies do not consider that class balancing processing may change the underlying characteristics of software changeset data. Third, only a single source of concept drift, the class imbalance evolution is addressed in prior JIT-SDP incremental learning models. We propose an incremental learning framework called CPI-JIT for JIT-SDP. First, in addition to a classification modeling component, the framework includes a time-series forecast modeling component in order to learn temporal interdependent relationship in the changesets. Second, the framework features a purposefully designed over-sampling balancing technique based on SMOTE and Principal Curves called SMOTE-PC. SMOTE-PC preserves the underlying distribution of software changeset data. In this framework, we propose an incremental deep neural network model called DeepICP. Via an evaluation using \numprojs software projects, we show that: 1) SMOTE-PC improves the model's predictive performance; 2) to some software projects it can be beneficial for defect prediction to harness temporal interdependent relationship of software changesets; and 3) principal curves summarize the underlying distribution of changeset data and reveals a new source of concept drift that the DeepICP model is proposed to adapt to.
Safe RLHF: Safe Reinforcement Learning from Human Feedback

With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly decouples human preferences regarding helpfulness and harmlessness, effectively avoiding the crowdworkers' confusion about the tension and allowing us to train separate reward and cost models. We formalize the safety concern of LLMs as an optimization task of maximizing the reward function while satisfying specified cost constraints. Leveraging the Lagrangian method to solve this constrained problem, Safe RLHF dynamically adjusts the balance between the two objectives during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing model performance compared to existing value-aligned algorithms. Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with collected human preferences, significantly improving its helpfulness and harmlessness according to human evaluations.
$4d$ steady gradient Ricci solitons with nonnegative curvature away from a compact set

In the paper, we analysis the asymptotic behavior of noncompact $\kappa$-noncollapsed steady gradient Ricci soliton $(M, g)$ with nonnegative curvature operator away from a compact set $K$ of $M$. In particular, we prove: any $4d$ noncompact $\kappa$-noncollapsed steady gradient Ricci soliton $(M^4, g)$ with nonnegative sectional curvature must be a Bryant Ricci soliton up to scaling if it admits a sequence of rescaled flows of $(M^4, g)$, which converges subsequently to a family of shrinking quotient cylinders.
SURE: A Visualized Failure Indexing Approach using Program Memory Spectrum

Failure indexing is a longstanding crux in software testing and debugging, the goal of which is to automatically divide failures (e.g., failed test cases) into distinct groups according to the culprit root causes, as such multiple faults in a faulty program can be handled independently and simultaneously. This community has long been plagued by two challenges: 1) The effectiveness of division is still far from promising. Existing techniques only employ a limited source of run-time data (e.g., code coverage) to be failure proximity, which typically delivers unsatisfactory results. 2) The outcome can be hardly comprehensible. A developer who receives the failure indexing result does not know why all failures should be divided the way they are. This leads to difficulties for developers to be convinced by the result, which in turn affects the adoption of the results. To tackle these challenges, in this paper, we propose SURE, a viSUalized failuRe indExing approach using the program memory spectrum. We first collect the run-time memory information at preset breakpoints during the execution of failed test cases, and transform it into human-friendly images (called program memory spectrum, PMS). Then, any pair of PMS images that serve as proxies for two failures is fed to a trained Siamese convolutional neural network, to predict the likelihood of them being triggered by the same fault. Results demonstrate the effectiveness of SURE: It achieves 101.20% and 41.38% improvements in faults number estimation, as well as 105.20% and 35.53% improvements in clustering, compared with the state-of-the-art technique in this field, in simulated and real-world environments, respectively. Moreover, we carry out a human study to quantitatively evaluate the comprehensibility of PMS, revealing that this novel type of representation can help developers better comprehend failure indexing results.
Perceptual Assessment and Optimization of High Dynamic Range Image Rendering

