Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Constrained Reinforcement Learning Algorithms for Legged Locomotion

Shifting from traditional control strategies to Deep Reinforcement Learning (RL) for legged robots poses inherent challenges, especially when addressing real-world physical constraints during training. While high-fidelity simulations provide significant benefits, they often bypass these essential physical limitations. In this paper, we experiment with the Constrained Markov Decision Process (CMDP) framework instead of the conventional unconstrained RL for robotic applications. We perform a comparative study of different constrained policy optimization algorithms to identify suitable methods for practical implementation. Our robot experiments demonstrate the critical role of incorporating physical constraints, yielding successful sim-to-real transfers, and reducing operational errors on physical systems. The CMDP formulation streamlines the training process by separately handling constraints from rewards. Our findings underscore the potential of constrained RL for the effective development and deployment of learned controllers in robotics.
Line Graph Neural Networks for Link Weight Prediction

Link weight prediction is of great practical importance, since real-world networks are often weighted networks. Previous studies have mainly used shallow graph features for link weight prediction, which limits the prediction performance. In this paper, we propose a new link weight prediction algorithm, namely Line Graph Neural Networks for Link Weight Prediction (LGLWP), which learns deeper graph features through deep learning. In our algorithm, we first extract the enclosing subgraph around a target link, and then employ a weighted graph labeling algorithm to label the subgraph nodes. Next, we transform the subgraph into a line graph and apply the graph convolution neural networks to learn the node embedding in the line graph, which can represent the links in the original subgraph. Finally, the link feature vectors are put into a fully-connected neural network to predict the weight of the target link. Our algorithm directly obtain the feature vectors of the target links in the original graph, which is better than the previous methods that splice the node feature vectors for link weight prediction. Experiments results on six real datasets of various network sizes and types show that our algorithm has better prediction performance than the state-of-art methods, while it has fewer parameters and high training efficiency.
An Integer Programming Approach To Subspace Clustering With Missing Data

In the Subspace Clustering with Missing Data (SCMD) problem, we are given a collection of n partially observed d-dimensional vectors. The data points are assumed to be concentrated near a union of low-dimensional subspaces. The goal of SCMD is to cluster the vectors according to their subspace membership and recover the underlying basis, which can then be used to infer their missing entries. State-of-the-art algorithms for SCMD can fail on instances with a high proportion of missing data, full-rank data, or if the underlying subspaces are similar to each other. We propose a novel integer programming approach for SCMD. The approach is based on dynamically determining a set of candidate subspaces and optimally assigning points to selected subspaces. The problem structure is identical to the classical facility-location problem, with subspaces playing the role of facilities and data points that of customers. We propose a column-generation approach for identifying candidate subspaces combined with a Benders decomposition approach for solving the linear programming relaxation of the formulation. An empirical study demonstrates that the proposed approach can achieve better clustering accuracy than state-of-the-art methods when the data is high-rank, the percentage of missing data is high, or the subspaces are similar.
Beauty beacon: correlated strategies for the Fisher runaway process

Suppose that females choose males based on attributes that do not signal any genetic quality that is not related to the choice itself. Can being choosy confer selective advantage in this situation? We introduce correlated strategies, which means that females, when making their choice, may take into consideration external and independent random factors that are known to be observable by all. Individual-based simulation is used to show that, in this case, choosiness can emerge against the cost of over 25% when pitted against randomly mating females. Moreover, after being established in the population, it can sustain costs of over 35% . While such costs are not biologically plausible, they demonstrate unequivocally that sexual choice is a strong evolutionary force. Thus, correlated strategies are shown to be an evolutionary tool that channels randomness from the environment into genetic diversity. In addition, it turns out that a higher number of attributes in the ornament makes the choice more advantageous, which may result in a runaway complexity of sexual traits. Implications for the evolution of (female) cognitive abilities and speciation are discussed.
T5APR: Empowering Automated Program Repair across Languages through Checkpoint Ensemble

Automated program repair (APR) using deep learning techniques has become an important area of research in recent years, aiming to automatically generate bug-fixing patches that can improve software reliability and maintainability. However, most existing methods either target a single language or require high computational resources to train multilingual models. In this paper, we propose T5APR, a novel neural program repair approach that provides a unified solution for bug fixing across multiple programming languages. T5APR leverages CodeT5, a powerful pre-trained text-to-text transformer model, and adopts a checkpoint ensemble strategy to improve patch recommendation. We conduct comprehensive evaluations on six well-known benchmarks in four programming languages (Java, Python, C, JavaScript), demonstrating T5APR's competitiveness against state-of-the-art techniques. T5APR correctly fixes 1,985 bugs, including 1,442 bugs that none of the compared techniques has fixed. We further support the effectiveness of our approach by conducting detailed analyses, such as comparing the correct patch ranking among different techniques. The findings of this study demonstrate the potential of T5APR for use in real-world applications and highlight the importance of multilingual approaches in the field of APR.
Spatial‐temporal variations of evapotranspiration and runoff/precipitation ratios responding to the changing climate in the Pacific Northwest during 1921‐2006

