Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Abstractions for Characterizing Communication Requirements in Asynchronous Distributed Systems

Whereas distributed computing research has been very successful in exploring the solvability/impossibility border of distributed computing problems like consensus in representative classes of computing models with respect to model parameters like failure bounds, this is not the case for characterizing necessary and sufficient communication requirements. In this paper, we introduce network abstractions as a novel approach for modeling communication requirements in asynchronous distributed systems. A network abstraction of a run is a sequence of directed graphs on the set of processes, where the $i$-th graph defines the potential message chains that may arise in the $i$-th portion of the run. Formally, it is defined via associating (potential) message sending times with the corresponding message receiving times in a message schedule. Network abstractions allow to reason about the future causal cones that might arise in a run, hence also facilitate reasoning about liveness properties, and are inherently compatible with temporal epistemic reasoning frameworks. We demonstrate the utility of our approach by providing necessary and sufficient network abstractions for solving the canonical firing rebels with relay (FRR) problem, and variants thereof, in asynchronous systems with up to $f$ byzantine processes. FRR is not only a basic primitive in clock synchronization and consensus algorithms, but also integrates several distributed computing problems, namely triggering events, agreement and even stabilizing agreement, in a single problem instance.
Seismic Site Characterization in Delhi Region using Multi channel Analysis of Shear wave Velocity (MASW) Testing

After the Bhuj earthquake the national capital region of Delhi attracted major attention of several scientific studies in the recent times. Since Delhi falls in zone IV (IS 1893:2002) with high seismic activity, there is a great need for site characterization and seismic hazard mapping of the area. Multi Channel Analysis of Surface Wave (MASW) tests were done in Delhi at 118 sites in predefined grids of 2kmX3km each. Shear-wave velocity, VS, is an important parameter for evaluating dynamic behavior of soil. This test was carried out using 48 channel digital engineering seismograph with 4.5 Hz geophones. Data was analyzed using SeisImager/SW software and two dimensional shear wave velocity models at every 5m depth from ground surface was developed. Also, the average shear wave velocity up to 30m (VS30) is measured which is used for site characterization. Based on the Vs30 value, Delhi is divided into three zones i.e., zone A (VS30 >350m/s), zone B (VS30 = 250- 350m/s) and zone C (VS30< 250m/s).
Proving a conjecture on the upper bound of semistrong chromatic indices of graphs

Let $G=(V(G), E(G))$ be a graph with maximum degree $\Delta$. For a subset $M$ of $E(G)$, we denote by $G[V(M)]$ the subgraph of $G$ induced by the endvertices of edges in $M$. We call $M$ a semistrong matching if each edge of $M$ is incident with a vertex that is of degree 1 in $G[V(M)]$. Given a positive integer $k$, a semistrong $k$-edge-coloring of $G$ is an edge coloring using at most $k$ colors in which each color class is a semistrong matching of $G$. The semistrong chromatic index of $G$, denoted by $\chi'_{ss}(G)$, is the minimum integer $k$ such that $G$ has a semistrong $k$-edge-coloring. Recently, Lu\v{z}ar, Mockov\v{c}iakov\'a and Sot\'ak conjectured that $\chi'_{ss}(G)\le \Delta^{2}-1$ for any connected graph $G$ except the complete bipartite graph $K_{\Delta,\Delta}$. In this paper, we settle this conjecture by proving that each such graph $G$ other than a cycle on $7$ vertices has a semistrong edge coloring using at most $\Delta^{2}-1$ colors.
Unified Browsing Models for Linear and Grid Layouts

