Miracle-dz/UniFAR-FTUs huggingface dataset:
On the dearth of C-enhanced metal-poor stars in the galactic bulge

The chemical fingerprints of the first stars are retained within the photospheres of ancient unevolved metal-poor stars. A significant fraction of these stellar fossils is represented by stars known as Carbon-Enhanced Metal-Poor (CEMP), [C/Fe] > +0.7 and [Fe/H] < −2, which are likely imprinted by low-energy primordial supernovae. These CEMP stars are largely observed in the Galactic halo and ultra-faint dwarf galaxies, with values reaching $\rm [C/Fe]=+4.5$. The Galactic bulge is predicted to host the oldest stars, but it shows a striking dearth of CEMP stars with $\rm [C/Fe]\gtrsim +2.0$. Here we explore the possible reasons for this anomaly by performing a statistical analysis of the observations of metal-poor stars in combination with the predictions of ΛCDM models. We suggest that the dearth of CEMP stars with high [C/Fe] is not due to the low statistics of observed metal-poor stars but is the result of the different formation process of the bulge. N-body simulations show that the first star-forming halos which end up in the bulge are characterized by the highest star-formation rates. These rates enable the formation of rare massive first stars exploding as pair-instability supernovae (PISNe), which wash out the signature of primordial faint supernovae. We demonstrate that the mean [C/Fe] of first stars polluted environments decreases with the increasing contribution of PISNe. We conclude that the dearth of CEMP stars in the Galactic bulge indirectly probes the existence of elusive PISNe, and propose a novel method which exploits this lack to constrain the mass distribution of the first stars.
Defect detection using weakly supervised learning

In many real-world scenarios, obtaining large amounts of labeled data can be a daunting task. Weakly supervised learning techniques have gained significant attention in recent years as an alternative to traditional supervised learning, as they enable training models using only a limited amount of labeled data. In this paper, the performance of a weakly supervised classifier to its fully supervised counterpart is compared on the task of defect detection. Experiments are conducted on a dataset of images containing defects, and evaluate the two classifiers based on their accuracy, precision, and recall. Our results show that the weakly supervised classifier achieves comparable performance to the supervised classifier, while requiring significantly less labeled data.
Chaotic winds from a dying world: a one-dimensional map for evolving atmospheres

Planets which are smaller than Mercury and heated to sublimation temperatures of $\sim$2000 K lose mass catastrophically in dusty evaporative winds. The winds are observed to gust and recede largely without pattern; transit depths from the Kepler mission vary randomly from orbit to orbit by up to a factor of 10 or more. We explain how chaotic outflows may arise by constructing a map for the wind mass-loss rate as a function of time. The map is built on three statements: (1) The wind mass-loss rate scales in proportion to the surface equilibrium vapor pressure, rising exponentially with ground temperature. (2) Because the wind takes a finite time to escape the planet's gravity well, the surface mass-loss rate at any time determines the wind optical depth at a later time -- the atmosphere has hysteresis. (3) The ground temperature increases with optical depth (greenhouse effect) when the atmosphere is optically thin, and decreases with optical depth when the atmosphere is optically thick (nuclear winter). Statement (3) follows from how dust condenses in the face of intense stellar irradiation. As discussed recently, condensates initially naked before the star must be silicate-rich and iron-poor, staying cool enough for condensation by absorbing weakly in the visible and emitting strongly in the infrared. Later, when grains are numerous enough to self-shield from starlight, they may accrete more iron and reverse their visible-to-infrared opacity ratio. Depending on parameters, the map for the wind can regularly boom and bust between a greenhouse and a nuclear winter, or erupt into chaos. Lyapunov times are measured in orbital periods, the time for the wind to turn by Coriolis forces away from the planet's dayside, out of the Hill sphere.
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey

With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal data is an important yet demanding aspect of urban computing, which can enhance intelligent management decisions in various fields, including transportation, environment, climate, public safety, healthcare, and others. Traditional statistical and deep learning methods struggle to capture complex correlations in urban spatio-temporal data. To this end, Spatio-Temporal Graph Neural Networks (STGNN) have been proposed, achieving great promise in recent years. STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods. In this manuscript, we provide a comprehensive survey on recent progress on STGNN technologies for predictive learning in urban computing. Firstly, we provide a brief introduction to the construction methods of spatio-temporal graph data and the prevalent deep-learning architectures used in STGNNs. We then sort out the primary application domains and specific predictive learning tasks based on existing literature. Afterward, we scrutinize the design of STGNNs and their combination with some advanced technologies in recent years. Finally, we conclude the limitations of existing research and suggest potential directions for future work.
Paired comparisons for games of chance