Water resources in the Pacific Northwest (PNW) are very sensitive to climate change. There are still big knowledge gaps on how evapotranspiration (ET) varies in responding to changing temperature (T) and precipitation (P) over different zones in terms of supply and demand regime for ET. Here, we employ the Variable Infiltration Capacity (VIC) hydrologic model and a high‐resolution meteorological data set to quantify spatial and seasonal variations of ET and the runoff (R)/precipitation (P) ratio over the PNW and attribute effects of T and P. We evaluate modeled ET and R with eddy covariance measurements, upscaled regional ET, and reconstructed natural streamflow. Simulation results indicate that water‐limited (annual potential ET (PET) ≥ P) and energy‐limited zones (annual PET < P) have different responses to changing climate. In general, water‐limited zones tend to be more associated with increasing ET and decreasing R/P than do energy‐limited regions. With controlled simulation experiments, we document that trends in annual and warm‐season ET and R/P are dominantly controlled by P, while in the cool season they are mainly controlled by T. During an entire cycle of the Pacific Decadal Oscillation (1947‐2006), the PNW experienced a substantial increase in ET and a decrease in R and R/P under the trends of warming and drying. In this snowmelt‐dominated region where warming‐induced changes to the snowpack are impacting seasonal freshwater availability, decreases in R/P could further aggravate water scarcity. To reduce uncertainties, high‐resolution meteorological data sets and intensive model calibrations and evaluations against ET are required.
Toward a practical method for estimating sediment‐transport rates in gravel‐bed rivers

Estimates of sediment transport rates in gravel‐bed rivers are typically developed from formulae or from a sampling campaign. The former are notoriously inaccurate; the latter require a large effort and may still not achieve acceptable accuracy. A wide range of geomorphological problems, particularly those at the watershed scale, could be addressed more accurately if reliable local estimates of transport rate could be incorporated routinely into such studies. This requires a method for estimating transport rate with acceptable accuracy and a minimum of effort. A consistent approach to estimating transport is not just a matter of which formula or sampler to use; a suitable approach requires a consistent and reliable scaling of the water discharge and an integral description of the sediment that is both meaningful and practical. An approach proposed here, using a few observations of small transport rates to calibrate a transport formula, is likely to provide a superior combination of accuracy and effort and substantially reduces flow‐scaling error. Small transport rates can be measured effectively using pit samplers, which allow the sampling periods needed for accurate measurements. Grain size can be represented as two fractions, sand and gravel, which capture some of the important transport dynamics and effectively represent important environmental problems within a tractable methodological approach. The approach is nearly independent of the uncertainty associated with measuring slope and bed grain‐size in the field. Copyright © 2001 John Wiley & Sons, Ltd.
Data-Driven Framework for Uncovering Hidden Control Strategies in Evolutionary Analysis

We devised a data-driven framework for uncovering hidden control strategies used by an evolutionary system described by an evolutionary probability distribution. This innovative framework enables deciphering of the concealed mechanisms that contribute to the progression or mitigation of such situations as the spread of COVID-19. Novel algorithms are used to estimate the optimal control in tandem with the parameters for evolution in general dynamical systems, thereby extending the concept of model predictive control. This marks a significant departure from conventional control methods, which require knowledge of the system to manipulate its evolution and of the controller’s strategy or parameters. We use a generalized additive model, supplemented by extensive statistical testing, to identify a set of predictor covariates closely linked to the control. Using real-world COVID-19 data, we delineate the descriptive behaviors of the COVID-19 epidemics in five prefectures in Japan and nine countries. We compare these nine countries and group them on the basis of shared profiles, providing valuable insights into their pandemic responses. Our findings underscore the potential of our framework as a powerful tool for understanding and managing complex evolutionary processes.
Postfabrication Tuning of Circular Bragg Resonators for Enhanced Emitter-Cavity Coupling

Solid-state quantum emitters embedded in circular Bragg resonators are attractive due to their ability to emit quantum light with high brightness and low multiphoton probability. As for any emitter-microcavity system, fabrication imperfections limit the spatial and spectral overlap of the emitter with the cavity mode, thus limiting their coupling strength. Here, we show that an initial spectral mismatch can be corrected after device fabrication by repeated wet chemical etching steps. We demonstrate an ∼16 nm wavelength tuning for optical modes in AlGaAs resonators on oxide, leading to a 4-fold Purcell enhancement of the emission of single embedded GaAs quantum dots. Numerical calculations reproduce the observations and suggest that the achievable performance of the resonator is only marginally affected in the explored tuning range. We expect the method to be applicable also to circular Bragg resonators based on other material platforms, thus increasing the device yield of cavity-enhanced solid-state quantum emitters.
Cognitive frailty and its association with depression, anxiety and stress during the COVID‐19 pandemic among older adults in the transforming cognitive frailty into later‐life self‐sufficiency (AGELESS) study

