Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Optimal Control Matching

Stochastic optimal control, which has the goal of driving the behavior of noisy systems, is broadly applicable in science, engineering and artificial intelligence. Our work introduces Stochastic Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for stochastic optimal control that stems from the same philosophy as the conditional score matching loss for diffusion models. That is, the control is learned via a least squares problem by trying to fit a matching vector field. The training loss, which is closely connected to the cross-entropy loss, is optimized with respect to both the control function and a family of reparameterization matrices which appear in the matching vector field. The optimization with respect to the reparameterization matrices aims at minimizing the variance of the matching vector field. Experimentally, our algorithm achieves lower error than all the existing IDO techniques for stochastic optimal control for three out of four control problems, in some cases by an order of magnitude. The key idea underlying SOCM is the path-wise reparameterization trick, a novel technique that is of independent interest, e.g., for generative modeling. Code at https://github.com/facebookresearch/SOC-matching
Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series

Multivariate time series are everywhere. Nevertheless, real-world time series data often exhibit numerous missing values, which is the time series imputation task. Although previous deep learning methods have been shown to be effective for time series imputation, they are shown to produce overconfident imputations, which might be a potentially overlooked threat to the reliability of the intelligence system. Score-based diffusion method(i.e., CSDI) is effective for the time series imputation task but computationally expensive due to the nature of the generative diffusion model framework. In this paper, we propose a non-generative time series imputation method that produces accurate imputations with inherent uncertainty and meanwhile is computationally efficient. Specifically, we incorporate deep ensembles into quantile regression with a shared model backbone and a series of quantile discrimination functions.This framework combines the merits of accurate uncertainty estimation of deep ensembles and quantile regression and above all, the shared model backbone tremendously reduces most of the computation overhead of the multiple ensembles. We examine the performance of the proposed method on two real-world datasets: air quality and health-care datasets and conduct extensive experiments to show that our method excels at making deterministic and probabilistic predictions. Compared with the score-based diffusion method: CSDI, we can obtain comparable forecasting results and is better when more data is missing. Furthermore, as a non-generative model compared with CSDI, the proposed method consumes a much smaller computation overhead, yielding much faster training speed and fewer model parameters.
Novel KLD-based Resource Allocation for Integrated Sensing and Communication

In this paper, we introduce a novel resource allocation approach for integrated sensing-communication (ISAC) using the Kullback-Leibler divergence (KLD) metric. Specifically, we consider a base-station with limited power and antenna resources serving a number of communication users and detecting multiple targets simultaneously. First, we analyze the KLD for two possible antenna deployments, which are the separated and shared deployments, then use the results to optimize the resources of the base-station through minimising the average KLD for the network while satisfying a minimum predefined KLD requirement for each user equipment (UE) and target. To this end, the optimisation is formulated and presented as a mixed integer nonlinear programming (MINLP) problem and then solved using two approaches. In the first approach, we employ a genetic algorithm, which offers remarkable performance but demands substantial computational resources; and in the second approach, we propose a rounding-based interior-point method (RIPM) that provides a more computationally-efficient alternative solution at a negligible performance loss. The results demonstrate that the KLD metric can be an effective means for optimising ISAC networks, and that both optimisation solutions presented offer superior performance compared to uniform power and antenna allocation.
PACE: A Program Analysis Framework for Continuous Performance Prediction

Software development teams establish elaborate continuous integration pipelines containing automated test cases to accelerate the development process of software. Automated tests help to verify the correctness of code modifications decreasing the response time to changing requirements. However, when the software teams do not track the performance impact of pending modifications, they may need to spend considerable time refactoring existing code. This paper presents PACE, a program analysis framework that provides continuous feedback on the performance impact of pending code updates. We design performance microbenchmarks by mapping the execution time of functional test cases given a code update. We map microbenchmarks to code stylometry features and feed them to predictors for performance predictions. Our experiments achieved significant performance in predicting code performance, outperforming current state-of-the-art by 75% on neural-represented code stylometry features.
The Formation of Bars and Warps in Rotating Halos

We investigate the effects of halo kinematics on the dynamics of stellar discs by simulating the evolution of isolated disc-halo systems from equilibrium initial conditions. Our main results come from four simulations where the initial disc is identical and the halo is either treated as a rigid potential or is live with isotropic orbits or orbits that preferentially rotate with or counter to the disc. We confirm previous results that bar formation is more vigorous in models with a live halo than a rigid one and is further enhanced when halo orbits preferentially rotate with the disc. We discuss two types of buckling events with different symmetries about the mid plane, one that occurs just as the bar is forming and the other well after the bar has been established. We also show that warps are most easily excited and maintained when the halo is counter-rotating with the disc, in agreement with theoretical predictions. Our most novel result is the discovery of a rotating halo instability, which causes the disc and halo cusp to spiral outward from the centre of mass of the system whether the halo rotates with the disc or counter to it and also occurs in a disc bulge halo system that does not form a bar. We provide a heuristic linear model that captures the essential dynamics of the instability.
Generalized Fej\'er monotone sequences and their finitary content