The increasing popularity of high dynamic range (HDR) imaging stems from its ability to faithfully capture luminance levels in natural scenes. However, HDR image quality assessment has been insufficiently addressed. Existing models are mostly designed for low dynamic range (LDR) images, which exhibit poorly correlated with human perception of HDR image quality. To fill this gap, we propose a family of HDR quality metrics by transferring the recent advancements in LDR domain. The key step in our approach is to employ a simple inverse display model to decompose an HDR image into a stack of LDR images with varying exposures. Subsequently, these LDR images are evaluated using state-of-the-art LDR quality metrics. Our family of HDR quality models offer three notable advantages. First, specific exposures (i.e., luminance ranges) can be weighted to emphasize their assessment when calculating the overall quality score. Second, our HDR quality metrics directly inherit the capabilities of their base LDR quality models in assessing LDR images. Third, our metrics do not rely on human perceptual data of HDR image quality for re-calibration. Experiments conducted on four human-rated HDR image quality datasets indicate that our HDR quality metrics consistently outperform existing methods, including the HDR-VDP family. Furthermore, we demonstrate the promise of our models in the perceptual optimization of HDR novel view synthesis.
Why do oceanic negative cloud‐to‐ground lightning exhibit larger peak current values?

This study examines the temporal (monthly) and spatial climatology (2004–2010) of the first return stroke of the cloud‐to‐ground (CG) lightning flash peak current (Ip) across various land/water boundaries over the contiguous United States. Four regions are examined: the Gulf of Mexico (region 1), the Florida peninsula (region 2), Lake Michigan (region 3), and part of the U.S. Mid‐Atlantic (region 4). The crosss across the coastlines of regions 1, 2, and 4 show a gradual oceanward increase in the mean negative polarity CG peak current values (−Ip). This transition along the respective land/ocean boundaries is not sharp but gradual. In direct contrast with ocean, there is no consistent behavior in −Ip values as we move from land out across the fresh water of Lake Michigan (region 3). Meanwhile, the positive CG flash peak current (+Ip) values do not exhibit a consistent variation across any coastal boundary. For region 1, the −Ip values increase as we move toward the coast (southwards) especially during the wet season (June–October). This finding is in direct contrast with studies that documented winter as the season of maximum −Ip values. The zonal and seasonal variations of −Ip values across region 4 are not quite as pronounced, but the oceanic −Ip values are still larger than over the adjoining landmass. We explore in turn which up to date hypotheses pertinent to the oceanic −Ip enhancement are supported or refuted by our findings. It is concluded that the oceanic −Ip enhancement is not an artifact related to CG detection or Ip retrieval methods, nor is it likely related to the cloud top heights or CG activity. The study cannot refute the role of electrical conductivity and its contribution to CG leader attachment processes. However, given the observed “blurred transition” of the Ip values across the coastlines this paper suggests that likely the main physical mechanism is acting on the thundercloud potential. The recently suggested role of sodium chloride (NaCl) but also the role of ice crystal size (implicated herein), as possible modulators of the thundercloud potential, exhibit distinct pros and cons. Their candidacy is supported by their strong physical links to the electrostatic charging and thundercloud electric potential buildup but also by the exhibited blurred −Ip transition across the coastlines. In contrast, the suggested mechanisms cannot individually explain the observed −Ip enhancement in terms of season, NaCl concentrations, and absence of similar behavior in the respective +Ip values.
Demystifying the Myths and Legends of Nonconvex Convergence of SGD

Stochastic gradient descent (SGD) and its variants are the main workhorses for solving large-scale optimization problems with nonconvex objective functions. Although the convergence of SGDs in the (strongly) convex case is well-understood, their convergence for nonconvex functions stands on weak mathematical foundations. Most existing studies on the nonconvex convergence of SGD show the complexity results based on either the minimum of the expected gradient norm or the functional sub-optimality gap (for functions with extra structural property) by searching the entire range of iterates. Hence the last iterations of SGDs do not necessarily maintain the same complexity guarantee. This paper shows that an $\epsilon$-stationary point exists in the final iterates of SGDs, given a large enough total iteration budget, $T$, not just anywhere in the entire range of iterates -- a much stronger result than the existing one. Additionally, our analyses allow us to measure the density of the $\epsilon$-stationary points in the final iterates of SGD, and we recover the classical $O(\frac{1}{\sqrt{T}})$ asymptotic rate under various existing assumptions on the objective function and the bounds on the stochastic gradient. As a result of our analyses, we addressed certain myths and legends related to the nonconvex convergence of SGD and posed some thought-provoking questions that could set new directions for research.
Solution to the Conflict between the Estimations of Resolved and Unresolved Galaxy Stellar Mass from the Perspective of JWST