Cognitive frailty describes the co‐occurrence of cognitive impairment and physical frailty and is classified into reversible and irreversible phenotypes. Data on the impact of COVID‐19 pandemic imposed lockdowns, locally known as the Movement Control Order (MCO), on the psychological status of cognitively frail older adults remain scarce. Therefore, this study aimed to determine the relationship between depression, anxiety, stress and cognitive frailty among older adults during the MCO.
DefectHunter: A Novel LLM-Driven Boosted-Conformer-based Code Vulnerability Detection Mechanism

One of the most pressing threats to computing systems is software vulnerabilities, which can compromise both hardware and software components. Existing methods for vulnerability detection remain suboptimal. Traditional techniques are both time-consuming and labor-intensive, while machine-learning-based approaches often underperform when applied to complex datasets, due to their inability to capture high-dimensional relationships. Previous deep-learning strategies also fall short in capturing sufficient feature information. Although self-attention mechanisms can process information over long distances, they fail to capture structural information. In this paper, we introduce DefectHunter, an innovative model for vulnerability identification that employs the Conformer mechanism. This mechanism fuses self-attention with convolutional networks to capture both local, position-wise features and global, content-based interactions. Furthermore, we optimize the self-attention mechanisms to mitigate the issue of excessive attention heads introducing extraneous noise by adjusting the denominator. We evaluated DefectHunter against ten baseline methods using six industrial and two highly complex datasets. On the QEMU dataset, DefectHunter exhibited a 20.62\% improvement in accuracy over Pongo-70B, and for the CWE-754 dataset, its accuracy was 14.64\% higher. To investigate how DefectHunter comprehends vulnerabilities, we conducted a case study, which revealed that our model effectively understands the mechanisms underlying vulnerabilities.
Hydromorphology Meets Mammal Ecology: River Morphological Quality, Recent Channel Adjustments and Otter Resilience

The need for a multidisciplinary approach for characterizing water bodies in terms of morphological, chemical and ecological quality has hastened the growth of hydromorphology as a cross‐disciplinary topic at the interface of hydrology, geomorphology and ecology. Many authors have analysed how hydroecology may affect freshwater biodiversity, although little is known about how this biodiversity might be affected by river morphological quality. We examined how well the presence of the semi‐aquatic Eurasian otter can be predicted by the rivers' morphological quality and its adjustments over the last decades. We tested the morphological quality index (MQI) methodology in 38 reaches of five rivers in southern Italy, 23 of which were positive to otter presence. In each reach, we examined 28 indicators contributing to the MQI and its 11 sub‐indices. The results showed a significant relationship between the probability of the presence of otters, MQI, and some sub‐indices. The best performing sub‐indices were related to channel adjustments and the continuity of river processes. A more detailed analysis of channel adjustments showed a detrimental effect of channel incision (>3 m) and a positive effect of narrowing, particularly where it occurred simultaneously with the development of forest in the new floodplain. The continuity of river processes has driven the migration of river banks and the development of ponds and secondary channels, likely increasing the availability of dens and resting sites and the hunting capabilities of otters. Our results stressed the importance of fluvial dynamics and sustainable adaptive river management for the habitat quality of semi‐aquatic species. Copyright © 2014 John Wiley & Sons, Ltd.
Multi‐year predictability of temperature and precipitation in multiple climate models

This paper explicitly identifies patterns in the ocean and land surface that are predictable on multi‐year time scales in multiple climate models. The patterns are identified by maximizing the Average Predictability Time (APT) of surface air temperature and precipitation in control simulations from the recently available fifth phase of the Coupled Model Intercomparison Project (CMIP5). Because the patterns are identified from control runs, the predictability arises from internal dynamics that occur in the absence of interannual variations of anthropogenic and natural forcing. The most predictable component of annual mean surface air temperature is shown to have significant predictability as long as 3–20 years, depending on model. Over land, two components of surface air temperature are verified to be significantly predictable on multi‐year time scales, with one component deriving predictability from the persistence of temperature over the oceans, and the other deriving predictability from evolving ENSO patterns. Annual mean land precipitation is shown to be significantly predictable for 2–4 years in multiple models. These results contradict the widely held belief that temperature and precipitationover land is unpredictable beyond seasonal time scales.
CATION SUBSTITUTIONS GOVERNING THE CHEMISTRY OF AMPHIBOLE IN THE SILGARÁ FORMATION METABASITES AT THE SOUTHWESTERN SANTANDER MASSIF