We present a Bayesian rating system based on the method of paired comparisons. Our system is a flexible generalization of the well-known Glicko, and in particular can better accommodate games with significant elements of luck. Our system is currently in use in the online game Duelyst II, and in that setting outperforms Glicko2.
Adaptive Force Controller for Contact-Rich Robotic Systems using an Unscented Kalman Filter

In multi-point contact systems, precise force control is crucial for achieving stable and safe interactions between robots and their environment. Thus, we demonstrate an admittance controller with auto-tuning that can be applied for these systems. The controller's objective is to track the target wrench profiles of each contact point while considering the additional torque due to rotational friction. Our admittance controller is adaptive during online operation by using an auto-tuning method that tunes the gains of the controller while following user-specified training objectives. These objectives include facilitating controller stability, such as tracking the wrench profiles as closely as possible, ensuring control outputs are within force limits that minimize slippage, and avoiding configurations that induce kinematic singularity. We demonstrate the robustness of our controller on hardware for both manipulation and locomotion tasks using a multi-limbed climbing robot.
A Generalized Hybrid Hoare Logic

Deductive verification of hybrid systems (HSs) increasingly attracts more attention in recent years because of its power and scalability, where a powerful specification logic for HSs is the cornerstone. Often, HSs are naturally modelled by concurrent processes that communicate with each other. However, existing specification logics cannot easily handle such models. In this paper, we present a specification logic and proof system for Hybrid Communicating Sequential Processes (HCSP), that extends CSP with ordinary differential equations (ODE) and interrupts to model interactions between continuous and discrete evolution. Because it includes a rich set of algebraic operators, complicated hybrid systems can be easily modelled in an algebra-like compositional way in HCSP. Our logic can be seen as a generalization and simplification of existing hybrid Hoare logics (HHL) based on duration calculus (DC), as well as a conservative extension of existing Hoare logics for concurrent programs. Its assertion logic is the first-order theory of differential equations (FOD), together with assertions about traces recording communications, readiness, and continuous evolution. We prove continuous relative completeness of the logic w.r. t. FOD, as well as discrete relative completeness in the sense that continuous behaviour can be arbitrarily approximated by discretization. Besides, we discuss how to simplify proofs using the logic by providing a simplified assertion language and a set of sound and complete rules for differential invariants for ODEs. Finally, we implement a proof assistant for the logic in Isabelle/HOL, and apply it to verify two case studies to illustrate the power and scalability of our logic.
TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering

We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses. Our method first introduces an RGBD-aided structure from motion, which can yield filtered depth maps and refines camera poses guided by corresponding depth. Then, we adopt the neural implicit surface reconstruction method, which allows for high-quality mesh and develops a new training process for applying a regularization provided by classical multi-view stereo methods. Moreover, we apply a differentiable rendering to fine-tune incomplete texture maps and generate textures which are perceptually closer to the original scene. Our pipeline can be applied to any common objects in the real world without the need for either in-the-lab environments or accurate mask images. We demonstrate results of captured objects with complex shapes and validate our method numerically against existing 3D reconstruction and texture mapping methods.
ActiveSelfHAR: Incorporating Self-Training Into Active Learning to Improve Cross-Subject Human Activity Recognition

Deep learning (DL)-based human activity recognition (HAR) methods have shown promise in the applications of health Internet of Things (IoT) and wireless body sensor networks (BSNs). However, adapting these methods to new users in real-world scenarios is challenging due to the cross-subject issue. To solve this issue, we propose ActiveSelfHAR, a framework that combines active learning’s benefit of sparsely acquiring informative samples with actual labels and self-training’s benefit of effectively utilizing unlabeled data to adapt the HAR model to the target domain, i.e., the new users. ActiveSelfHAR consists of several key steps. First, we utilize the model from the source domain to select and label the domain invariant samples, forming a self-training set. Second, we leverage the distribution information of the self-training set to identify and annotate samples located around the class boundaries, forming a core set. Third, we augment the core set by considering the spatiotemporal relationships among the samples in the nonself-training set. Finally, we combine the self-training set and augmented core set to construct a diverse training set in the target domain and fine-tune the HAR model. Through leave-one-subject-out validation on three IMU-based data sets and one EMG-based data set, our method achieves mean HAR accuracies of 95.20%, 82.06%, 89.52%, and 92.82%, respectively. Our method demonstrates similar HAR accuracies to the upper bound, i.e., fine-tuning framework with approximately 1% labeled data of the target data set, while significantly improving data efficiency and time cost. Our work highlights the potential of implementing user-independent HAR methods into health IoT and BSN.
From RDMA to RDCA: Toward High-Speed Last Mile of Data Center Networks Using Remote Direct Cache Access