We provide quantitative and abstract strong convergence results for sequences from a compact metric space satisfying a certain form of generalized Fej\'er monotonicity where (1) the metric can be replaced by a much more general type of function measuring distances (including, in particular, certain Bregman distances), (2) these distance functions are allowed to vary along the iteration and (3) full Fej\'er monotonicity is relaxed to a certain partial variant. To that end, the paper provides explicit and effective rates of metastability and even rates of convergence for such sequences, the latter under a regularity assumption that generalizes the notion of metric regularity introduced by Kohlenbach, L\'opez-Acedo and Nicolae, itself an abstract generalization of many regularity notions from the literature of nonlinear analysis. In the second part of the paper, we apply the abstract quantitative results established in the first part to two algorithms: one algorithm for approximating zeros of maximally monotone and maximally $\rho$-comonotone operators in Hilbert spaces (in the sense of Combettes and Pennanen as well as Bauschke, Moursi and Wang) that incorporates inertia terms every other term and another algorithm for approximating zeros of monotone operators in Banach spaces (in the sense of Browder) that is only Fej\'er monotone w.r. t. a certain Bregman distance.
A Linear-time Simulation of Deterministic d-Limited Automata

A $d$-limited automaton is a Turing machine that uses only the cells with the input word (and end-markers) and rewrites symbols only in the first $d$ visits. This model was introduced by T. Hibbard in 1967 and he showed that $d$-limited automata recognize context-free languages for each $d \geq 2$. He also proved that languages recognizable by deterministic $d$-limited automata form a hierarchy and it was shown later by Pighizzini and Pisoni that it begins with deterministic context-free languages (DCFLs) (for $d=2$). As well-known, DCFLs are widely used in practice, especially in compilers since they are linear-time recognizable and have the corresponding CF-grammars subclass (LR$(1)$-grammars). In this paper we present a linear time recognition algorithm for deterministic $d$-limited automata (in the RAM model) which opens an opportunity for their possible practical applications. We also generalize this algorithm to deterministic $d(n)$-limited automata: the extension of deterministic $d$-limited automata, where $d$ is not a constant, but a function depending on the input length $n$.
Numerical modelling of pressure solution deformation at axisymmetric asperities under normal load

Abstract We developed a numerical model of compression of asperities by pressure solution (PS). The dissolution rate along the contact was determined by (1) computing the normal stress distribution from the present shape of the asperities, and (2) solving the diffusion equation inside the fluid-saturated solid–solid interface, including local dissolution source terms corresponding to the stress field previously determined. The change in shape of the asperities during an infinitesimal time interval can then be calculated and the entire procedure repeated as many times as desired. We simulated PS compaction of axisymmetric asperities with different sizes and shapes under various temperatures and loads, and using different values of the interface diffusion coefficient. Our results show that as the contact flattens and grows during PS, the initial elastic deformation is partially relaxed and the stress transferred from the contact centre to the edge. Transient stress release remains significant during an extended period and could strongly influence the interpretation of laboratory experiments. In our model, dissolution and interface diffusion are not sequentially combined as is usually assumed, making it impossible to identify a single rate-limiting process. The convergence rate of the asperities was approximately proportional to the mean effective stress at the contact, and depended in a complex way on the contact radius, but was not sensitive to the asperity size. We also estimated the conditions under which undercutting at the contact edge is triggered.
RobustCalib: Robust Lidar-Camera Extrinsic Calibration with Consistency Learning

Current traditional methods for LiDAR-camera extrinsics estimation depend on offline targets and human efforts, while learning-based approaches resort to iterative refinement for calibration results, posing constraints on their generalization and application in on-board systems. In this paper, we propose a novel approach to address the extrinsic calibration problem in a robust, automatic, and single-shot manner. Instead of directly optimizing extrinsics, we leverage the consistency learning between LiDAR and camera to implement implicit re-calibartion. Specially, we introduce an appearance-consistency loss and a geometric-consistency loss to minimizing the inconsitency between the attrbutes (e.g., intensity and depth) of projected LiDAR points and the predicted ones. This design not only enhances adaptability to various scenarios but also enables a simple and efficient formulation during inference. We conduct comprehensive experiments on different datasets, and the results demonstrate that our method achieves accurate and robust performance. To promote further research and development in this area, we will release our model and code.
QAFE-Net: Quality Assessment of Facial Expressions with Landmark Heatmaps