Many information access systems operationalize their results in terms of rankings, which are then displayed to users in various ranking layouts such as linear lists or grids. User interaction with a retrieved item is highly dependent on the item's position in the layout, and users do not provide similar attention to every position in ranking (under any layout model). User attention is an important component in the evaluation process of ranking, due to its use in effectiveness metrics that estimate utility as well as fairness metrics that evaluate ranking based on social and ethical concerns. These metrics take user browsing behavior into account in their measurement strategies to estimate the attention the user is likely to provide to each item in ranking. Research on understanding user browsing behavior has proposed several user browsing models, and further observed that user browsing behavior differs with different ranking layouts. However, the underlying concepts of these browsing models are often similar, including varying components and parameter settings. We seek to leverage that similarity to represent multiple browsing models in a generalized, configurable framework which can be further extended to more complex ranking scenarios. In this paper, we describe a probabilistic user browsing model for linear rankings, show how they can be configured to yield models commonly used in current evaluation practice, and generalize this model to also account for browsing behaviors in grid-based layouts. This model provides configurable framework for estimating the attention that results from user browsing activity for a range of IR evaluation and measurement applications in multiple formats, and also identifies parameters that need to be estimated through user studies to provide realistic evaluation beyond ranked lists.
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions

Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks. However, it remains unclear whether the existing focus of NLP research accurately captures the genuine requirements of human users. This paper provides a comprehensive analysis of the divergence between current NLP research and the needs of real-world NLP applications via a large-scale collection of user-GPT conversations. We analyze a large-scale collection of real user queries to GPT. We compare these queries against existing NLP benchmark tasks and identify a significant gap between the tasks that users frequently request from LLMs and the tasks that are commonly studied in academic research. For example, we find that tasks such as ``design'' and ``planning'' are prevalent in user interactions but are largely neglected or different from traditional NLP benchmarks. We investigate these overlooked tasks, dissect the practical challenges they pose, and provide insights toward a roadmap to make LLMs better aligned with user needs.
CAT: Closed-loop Adversarial Training for Safe End-to-End Driving

Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling accident-prone traffic events by algorithm designs at the policy level, we investigate a Closed-loop Adversarial Training (CAT) framework for safe end-to-end driving in this paper through the lens of environment augmentation. CAT aims to continuously improve the safety of driving agents by training the agent on safety-critical scenarios that are dynamically generated over time. A novel resampling technique is developed to turn log-replay real-world driving scenarios into safety-critical ones via probabilistic factorization, where the adversarial traffic generation is modeled as the multiplication of standard motion prediction sub-problems. Consequently, CAT can launch more efficient physical attacks compared to existing safety-critical scenario generation methods and yields a significantly less computational cost in the iterative learning pipeline. We incorporate CAT into the MetaDrive simulator and validate our approach on hundreds of driving scenarios imported from real-world driving datasets. Experimental results demonstrate that CAT can effectively generate adversarial scenarios countering the agent being trained. After training, the agent can achieve superior driving safety in both log-replay and safety-critical traffic scenarios on the held-out test set. Code and data are available at https://metadriverse.github. io/cat.
Further analysis of Multivariate fractal functions

The aim of this paper is to characterize a fractal operator associated with multivariate fractal interpolation functions (FIFs) and study the several properties of this fractal operator. Further, with the help of this operator, we characterize a latest category of functions and study their approximation aspects. The basic characteristics of this multivariate fractal operator's are given in several ways in this note. The extension of this fractal operator to the $L^p$-spaces for $ p \ge 1$ are also examined. Multivariate continuous fractal functions approximation characteristics are also examined.
Enhanced Production of Λ_{b}^{0} Baryons in High-Multiplicity pp Collisions at sqrt[s]=13  TeV.

The production rate of Λ_{b}^{0} baryons relative to B^{0} mesons in pp collisions at a center-of-mass energy sqrt[s]=13 TeV is measured by the LHCb experiment. The ratio of Λ_{b}^{0} to B^{0} production cross sections shows a significant dependence on both the transverse momentum and the measured charged-particle multiplicity. At low multiplicity, the ratio measured at LHCb is consistent with the value measured in e^{+}e^{-} collisions, and increases by a factor of ∼2 with increasing multiplicity. At relatively low transverse momentum, the ratio of Λ_{b}^{0} to B^{0} cross sections is higher than what is measured in e^{+}e^{-} collisions, but converges with the e^{+}e^{-} ratio as the momentum increases. These results imply that the evolution of heavy b quarks into final-state hadrons is influenced by the density of the hadronic environment produced in the collision. Comparisons with several models and implications for the mechanisms enforcing quark confinement are discussed.
Spatial and Temporal Attention-Based Emotion Estimation on HRI-AVC Dataset