In this paper, we conduct systematic measurement studies to show that the high memory bandwidth consumption of modern distributed applications can lead to a significant drop of network throughput and a large increase of tail latency in high-speed RDMA networks.We identify its root cause as the high contention of memory bandwidth between application processes and network processes. This contention leads to frequent packet drops at the NIC of receiving hosts, which triggers the congestion control mechanism of the network and eventually results in network performance degradation. To tackle this problem, we make a key observation that given the distributed storage service, the vast majority of data it receives from the network will be eventually written to high-speed storage media (e.g., SSD) by CPU. As such, we propose to bypass host memory when processing received data to completely circumvent this performance bottleneck. In particular, we design Lamda, a novel receiver cache processing system that consumes a small amount of CPU cache to process received data from the network at line rate. We implement a prototype of Lamda and evaluate its performance extensively in a Clos-based testbed. Results show that for distributed storage applications, Lamda improves network throughput by 4.7% with zero memory bandwidth consumption on storage nodes, and improves network throughput by up 17% and 45% for large block size and small size under the memory bandwidth pressure, respectively. Lamda can also be applied to latency-sensitive HPC applications, which reduces their communication latency by 35.1%.
Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views

Neural view synthesis (NVS) is one of the most successful techniques for synthesizing free viewpoint videos, capable of achieving high fidelity from only a sparse set of captured images. This success has led to many variants of the techniques, each evaluated on a set of test views typically using image quality metrics such as PSNR, SSIM, or LPIPS. There has been a lack of research on how NVS methods perform with respect to perceived video quality. We present the first study on perceptual evaluation of NVS and NeRF variants. For this study, we collected two datasets of scenes captured in a controlled lab environment as well as in-the-wild. In contrast to existing datasets, these scenes come with reference video sequences, allowing us to test for temporal artifacts and subtle distortions that are easily overlooked when viewing only static images. We measured the quality of videos synthesized by several NVS methods in a well-controlled perceptual quality assessment experiment as well as with many existing state-of-the-art image/video quality metrics. We present a detailed analysis of the results and recommendations for dataset and metric selection for NVS evaluation.
ARO-Net: Learning Implicit Fields from Anchored Radial Observations

We introduce anchored radial observations (ARO), a novel shape encoding for learning implicit field representation of 3D shapes that is category-agnostic and generalizable amid significant shape variations. The main idea behind our work is to reason about shapes through partial observations from a set of viewpoints, called anchors. We develop a general and unified shape representation by employing a fixed set of anchors, via Fibonacci sampling, and designing a coordinate-based deep neural network to predict the occupancy value of a query point in space. Differently from prior neural implicit models that use global shape feature, our shape encoder operates on contextual, query-specific features. To predict point occupancy, locally observed shape information from the perspective of the anchors surrounding the input query point are encoded and aggregated through an attention module, before implicit decoding is performed. We demonstrate the quality and generality of our network, coined ARO-Net, on surface reconstruction from sparse point clouds, with tests on novel and unseen object categories, “one-shape” training, and comparisons to state-of-the-art neural and classical methods for reconstruction and tessellation.
Rotation, scaling, and translation invariant image watermarking based on radon transform

Geometric distortions are simple and effective attack to many existing watermarking methods. We propose a rotation, scaling and translation (RST) resilient watermarking method through embedding watermark in RST invariant derived from Radon transform and Fourier transform. Based on the translation and rotation properties of Radon transform and the translation invariant property of Fourier magnitude, the RST invariant is obtained. The watermark is embedded in middle frequency of RST invariant domain, and the watermarked image is obtained with the inverse procedure. In detection process, the image which need to be detected is first transformed to the translation and rotation invariant domain, the normalized correlation between the watermarked coefficients and watermark is calculated. The correlation is compared with a threshold in order to determine if the watermark is present. To resist scale attack, the scale factor need be estimated by exhaustive search. The experiment results show the scheme is robust to RST attack and JPEG compression.
Universal enhancement of vacancy diffusion by Mn inducing anomalous Friedel oscillation in concentrated solid-solution alloys

We present a proof-of-principle demonstration of a universal law for the element Mn, which greatly enhances vacancy diffusion through an anomalous Friedel Oscillation effect in a series of Ni-based concentrated solid-solution alloys, regardless of the type of atom involved. The antiferromagnetic element Mn possesses a unique half-filled 3d electron structure, creating split virtual bound states near the Fermi energy level and producing a large local magnetic moment after vacancy formation. The resultant electron spin oscillations reduce the number of electrons involved in charge density oscillations, destroying charge screening and lowering potential interaction at the saddle point between the vacancy and diffusing atom. This ultimately facilitates vacancy diffusion by reducing energy level variations of conduction band electrons during the diffusion process. These findings offer valuable insights into atom diffusion mechanisms and open up new avenues for manipulating defect properties through unique element design, thereby enabling the creation of high-performance alloys in a broad range of fields.
Exposing the Functionalities of Neurons for Gated Recurrent Unit Based Sequence-to-Sequence Model

