Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Reconfigurable Foot Design, Multi-modal Contact Sensing, and Terrain Classification for Bipedal Locomotion

The ability of bipedal robots to adapt to diverse and unstructured terrain conditions is crucial for their deployment in real-world environments. To this end, we present a novel, bio-inspired robot foot design with stabilizing tarsal segments and a multifarious sensor suite involving acoustic, capacitive, tactile, temperature, and acceleration sensors. A real-time signal processing and terrain classification system is developed and evaluated. The sensed terrain information is used to control actuated segments of the foot, leading to improved ground contact and stability. The proposed framework highlights the potential of the sensor-integrated adaptive foot for intelligent and adaptive locomotion.
3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset

3D dense reconstruction refers to the process of obtaining the complete shape and texture features of 3D objects from 2D planar images. 3D reconstruction is an important and extensively studied problem, but it is far from being solved. This work systematically introduces classical methods of 3D dense reconstruction based on geometric and optical models, as well as methods based on deep learning. It also introduces datasets for deep learning and the performance and advantages and disadvantages demonstrated by deep learning methods on these datasets.
Otsu Thresholding Algorithm Based on Rebuilding and Dimension Reduction of the 3-Dimensional Histogram

In this work,a novel Otsu thresholding algorithm based on rebuilding and dimension reduction of the three-dimensional(3-D) was proposed with the consideration of the poor anti-noise performance in 3-D Otsu′s method due to the obviously wrong region division.Firstly,our method rebuilt the 3-D histogram based on the distribution of noise points,which reduced the noise interference.And then,we transfer the region division in 3-D histogram from eight partitions into two partitions,thus reducing the search space of threshold from 3-dimension to 1-dimension,which saved a lot of processing time and memory space.Finally,segmentation result and running time were given at the end of this paper.In addition,comparations with those of 3-D Otsu′s method,2-D decomposing approach and 2-D oblique algorithm were given as well.The results show that the presented method has better anti-noise performance and visual quality compared with the above three approaches,and lower time complexity compared with 3-D Otsu′s method.
Helmholtz-Zentrum Dresden-Rossendorf (HZDR) Unified modeling of bubbly flows in pipes, bubble columns, and airlift columns

Multiphase CFD simulation is a valuable tool in process engineering which is particularly useful to study new reactor concepts and their scale-up from laboratory to production scale. Simulations of bubbly flows up to industrial dimensions are feasible within the Eulerian twofluid framework of interpenetrating continua. However, for practical applications suitable closure models are needed which describe the physics on the scale of individual bubbles or groups thereof. The quest for such models with a broad range of applicability allowing predictive simulations is an ongoing venture. A set of closure relations for the fluid dynamics of bubbly flow has been collected that represents the best available knowledge and may serve as a baseline for further improvements and extensions. It is shown that this model is applicable to bubbly flows in different systems, namely pipes, bubble columns, and airlift columns. While some of these systems have been considered individually before, the novelty of the present work lies in their unified treatment by a single model.
The Future of ChatGPT-enabled Labor Market: A Preliminary Study

As a phenomenal large language model, ChatGPT has achieved unparalleled success in various real-world tasks and increasingly plays an important role in our daily lives and work. However, extensive concerns are also raised about the potential ethical issues, especially about whether ChatGPT-like artificial general intelligence (AGI) will replace human jobs. To this end, in this paper, we introduce a preliminary data-driven study on the future of ChatGPT-enabled labor market from the view of Human-AI Symbiosis instead of Human-AI Confrontation. To be specific, we first conduct an in-depth analysis of large-scale job posting data in BOSS Zhipin, the largest online recruitment platform in China. The results indicate that about 28% of occupations in the current labor market require ChatGPT-related skills. Furthermore, based on a large-scale occupation-centered knowledge graph, we develop a semantic information enhanced collaborative filtering algorithm to predict the future occupation-skill relations in the labor market. As a result, we find that additional 45% occupations in the future will require ChatGPT-related skills. In particular, industries related to technology, products, and operations are expected to have higher proficiency requirements for ChatGPT-related skills, while the manufacturing, services, education, and health science related industries will have lower requirements for ChatGPT-related skills.
IMAC-Sim:: A Circuit-level Simulator For In-Memory Analog Computing Architectures