Facial expression recognition (FER) methods have made great inroads in categorising moods and feelings in humans. Beyond FER, pain estimation methods assess levels of intensity in pain expressions, however assessing the quality of all facial expressions is of critical value in health-related applications. In this work, we address the quality of five different facial expressions in patients affected by Parkinson's disease. We propose a novel landmark-guided approach, QAFE-Net, that combines temporal landmark heatmaps with RGB data to capture small facial muscle movements that are encoded and mapped to severity scores. The proposed approach is evaluated on a new Parkinson's Disease Facial Expression dataset (PFED5), as well as on the pain estimation benchmark, the UNBC-McMaster Shoulder Pain Expression Archive Database. Our comparative experiments demonstrate that the proposed method outperforms SOTA action quality assessment works on PFED5 and achieves lower mean absolute error than the SOTA pain estimation methods on UNBC-McMaster. Our code and the new PFED5 dataset are available at https://github.com/shuchaoduan/QAFE-Net.
Adaptive capacity of the oil sardine Sardinella longiceps inthe new distributional area with reference to food type

The oil sardine, Sardinella longiceps Val. , is a major neretic pelagic fishery resource of India. For centuries, there has been a massive fishery for the oil sardine along the southwest coast of India. An average catch of 2.5 lakh tons has been taken annually during the current decade along the Indian coast, and, the oil sardine landings during 2009 was 3,92,486 tones. It is a highly fluctuating fishery and the contribution of oil sardine to the annual marine fish landings in India ranged from 1 to 33%.
Pointer Networks Trained Better via Evolutionary Algorithms

Pointer Network (PtrNet) is a specific neural network for solving Combinatorial Optimization Problems (COPs). While PtrNets offer real-time feed-forward inference for complex COPs instances, its quality of the results tends to be less satisfactory. One possible reason is that such issue suffers from the lack of global search ability of the gradient descent, which is frequently employed in traditional PtrNet training methods including both supervised learning and reinforcement learning. To improve the performance of PtrNet, this paper delves deeply into the advantages of training PtrNet with Evolutionary Algorithms (EAs), which have been widely acknowledged for not easily getting trapped by local optima. Extensive empirical studies based on the Travelling Salesman Problem (TSP) have been conducted. Results demonstrate that PtrNet trained with EA can consistently perform much better inference results than eight state-of-the-art methods on various problem scales. Compared with gradient descent based PtrNet training methods, EA achieves up to 30.21\% improvement in quality of the solution with the same computational time. With this advantage, this paper is able to at the first time report the results of solving 1000-dimensional TSPs by training a PtrNet on the same dimensionality, which strongly suggests that scaling up the training instances is in need to improve the performance of PtrNet on solving higher-dimensional COPs.
Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods

Light curves produced by wide-field exoplanet transit surveys such as CoRoT, Kepler, and the Transiting Exoplanet Survey Satellite are affected by sensor-wide systematic noise, which is correlated both spatiotemporally and with other instrumental parameters such as the photometric magnitude. Robust and effective systematics mitigation is necessary to achieve the level of photometric accuracy required to detect exoplanet transits and to faithfully recover other forms of intrinsic astrophysical variability. We demonstrate the feasibility of a new exploratory algorithm to remove spatially correlated systematic noise and detrend light curves obtained from wide-field transit surveys. This spatial systematics algorithm is data-driven and fits a low-rank linear model for the systematics conditioned on a total-variation spatial constraint. The total-variation constraint models spatial systematic structure across the sensor on a foundational level. The fit is performed using gradient descent applied to, a variable reduced least-squares penalty and a modified form of total-variation prior; both the systematics basis vectors and their weighting coefficients are iteratively varied. The algorithm was numerically evaluated against a reference principal component analysis, using both signal injection on a selected Kepler dataset, as well as full simulations within the same Kepler coordinate framework. We develop our algorithm to reduce the overfitting of astrophysical variability over longer signal timescales (days) while performing comparably relative to the reference method for exoplanet transit timescales. The algorithm performance and application are assessed, and future development is outlined.
Robust Resource Partitioning Approach for ARINC 653 RTOS