The goal of this paper is to report certain scientific discoveries about a Seq2Seq model. It is known that analyzing the behavior of RNN-based models at the neuron level is considered a more challenging task than analyzing a DNN or CNN models due to their recursive mechanism in nature. This paper aims to provide neuron-level analysis to explain why a vanilla GRU-based Seq2Seq model without attention can achieve token-positioning. We found four different types of neurons: storing, counting, triggering, and outputting and further uncover the mechanism for these neurons to work together in order to produce the right token in the right position.
An Open-Source Modular Treadmill for Dynamic Force Measurement with Load Dependant Range Adjustment

Ground reaction force sensing is one of the key components of gait analysis in legged locomotion research. To measure continuous force data during locomotion, we present a novel compound instrumented treadmill design. The treadmill is 1.7m long, with a natural frequency of 170Hz and an adjustable range that can be used for humans and small robots alike. Here, we present the treadmill's design methodology and characterize it in its natural frequency, noise behavior and real-life performance. Additionally, we apply an ISO 376 norm conform calibration procedure for all spatial force directions and center of pressure position. We achieve a force accuracy of $\leq$5.6N for the ground reaction forces and $\leq$13mm in center of pressure position.
Spectrum of the Laplacian with mixed boundary conditions in a chamfered quarter of layer

We investigate the spectrum of a Laplace operator with mixed boundary conditions in an unbounded chamfered quarter of layer. The geometry depends on two parameters gathered in some vector $\kappa=(\kappa_1,\kappa_2)$ which characterizes the domain at the edges. We identify the essential spectrum and establish different results concerning the discrete spectrum with respect to $\kappa$. In particular, we show that for a given $\kappa_1>0$, there is some $h(\kappa_1)>0$ such that discrete spectrum exists for $\kappa_2\in(-\kappa_1,0)\cup(h(\kappa_1),\kappa_1)$ whereas it is empty for $\kappa_2\in[0;h(\kappa_1)]$. The proofs rely on classical arguments of spectral theory such as the max-min principle. The main originality lies rather in the delicate use of the features of the geometry.
Saihu: A Common Interface of Worst-Case Delay Analysis Tools for Time-Sensitive Networks

Time-sensitive networks, as in the context of IEEE-TSN and IETF-Detnet, require bounds on worst-case delays. Various network analysis tools compute such bounds, but these tools are based on different methods and provide different valid delay bounds. Hence, it is essential to identify the best, smallest bounds. As of today, users must implement multiple pieces of code with different syntaxes for each tool, as each tool is implemented by different groups and uses different programming languages and syntaxes. This results in a significant amount of mechanical actions from users and being error-prone. In this paper, we present Saihu, a Python interface that integrates xTFA (supports TFA), DiscoDNC (supports LUDB, PMOO, SFA), and Panco (supports PLP and ELP), the three most frequently used worst-case network analysis tools. Saihu provides a general interface that enables defining the networks in a single XML or JSON file and executing all tools simultaneously without any adjustment for individual tools. Saihu also exports analysis results into formatted reports automatically, and it offers automatic network generation for certain types of networks. Therefore, with its straightforward syntax and ease of execution, Saihu reduces the burden on users and makes it a valuable tool for anyone working with time-sensitive networks. Lastly, we modularize the package to incorporate more tools in the future.
Collaborative Multi-Object Tracking With Conformal Uncertainty Propagation

Object detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detection and uncertainty quantification are both critical for onboard modules, such as perception, prediction, and planning, to improve the safety and robustness of autonomous vehicles. Collaborative object detection (COD) has been proposed to improve detection accuracy and reduce uncertainty by leveraging the viewpoints of multiple agents. However, little attention has been paid to how to leverage the uncertainty quantification from COD to enhance MOT performance. In this letter, as the first attempt to address this challenge, we design an uncertainty propagation framework called MOT-CUP. Our framework first quantifies the uncertainty of COD through direct modeling and conformal prediction, and propagates this uncertainty information into the motion prediction and association steps. MOT-CUP is designed to work with different collaborative object detectors and baseline MOT algorithms. We evaluate MOT-CUP on V2X-Sim, a comprehensive collaborative perception dataset, and demonstrate a 2% improvement in accuracy and a $2.67\times$ reduction in uncertainty compared to the baselines, e.g. SORT and ByteTrack. In scenarios characterized by high occlusion levels, our MOT-CUP demonstrates a noteworthy 4.01% improvement in accuracy. MOT-CUP demonstrates the importance of uncertainty quantification in both COD and MOT, and provides the first attempt to improve the accuracy and reduce the uncertainty in MOT based on COD through uncertainty propagation.