With the increased attention to memristive-based in-memory analog computing (IMAC) architectures as an alternative for energy-hungry computer systems for machine learning applications, a tool that enables exploring their device- and circuit-level design space can significantly boost the research and development in this area. Thus, in this paper, we develop IMAC-Sim, a circuit-level simulator for the design space exploration of IMAC architectures. IMAC-Sim is a Python-based simulation framework, which creates the SPICE netlist of the IMAC circuit based on various device- and circuit-level hyperparameters selected by the user, and automatically evaluates the accuracy, power consumption, and latency of the developed circuit using a user-specified dataset. Moreover, IMAC-Sim simulates the interconnect parasitic resistance and capacitance in the IMAC architectures and is also equipped with horizontal and vertical partitioning techniques to surmount these reliability challenges. IMAC-Sim is a flexible tool that supports a broad range of device- and circuit-level hyperparameters. In this paper, we perform controlled experiments to exhibit some of the important capabilities of the IMAC-Sim, while the entirety of its features is available for researchers via an open-source tool at https://github.com/iCAS-Lab/IMAC-Sim.
The error and perturbation bounds for the absolute value equations with some applications

In this paper, inspired by in the pervious published work in [Math. Program. , 198 (2023), pp. 85-113] by Zamani and Hlad\'{\i}k, we focus on the error and perturbation bounds for the general absolute value equations because so far, to our knowledge, the error and perturbation bounds for the general absolute value equations are not discussed. In order to fill in this study gap, in this paper, by introducing a class of absolute value functions, we study the error bounds and perturbation bounds for two types of absolute value equations (AVEs): $Ax-B|x|=b$ and $Ax-|Bx|=b$. Some useful error bounds and perturbation bounds for the above two types of absolute value equations are presented. By applying the absolute value equations, we also obtain the error and perturbation bounds for the horizontal linear complementarity problem (HLCP). In addition, a new perturbation bound for the LCP without constraint conditions is given as well, which are weaker than the presented work in [SIAM J. Optim. , 2007, 18: 1250-1265] in a way. Besides, without limiting the matrix type, some computable estimates for the above upper bounds are given, which are sharper than some existing results under certain conditions. Some numerical examples for the AVEs from the LCP are given to show the feasibility of the perturbation bounds.
Hadronic decays of the heavy-quark-spin molecular partner of $T_{cc}^+$

Starting from the hypothesis that the $T_{cc}^+$ discovered at LHCb is a $D^{\ast+} D^0/D^{\ast 0}D^+$ hadronic molecule, we consider the partial width of its heavy quark spin partner, the $T_{cc}^{\ast +}$ as a $D^{\ast +} D^{\ast 0}$ shallow bound state, decaying into the $D^{\ast}D\pi$ final states including the contributions of the $D^{\ast} D$ and $D^{\ast} \pi$ final state interaction by using a nonrelativistic effective field theory. Because of the existence of the $T_{cc}^+$ pole, the $I=0$ $D^{\ast} D$ rescattering can give a sizeable correction up to about $40\%$ to the decay widths considering only the tree diagrams, and the $D^{\ast} \pi$ rescattering correction is about $10\%$. The four-body partial widths of the $T_{cc}^{*+}$ into $D D\pi\pi$ are also explicitly calculated, and we find that the interference effect between different intermediate $D^*D\pi$ states is small. The total width of the $T_{cc}^{*+}$ is predicted to be about 41 keV.
PrimeTime: A Finite-Time Consensus Protocol for Open Networks

In distributed problems where consensus between agents is required but average consensus is not desired, it can be necessary for each agent to know not only the data of each other agent in the network, but also the origin of each piece of data before consensus can be reached. However, transmitting large tables of data with IDs can cause the size of an agent's message to increase dramatically, while truncating down to fewer pieces of data to keep the message size small can lead to problems with the speed of achieving consensus. Also, many existing consensus protocols are not robust against agents leaving and entering the network. We introduce PrimeTime, a novel communication protocol that exploits the properties of prime numbers to quickly and efficiently share small integer data across an open network. For sufficiently small networks or small integer data, we show that messages formed by PrimeTime require fewer bits than messages formed by simply tabularizing the data and IDs to be transmitted.
An Empirical Study of Leveraging Knowledge Distillation for Compressing Multilingual Neural Machine Translation Models

Knowledge distillation (KD) is a well-known method for compressing neural models. However, works focusing on distilling knowledge from large multilingual neural machine translation (MNMT) models into smaller ones are practically nonexistent, despite the popularity and superiority of MNMT. This paper bridges this gap by presenting an empirical investigation of knowledge distillation for compressing MNMT models. We take Indic to English translation as a case study and demonstrate that commonly used language-agnostic and language-aware KD approaches yield models that are 4-5x smaller but also suffer from performance drops of up to 3.5 BLEU. To mitigate this, we then experiment with design considerations such as shallower versus deeper models, heavy parameter sharing, multistage training, and adapters. We observe that deeper compact models tend to be as good as shallower non-compact ones and that fine-tuning a distilled model on a high-quality subset slightly boosts translation quality. Overall, we conclude that compressing MNMT models via KD is challenging, indicating immense scope for further research.
Physical and Surface Properties of Comet Nuclei from Remote Observations