ABSTRACT Amphibole-bearing parageneses from the Silgara Formation metabasites at the southwestern Santander Massif record evidence of prograding metamorphism. Optical and microprobe analyses, together with thermobarometric estimates on these rocks, show that the main variation in Ca-amphibole is the simultaneous substitution of Al into the T1 site (AlIV) and Na+K into the A-site of the amphibole’s crystal structure. AlIV is strongly temperature dependant, and this dependancy masks any pressure effect. Changes in the chemical composition of Ca-amphibole grains are interpreted through coupled substitutions, and reactions with co-existing minerals during an increase in metamorphic conditions from greenschist to lower amphibolite facies, being favourable circumstances record not only the metamorphic facies reached by rocks but also the P-T conditions by which these were attained. Keywords: amphibole, Silgara, metabasites, substitution, metamorphic conditions SUSTITUCIONES CATIONICAS QUE GOBIERNAN LA QUIMICA DEL ANFIBOL EN METABASITAS DE LA FORMACION SILGARA EN LA REGION SUROCCIDENTAL DEL MACIZO DE SANTANDER RESUMEN Las paragenesis con presencia de anfibol de las rocas metabasicas de la Formacion Silgara en la region suroccidental del Macizo de Santander registran evidencia de metamorfismo progrado. Analisis opticos y de microsonda electronica, junto con calculos termobarometricos en estas rocas, muestran que la principal variacion en anfiboles calcicos es la sustitucion simultanea de Al en el sitio T1 (AlIV) y Na+K en el sitio A de la estructura cristalina del anfibol. AlIV es fuertemente dependiente de la temperatura, y esta dependencia enmascara cualquier efecto de la presion. Los cambios en la composicion quimica del anfibol calcico son interpretados a traves de sustituciones acopladas, y reacciones con los minerales con los cuales coexiste durante un aumento en las condiciones de metamorfismo desde la facies esquisto verde hasta la facies anfibolita inferior, siendo circunstancias favorables que registran no solo las facies metamorficas alcanzadas por las rocas sino tambien las condiciones de P-T por las cuales estas fueron obtenidas. Palabras claves: anfibol, Silgara, metabasitas, sustitucion, condiciones de metamorfismo
Importance-Weighted Offline Learning Done Right

We study the problem of offline policy optimization in stochastic contextual bandit problems, where the goal is to learn a near-optimal policy based on a dataset of decision data collected by a suboptimal behavior policy. Rather than making any structural assumptions on the reward function, we assume access to a given policy class and aim to compete with the best comparator policy within this class. In this setting, a standard approach is to compute importance-weighted estimators of the value of each policy, and select a policy that minimizes the estimated value up to a"pessimistic"adjustment subtracted from the estimates to reduce their random fluctuations. In this paper, we show that a simple alternative approach based on the"implicit exploration"estimator of \citet{Neu2015} yields performance guarantees that are superior in nearly all possible terms to all previous results. Most notably, we remove an extremely restrictive"uniform coverage"assumption made in all previous works. These improvements are made possible by the observation that the upper and lower tails importance-weighted estimators behave very differently from each other, and their careful control can massively improve on previous results that were all based on symmetric two-sided concentration inequalities. We also extend our results to infinite policy classes in a PAC-Bayesian fashion, and showcase the robustness of our algorithm to the choice of hyper-parameters by means of numerical simulations.
Long-lived coherences in strongly interacting spin ensembles

Periodic driving has emerged as a powerful tool to control, engineer, and characterize many-body quantum systems. However, the required pulse sequences are often complex, long, or require the ability to control the individual degrees of freedom. In this work, we study how a simple Carr-Purcell Meiboom-Gill (CPMG)-like pulse sequence can be leveraged to enhance the coherence of a large ensemble of spin qubits and serve as an important characterization tool. We implement the periodic drive on an ensemble of dense nitrogen-vacancy (NV) centers in diamond and examine the effect of pulse rotation offset as a control parameter on the dynamics. We use a single diamond sample prepared with several spots of varying NV density, which, in turn, varies the NV-NV dipolar interaction strength. Counter-intuitively, we find that rotation offsets deviating from the ideal {\pi}-pulse in the CPMG sequence (often classified as pulse errors) play a critical role in preserving coherence even at nominally zero rotation offset. The cause of the coherence preservation is an emergent effective field that scales linearly with the magnitude of the rotation offset. In addition to extending coherence, we compare the rotation offset dependence of coherence to numerical simulations to measure the disorder and dipolar contributions to the Hamiltonian to quantitatively extract the densities of the constituent spin species within the diamond.