Modern airborne operating systems implement the concept of robust time and resource partitioning imposed by the standards for aerospace and airborne-embedded software systems, such as ARINC 653. While these standards do provide a considerable amount of design choices in regards to resource partitioning on the architectural and API levels, such as isolated memory spaces between the application partitions, predefined resource configuration, and unidirectional ports with limited queue and message sizes for inter-partition communication, they do not specify how an operating system should implement them in software. Furthermore, they often tend to set the minimal level of the required guarantees, for example, in terms of memory permissions, and disregard the hardware state of the art, which presently can provide considerably stronger guarantees at no extra cost. In the paper we present an architecture of robust resource partitioning for ARINC 653 real-time operating systems based on completely static MMU configuration. The architecture was implemented on different types of airborne hardware, including platforms with TLB-based and page table-based MMU. Key benefits of the proposed approach include minimised run-time overhead and simpler verification of the memory subsystem.
Dimorphinites (Ammonoidea, Jurassic, Upper Bajocian) in the Precordillera of Northern Chile

Abstract A new upper Bajocian ammonite assemblage containing the morphoceratids Dimorphinites dimorphus (d'Orbigny) and Vigoriceras defrancei (d'Orbigny) is reported from the circum-Pacific area. These ammonites were found at the top of the Torcazas Formation, in the Quebrada San Pedro area, Precordillera of northern Chile. Taphonomic, systematic, and paleobiogeographic data confirm these dimorphs were part of indigenous populations within the Tarapaca Basin, belonging to the same demic biospecies: Dimorphinites defrancei (d'Orbigny). The West Tethyan and East-Pacific distribution of D. defrancei corroborates the availability of the migratory seaway, the so-called Hispanic Corridor, between the western Tethys and the eastern Pacific Ocean during the upper Bajocian Parkinsoni Zone. The discovery of this upper Bajocian Dimorphinites assemblage provides a new biochronostratigraphic horizon in the Tarapaca Basin and an interoceanic correlation point for the latest Bajocian.
Cohomology of knotted semimetals in three dimensions

We extend the topological classification scheme of Weyl semimetals via cohomology and the Mayer-Vietoris sequence to account for nodal line semimetals with space-time inversion symmetry. These are semimetals where bands meet generally in 1-dimensional submanifolds, which can generally be knots in $\T^3$. These nodal loops have two charges, the quantized Berry phase and the $\Z_2$-monopole charge, the second related to linking numbers of nodal knots between bands. We provide a manifestly topological proof of the Weyl charge cancellation condition for the $\Z_2$ monopole charge, which is known to be the second Stiefel-Whitney class of a tubular neighbourhood surrounding a Weyl submanifold via the Mayer-Vietoris sequence.
End-to-End Speech-to-Text Translation: A Survey

Speech-to-text translation pertains to the task of converting speech signals in a language to text in another language. It finds its application in various domains, such as hands-free communication, dictation, video lecture transcription, and translation, to name a few. Automatic Speech Recognition (ASR), as well as Machine Translation(MT) models, play crucial roles in traditional ST translation, enabling the conversion of spoken language in its original form to written text and facilitating seamless cross-lingual communication. ASR recognizes spoken words, while MT translates the transcribed text into the target language. Such disintegrated models suffer from cascaded error propagation and high resource and training costs. As a result, researchers have been exploring end-to-end (E2E) models for ST translation. However, to our knowledge, there is no comprehensive review of existing works on E2E ST. The present survey, therefore, discusses the work in this direction. Our attempt has been to provide a comprehensive review of models employed, metrics, and datasets used for ST tasks, providing challenges and future research direction with new insights. We believe this review will be helpful to researchers working on various applications of ST models.
Geomagnetic coordinates for the entire Earth

During a recent investigation it was found necessary to construct a system of geomagnetic coordinates covering the entire Earth. As these may be found useful to those engaged in terrestrial-magnetic, cosmic-ray, and other geophysical researches for finding the geomagnetic coordinates of various places the nomographs prepared are reproduced herewith. Many magnetic phenomena are frequently found to have a relatively simple distribution when consideration is given to the location of the observing stations relative to the pole of the first-degree harmonics of the Earth's general magnetic field. A coordinate-system having this pole as its origin is called a system of geomagnetic coordinates. The term magnetic coordinates is sometimes used to denote this system but as magnetic coordinates are sometimes considered as deducible for a point from the magnetic elements at that point the former term avoids ambiguity in considering those based upon the general field. Thus, the magnetic meridian at a point is defined by the declination at that point, the magnetic latitude by the inclination—the tangent of the magnetic latitude being half the tangent of the inclination—while the geomagnetic meridian is the portion of the great circle joining the point to the pole of the first-degree harmonics and the geomagnetic latitude is the complement of the angular distance along that circle to the pole.