Many attempts have been made at estimating discrete emotions (calmness, anxiety, boredom, surprise, anger) and continuous emotional measures commonly used in psychology, namely ‘valence’ (The pleasantness of the emotion being displayed) and ‘arousal’ (The intensity of the emotion being displayed). Existing methods to estimate arousal and valence rely on learning from data sets, where an expert annotator labels every image frame. Access to an expert annotator is not always possible, and the annotation can also be tedious. Hence it is more practical to obtain self-reported arousal and valence values directly from the human in a real-time Human-Robot collaborative setting. Hence this paper provides an emotion data set (HRI-AVC) obtained while conducting a human-robot interaction (HRI) task. The self-reported pair of labels in this data set is associated with a set of image frames. This paper also proposes a spatial and temporal attention-based network to estimate arousal and valence from this set of image frames. The results show that an attention-based network can estimate valence and arousal on the HRI-AVC data set even when Arousal and Valence values are unavailable per frame.
Poincar\'e inequality and topological rigidity of translators and self-expanders for the mean curvature flow

We prove an abstract structure theorem for weighted manifolds supporting a weighted $f$-Poincar\'e inequality and whose ends satisfy a suitable non-integrability condition. We then study how our arguments can be used to obtain full topological control on two important classes of hypersurfaces of the Euclidean space, namely translators and self-expanders for the mean curvature flow, under either stability or curvature asumptions. As an important intermediate step in order to get our results we get the validity of a Poincar\'e inequality with respect to the natural weighted measure on any translator and we prove that any end of a translator must have infinite weighted volume. Similar tools can be obtained for properly immersed self-expanders permitting to get topological rigidity under curvature assumptions.
FLAVONOIDES DEL EXTRACTO ISOPROPANOL-AGUA DE TIBOUCHINA CILIARIS (MELASTOMATACEAE)

Compounds; Quercetin-3’-O-rhamnoside (compound 2), Kaempfero-7-O-pcumaroil have been isolated from the dry leafs of Tibouchina ciliaris, joined other compounds like: Kaempferol (compound 1) and Ellagic Acid, their structures were elucidated by RMM, HPLC-DAD , and MS analysis, and by comparison with bibliographic data. Kaempferol showed a MIC (Minimum Inhibitory Concentration) of 512 µg/mL against Escherichia coli.
Self-switching random walks on Erd\"os-R\'enyi random graphs feel the phase transition

We study random walks on Erd\"os-R\'enyi random graphs in which, every time the random walk returns to the starting point, first an edge probability is independently sampled according to a priori measure $\mu$, and then an Erd\"os-R\'enyi random graph is sampled according to that edge probability. When the edge probability $p$ does not depend on the size of the graph $n$ (dense case), we show that the proportion of time the random walk spends on different values of $p$ -- {\it occupation measure} -- converges to the a priori measure $\mu$ as $n$ goes to infinity. More interestingly, when $p=\lambda/n$ (sparse case), we show that the occupation measure converges to a limiting measure with a density that is a function of the survival probability of a Poisson branching process. This limiting measure is supported on the supercritial values for the Erd\"os-R\'enyi random graphs, showing that self-witching random walks can detect the phase transition.
Tight approximability of MAX 2-SAT and relatives, under UGC