By utilizing the spatially resolved photometry of galaxies at 0.2 < z < 3.0 in the CEERS field, we estimate the resolved and unresolved stellar mass via fitting of the spectral energy distribution (SED) to study the discrepancy between them. We first compare M * derived from photometry with and without the JWST wavelength coverage and find that M * can be overestimated by up to 0.2 dex when lacking rest-frame near-infrared (NIR) data. The SED fitting process tends to overestimate both stellar age and dust attenuation in the absence of rest-frame NIR data, consequently leading to a larger observed mass-to-light ratio and hence an elevated M *. With the inclusion of the JWST NIR photometry, we find no significant disparity between the estimates of resolved and unresolved stellar mass, providing a plausible solution to the conflict between them out to z ∼ 3. Further investigation demonstrates that reliable M * estimates can be obtained, regardless of whether they are derived from spatially resolved or spatially unresolved photometry, so long as the reddest filter included in the SED fitting has a rest-frame wavelength longer than 10000 Å.
Minimalist and High-Performance Semantic Segmentation with Plain Vision Transformers

In the wake of Masked Image Modeling (MIM), a diverse range of plain, non-hierarchical Vision Transformer (ViT) models have been pre-trained with extensive datasets, offering new paradigms and significant potential for semantic segmentation. Current state-of-the-art systems incorporate numerous inductive biases and employ cumbersome decoders. Building upon the original motivations of plain ViTs, which are simplicity and generality, we explore high-performance `minimalist' systems to this end. Our primary purpose is to provide simple and efficient baselines for practical semantic segmentation with plain ViTs. Specifically, we first explore the feasibility and methodology for achieving high-performance semantic segmentation using the last feature map. As a result, we introduce the PlainSeg, a model comprising only three 3$\times$3 convolutions in addition to the transformer layers (either encoder or decoder). In this process, we offer insights into two underlying principles: (i) high-resolution features are crucial to high performance in spite of employing simple up-sampling techniques and (ii) the slim transformer decoder requires a much larger learning rate than the wide transformer decoder. On this basis, we further present the PlainSeg-Hier, which allows for the utilization of hierarchical features. Extensive experiments on four popular benchmarks demonstrate the high performance and efficiency of our methods. They can also serve as powerful tools for assessing the transfer ability of base models in semantic segmentation. Code is available at \url{https://github.com/ydhongHIT/PlainSeg}.
Van der Waals chain: A simple model for Casimir forces in dielectrics

The Casimir force between dielectric bodies is well-understood, but not the Casimir force inside a dielectric, in particular its renormalization. We develop and analyse a simple model for the Casimir forces inside a medium that is completely free of renormalization and show then how renormalization emerges. We consider a one-dimensional chain of point particles interacting with each other by scattering the zero-point fluctuations of the electromagnetic field confined to one dimension. We develop a fast, efficient algorithm for calculating the forces on each particle and apply it to study the macroscopic limit of infinitely many, infinitely weak scatterers. The force density converges for piece-wise homogeneous media, but diverges in inhomogeneous media, which would cause instant collapse in theory. We argue that short-range counter forces in the medium prevent this collapse in reality. Their effect appears as the renormalization of the Casimir stress in dielectrics. Our simple model also allows us to derive an elementary analogue of the trace anomaly of quantum fields in curved space.
Towards Simpler Sorting Networks and Monotone Circuits for Majority

In this paper, we study the problem of computing the majority function by low-depth monotone circuits and a related problem of constructing low-depth sorting networks. We consider both the classical setting with elementary operations of arity $2$ and the generalized setting with operations of arity $k$, where $k$ is a parameter. For both problems and both settings, there are various constructions known, the minimal known depth being logarithmic. However, there is currently no known construction that simultaneously achieves sub-log-squared depth, effective constructability, simplicity, and has a potential to be used in practice. In this paper we make progress towards resolution of this problem. For computing majority by standard monotone circuits (gates of arity 2) we provide an explicit monotone circuit of depth $O(\log_2^{5/3} n)$. The construction is a combination of several known and not too complicated ideas. For arbitrary arity of gates $k$ we provide a new sorting network architecture inspired by representation of inputs as a high-dimensional cube. As a result we provide a simple construction that improves previous upper bound of $4 \log_k^2 n$ to $2 \log_k^2 n$. We prove the similar bound for the depth of the circuit computing majority of $n$ bits consisting of gates computing majority of $k$ bits. Note, that for both problems there is an explicit construction of depth $O(\log_k n)$ known, but the construction is complicated and the constant hidden in $O$-notation is huge.