We summarize the collective knowledge of physical and surface properties of comet nuclei, focusing on those that are obtained from remote observations. We now have measurements or constraints on effective radius for over 200 comets, rotation periods for over 60, axial ratios and color indices for over 50, geometric albedos for over 25, and nucleus phase coefficients for over 20. The sample has approximately tripled since the publication of Comets II, with IR surveys using Spitzer and NEOWISE responsible for the bulk of the increase in effective radii measurements. Advances in coma morphology studies and long-term studies of a few prominent comets have resulted in meaningful constraints on rotation period changes in nearly a dozen comets, allowing this to be added to the range of nucleus properties studied. The first delay-Doppler radar and visible light polarimetric measurements of comet nuclei have been made since Comets II and are considered alongside the traditional methods of studying nuclei remotely. We use the results from recent in situ missions, notably Rosetta, to put the collective properties obtained by remote observations into context, emphasizing the insights gained into surface properties and the prevalence of highly elongated and/or bilobate shapes. We also explore how nucleus properties evolve, focusing on fragmentation and the likely related phenomena of outbursts and disintegration. Knowledge of these behaviors has been shaped in recent years by diverse sources: high resolution images of nucleus fragmentation and disruption events, the detection of thousands of small comets near the Sun, regular photometric monitoring of large numbers of comets throughout the solar system, and detailed imaging of the surfaces of mission targets. Finally, we explore what advances in the knowledge of the bulk nucleus properties may be enabled in coming years.
Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data

The manual processing and analysis of videos from camera traps is time-consuming and includes several steps, ranging from the filtering of falsely triggered footage to identifying and re-identifying individuals. In this study, we developed a pipeline to automatically analyze videos from camera traps to identify individuals without requiring manual interaction. This pipeline applies to animal species with uniquely identifiable fur patterns and solitary behavior, such as leopards (Panthera pardus). We assumed that the same individual was seen throughout one triggered video sequence. With this assumption, multiple images could be assigned to an individual for the initial database filling without pre-labeling. The pipeline was based on well-established components from computer vision and deep learning, particularly convolutional neural networks (CNNs) and scale-invariant feature transform (SIFT) features. We augmented this basis by implementing additional components to substitute otherwise required human interactions. Based on the similarity between frames from the video material, clusters were formed that represented individuals bypassing the open set problem of the unknown total population. The pipeline was tested on a dataset of leopard videos collected by the Pan African Programme: The Cultured Chimpanzee (PanAf) and achieved a success rate of over 83% for correct matches between previously unknown individuals. The proposed pipeline can become a valuable tool for future conservation projects based on camera trap data, reducing the work of manual analysis for individual identification, when labeled data is unavailable.
Bridging RL Theory and Practice with the Effective Horizon

Deep reinforcement learning (RL) works impressively in some environments and fails catastrophically in others. Ideally, RL theory should be able to provide an understanding of why this is, i.e. bounds predictive of practical performance. Unfortunately, current theory does not quite have this ability. We compare standard deep RL algorithms to prior sample complexity prior bounds by introducing a new dataset, BRIDGE. It consists of 155 MDPs from common deep RL benchmarks, along with their corresponding tabular representations, which enables us to exactly compute instance-dependent bounds. We find that prior bounds do not correlate well with when deep RL succeeds vs. fails, but discover a surprising property that does. When actions with the highest Q-values under the random policy also have the highest Q-values under the optimal policy, deep RL tends to succeed; when they don't, deep RL tends to fail. We generalize this property into a new complexity measure of an MDP that we call the effective horizon, which roughly corresponds to how many steps of lookahead search are needed in order to identify the next optimal action when leaf nodes are evaluated with random rollouts. Using BRIDGE, we show that the effective horizon-based bounds are more closely reflective of the empirical performance of PPO and DQN than prior sample complexity bounds across four metrics. We also show that, unlike existing bounds, the effective horizon can predict the effects of using reward shaping or a pre-trained exploration policy.
BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer

Retrieval-based language models are increasingly employed in question-answering tasks. These models search in a corpus of documents for relevant information instead of having all factual knowledge stored in its parameters, thereby enhancing efficiency, transparency, and adaptability. We develop the first Norwegian retrieval-based model by adapting the REALM framework and evaluate it on various tasks. After training, we also separate the language model, which we call the reader, from the retriever components, and show that this can be fine-tuned on a range of downstream tasks. Results show that retrieval augmented language modeling improves the reader’s performance on extractive question-answering, suggesting that this type of training improves language models’ general ability to use context and that this does not happen at the expense of other abilities such as part-of-speech tagging, dependency parsing, named entity recognition, and lemmatization. Code, trained models, and data are made publicly available.
Large Language Models Based Automatic Synthesis of Software Specifications

Software configurations play a crucial role in determining the behavior of software systems. In order to ensure safe and error-free operation, it is necessary to identify the correct configuration, along with their valid bounds and rules, which are commonly referred to as software specifications. As software systems grow in complexity and scale, the number of configurations and associated specifications required to ensure the correct operation can become large and prohibitively difficult to manipulate manually. Due to the fast pace of software development, it is often the case that correct software specifications are not thoroughly checked or validated within the software itself. Rather, they are frequently discussed and documented in a variety of external sources, including software manuals, code comments, and online discussion forums. Therefore, it is hard for the system administrator to know the correct specifications of configurations due to the lack of clarity, organization, and a centralized unified source to look at. To address this challenge, we propose SpecSyn a framework that leverages a state-of-the-art large language model to automatically synthesize software specifications from natural language sources. Our approach formulates software specification synthesis as a sequence-to-sequence learning problem and investigates the extraction of specifications from large contextual texts. This is the first work that uses a large language model for end-to-end specification synthesis from natural language texts. Empirical results demonstrate that our system outperforms prior the state-of-the-art specification synthesis tool by 21% in terms of F1 score and can find specifications from single as well as multiple sentences.
Pulse shape and voltage-dependent synchronization in spiking neuron networks

Pulse-coupled spiking neural networks are a powerful tool to gain mechanistic insights into how neurons self-organize to produce coherent collective behavior. These networks use simple spiking neuron models, such as the $\theta$-neuron or the quadratic integrate-and-fire (QIF) neuron, that replicate the essential features of real neural dynamics. Interactions between neurons are modeled with infinitely narrow pulses, or spikes, rather than the more complex dynamics of real synapses. To make these networks biologically more plausible, it has been proposed that they must also account for the finite width of the pulses, which can have a significant impact on the network dynamics. However, the derivation and interpretation of these pulses is contradictory and the impact of the pulse shape on the network dynamics is largely unexplored. Here, I take a comprehensive approach to pulse-coupling in networks of QIF and $\theta$-neurons. I argue that narrow pulses activate voltage-dependent synaptic conductances and show how to implement them in QIF neurons such that their effect can last through the phase after the spike. Using an exact low-dimensional description for networks of globally coupled spiking neurons, I prove for instantaneous interactions that collective oscillations emerge due to an effective coupling through the mean voltage. I analyze the impact of the pulse shape by means of a family of smooth pulse functions with arbitrary finite width and symmetric or asymmetric shapes. For symmetric pulses, the resulting voltage-coupling is not very effective in synchronizing neurons, but pulses that are slightly skewed to the phase after the spike readily generate collective oscillations. The results unveil a voltage-dependent spike synchronization mechanism in neural networks, which is facilitated by pulses of finite width and complementary to traditional synaptic transmission.
Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation

Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided percutaneous pelvic fracture fixation, which models the procedure at four levels of granularity -- corridor, activity, view, and frame value -- simulating the pelvic fracture fixation workflow as a Markov process to provide fully annotated training data. Using added supervision from detection of bony corridors, tools, and anatomy, we learn image representations that are fed into a transformer model to regress surgical phases at the four granularity levels. Our approach demonstrates the feasibility of X-ray-based SPR, achieving an average accuracy of 93.8% on simulated sequences and 67.57% in cadaver across all granularity levels, with up to 88% accuracy for the target corridor in real data. This work constitutes the first step toward SPR for the X-ray domain, establishing an approach to categorizing phases in X-ray-guided surgery, simulating realistic image sequences to enable machine learning model development, and demonstrating that this approach is feasible for the analysis of real procedures. As X-ray-based SPR continues to mature, it will benefit procedures in orthopedic surgery, angiography, and interventional radiology by equipping intelligent surgical systems with situational awareness in the operating room.
FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging

The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer. As has been the case with fastMRI, increasing accessibility to raw prostate MRI data will further facilitate research in MR image reconstruction and evaluation with the larger goal of improving the utility of MRI for prostate cancer detection and evaluation. The dataset is available at https://fastmri.med.nyu.edu.