Austrin showed that the approximation ratio $\beta\approx 0.94016567$ obtained by the MAX 2-SAT approximation algorithm of Lewin, Livnat and Zwick (LLZ) is optimal modulo the Unique Games Conjecture (UGC) and modulo a Simplicity Conjecture that states that the worst performance of the algorithm is obtained on so called simple configurations. We prove Austrin's conjecture, thereby showing the optimality of the LLZ approximation algorithm, relying only on the Unique Games Conjecture. Our proof uses a combination of analytic and computational tools. We also present new approximation algorithms for two restrictions of the MAX 2-SAT problem. For MAX HORN-$\{1,2\}$-SAT, i.e., MAX CSP$(\{x\lor y,\bar{x}\lor y,x,\bar{x}\})$, in which clauses are not allowed to contain two negated literals, we obtain an approximation ratio of $0.94615981$. For MAX CSP$(\{x\lor y,x,\bar{x}\})$, i.e., when 2-clauses are not allowed to contain negated literals, we obtain an approximation ratio of $0.95397990$. By adapting Austrin's and our arguments for the MAX 2-SAT problem we show that these two approximation ratios are also tight, modulo only the UGC conjecture. This completes a full characterization of the approximability of the MAX 2-SAT problem and its restrictions.
Accelerating the force-coupling method for hydrodynamic interactions in periodic domains

The efficient simulation of fluid-structure interactions at zero Reynolds number requires the use of fast summation techniques in order to rapidly compute the long-ranged hydrodynamic interactions between the structures. One approach for periodic domains involves utilising a compact or exponentially decaying kernel function to spread the force on the structure to a regular grid where the resulting flow and interactions can be computed efficiently using an FFT-based solver. A limitation to this approach is that the grid spacing must be chosen to resolve the kernel and thus, these methods can become inefficient when the separation between the structures is large compared to the kernel width. In this paper, we address this issue for the force-coupling method (FCM) by introducing a modified kernel that can be resolved on a much coarser grid, and subsequently correcting the resulting interactions in a pairwise fashion. The modified kernel is constructed to ensure rapid convergence to the exact hydrodynamic interactions and a positive-splitting of the associated mobility matrix. We provide a detailed computational study of the methodology and establish the optimal choice of the modified kernel width, which we show plays a similar role to the splitting parameter in Ewald summation. Finally, we perform example simulations of rod sedimentation and active filament coordination to demonstrate the performance of fast FCM in application.
Quantum Annealing Solutions for the Closest String Problem with D-Wave Systems

The Closest String Problem is an NP-complete problem which appears more commonly in bioinformatics and coding theory. Less surprisingly, classical approaches have been pursued with two prominent algorithms being the genetic algorithm and simulated annealing. Latest improvements to quantum computing devices with a specialization in optimization tasks such as DWave systems, suggest that an attempt to embed the problem in a model accepted by such systems is worthwhile. In this work, two QUBO formulations have been proposed, with one being a slight modification over the other. Subsequently, an evaluation based on a few simple test cases had been carried out on both formulations. In this regard, the D-Wave annealers have been used, while providing guidelines for optimality on certain platform-specific concerns. For evaluation purposes, a metric termed Occurrence Ratio (OR) has been defined. With minimal hyperparameter tuning, the expected solutions were obtained for every test case and the optimality was guaranteed. To address practical and implementation issues, an inherent decomposition strategy based on the possibility of having substrings has been elucidated to accommodate the restricted qubit count. Conclusively, the need for further investigation on tuning the hyperparameters is emphasized.
Parity Games on Temporal Graphs

Temporal graphs are a popular modelling mechanism for dynamic complex systems that extend ordinary graphs with discrete time. Simply put, time progresses one unit per step and the availability of edges can change with time. We consider the complexity of solving $\omega$-regular games played on temporal graphs where the edge availability is ultimately periodic and fixed a priori. We show that solving parity games on temporal graphs is decidable in PSPACE, only assuming the edge predicate itself is in PSPACE. A matching lower bound already holds for what we call punctual reachability games on static graphs, where one player wants to reach the target at a given, binary encoded, point in time. We further study syntactic restrictions that imply more efficient procedures. In particular, if the edge predicate is in $P$ and is monotonically increasing for one player and decreasing for the other, then the complexity of solving games is only polynomially increased compared to static graphs.
System identification and closed-loop control of laser hot-wire directed energy deposition using the parameter-signature-property modeling scheme

Hot-wire directed energy deposition using a laser beam (DED-LB/w) is a method of metal additive manufacturing (AM) that has benefits of high material utilization and deposition rate, but parts manufactured by DED-LB/w suffer from a substantial heat input and undesired surface finish. Hence, monitoring and controlling the process parameters and signatures during the deposition is crucial to ensure the quality of final part properties and geometries. This paper explores the dynamic modeling of the DED-LB/w process and introduces a parameter-signature-property modeling and control approach to enhance the quality of modeling and control of part properties that cannot be measured in situ. The study investigates different process parameters that influence the melt pool width (signature) and bead width (property) in single and multi-layer beads. The proposed modeling approach utilizes a parameter-signature model as F_1 and a signature-property model as F_2. Linear and nonlinear modeling approaches are compared to describe a dynamic relationship between process parameters and a process signature, the melt pool width (F_1). A fully connected artificial neural network is employed to model and predict the final part property, i.e., bead width, based on melt pool signatures (F_2). Finally, the effectiveness and usefulness of the proposed parameter-signature-property modeling is tested and verified by integrating the parameter-signature (F_1) and signature-property (F_2) models in the closed-loop control of the width of the part. Compared with the control loop with only F_1, the proposed method shows clear advantages and bears potential to be applied to control other part properties that cannot be directly measured or monitored in situ.
Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-based Detection Mechanisms

The widespread deployment of"smart"electric vehicle charging stations (EVCSs) will be a key step toward achieving green transportation. The connectivity features of smart EVCSs can be utilized to schedule EV charging operations while respecting user preferences, thus avoiding synchronous charging from a large number of customers and relieving grid congestion. However, the communication and connectivity requirements involved in smart charging raise cybersecurity concerns. In this work, we investigate charge manipulation attacks (CMAs) against EV charging, in which an attacker manipulates the information exchanged during smart charging operations. The objective of CMAs is to shift the EV aggregator's demand across different times of the day. The proposed CMAs can bypass existing protection mechanisms in EV communication protocols. We quantify the impact of CMAs on the EV aggregator's economic profit by modeling their participation in the day-ahead (DA) and real-time (RT) electricity markets. Finally, we propose an unsupervised deep learning-based mechanism to detect CMAs by monitoring the parameters involved in EV charging. We extensively analyze the attack impact and the efficiency of the proposed detection on real-world EV charging datasets. The results highlight the vulnerabilities of smart charging operations and the need for a monitoring mechanism to detect malicious CMAs.
Influence of thermal environment on heart rate variability

生理応答の一つである心拍変動が, 実際の環境を評価する指標として適用可能であるかを等温除湿時の環境を体感する被験者実験を行い検証した. 本研究では, 精神的なストレス状態や生体への負担との関係が深いとされている心拍間隔とその分散, 心拍変動波形のスペクトル解析, および心拍変動波形の相関次元の3つを評価指標として用いた. その結果, 心拍間隔と心拍間隔の分散は, 室内温度が高いほうが, また湿度も高いほうが小さい値を示し, また相関次元は, 室内温度の変化では大きな相違がみられなかったものの, 低湿度では相関次元が小さくなる傾向がみられ, 精神的なストレス状態にあるときと同様の生体反応を示すことがわかった. 心拍変動波形のスペクトル解析の結果では, 比較的快適な室内温度とされている室温26℃のとき, 湿度の低下に伴い血圧調節リズムである0. 1Hz近傍で顕著なピークが現れ, このとき交感神経が優位に活動していたことが示唆された.
Fast Power Curve Approximation for Posterior Analyses

Bayesian hypothesis testing leverages posterior probabilities, Bayes factors, or credible intervals to assess characteristics that summarize data. We propose a framework for power curve approximation with such hypothesis tests that assumes data are generated using statistical models with fixed parameters for the purposes of sample size determination. We present a fast approach to explore the sampling distribution of posterior probabilities when the conditions for the Bernstein-von Mises theorem are satisfied. We extend that approach to facilitate targeted sampling from the approximate sampling distribution of posterior probabilities for each sample size explored. These sampling distributions are used to construct power curves for various types of posterior analyses. Our resulting method for power curve approximation is orders of magnitude faster than conventional power curve estimation for Bayesian hypothesis tests. We also prove the consistency of the corresponding power estimates and sample size recommendations under certain conditions.