Miracle-dz/UniFAR-FTUs huggingface dataset:
Somatotopic disruption of the functional connectivity of the primary sensorimotor cortex in complex regional pain syndrome type 1

In complex regional pain syndrome (CRPS), the representation area of the affected limb in the primary sensorimotor cortex (SM1) reacts abnormally during sensory stimulation and motor actions. We recorded 3T functional magnetic resonance imaging resting‐state data from 17 upper‐limb CRPS type 1 patients and 19 healthy control subjects to identify alterations of patients' SM1 function during spontaneous pain and to find out how the spatial distribution of these alterations were related to peripheral symptoms. Seed‐based correlations and independent component analyses indicated that patients' upper‐limb SM1 representation areas display (i) reduced interhemispheric connectivity, associated with the combined effect of intensity and spatial extent of limb pain, (ii) increased connectivity with the right anterior insula that positively correlated with the duration of CRPS, (iii) increased connectivity with periaqueductal gray matter, and (iv) disengagement from the other parts of the SM1 network. These findings, now reported for the first time in CRPS, parallel the alterations found in patients suffering from other chronic pain conditions or from limb denervation; they also agree with findings in healthy persons who are exposed to experimental pain or have used their limbs asymmetrically. Our results suggest that CRPS is associated with a sustained and somatotopically specific alteration of SM1 function, that has correspondence to the spatial distribution of the peripheral manifestations and to the duration of the syndrome.
Inner shelf intrusions of offshore water in an upwelling system affect coastal connectivity

Poleward warm water flows around Point Conception are an important transport mechanism linking biogeographic provinces along central California. These flows are initiated by relaxation of upwelling winds; the intensity and duration of upwelling is an important driver of how far north warm water penetrates against the prevailing surface currents. In this paper we present evidence of offshore surface water intrusions to the inner shelf, 130 km north of Point Conception, near Cambria, California. Satellite observations show that the intrusions originate as eddies generated offshore along the upwelling front. These eddies may form as submesoscale instabilities or by interaction of upwelling centers with offshore waters. The intrusions move southern waters to the central California inner shelf further northward than is typical for a coastal relaxation plume, and therefore, inner shelf connectivity and ecology may be governed over timescales and distances longer than those set by the intermittent relaxation of upwelling winds.
Characteristics of patients with myofascial pain syndrome of the low back

Objective: Identify characteristics of patients with myofascial pain syndrome (MPS) of the low back. Methods: Twenty-five subjects with myofascial trigger point(s) [MTrP(s)] on the low back participated in this cross-sectional study. The location, number and type (active or latent) of MTrPs were verified by ultrasound; additionally, data on pain pressure threshold, physical function, quality of life, disability, pain catastrophizing, pain self-efficacy, kinesiophobia, emotional health, exercise motivation and pain were collected. Descriptive statistics, Chi-square, one-way ANOVAs and factorial ANCOVA were used to achieve study objectives. Results: No statistically significant differences in variables were found between types of MTrPs: Group 1 (Active, n=13), 2 (Latent, n=5) 3 (Atypical, no twitching but with spontaneous pain, n=2) and 4 (Atypical, no twitching and no spontaneous pain, n=5) except the number of MTrPs, current pain, and worst pain (p=.01-.001). There were interaction effects between spontaneous pain and twitching response on reports of physical function, current pain level, and worst pain level (p=.04-.002). Participants in Group 3 reported lower levels of physical function, higher levels of current pain, and higher levels of worst pain compared to those in Group 4. Participants in Group 1 and 2 had similar levels of physical function, current pain and worst pain. Discussion: Number of MTrPs is most closely associated with the level of pain. Spontaneous pain report seems to be a decisive factor associated with poor physical function; however, twitching response is not.
Demonstration of chronometric leveling using transportable optical clocks beyond laser coherence limit

Optical clock network requires the establishment of optical frequency transmission link between multiple optical clocks, utilizing narrow linewidth lasers. Despite achieving link noise levels of 10${^{-20}}$, the final accuracy is limited by the phase noise of the clock laser. Correlation spectroscopy is developed to transmit frequency information between two optical clocks directly, enabling optical clock comparison beyond the phase noise limit of clock lasers, and significantly enhancing the measurement accuracy or shorten the measurement time. In this letter, two compact transportable ${^{40}}$Ca${^+}$ clocks are employed to accomplish the correlation spectroscopy comparison, demonstrating an 10 cm level measurement accuracy of chronometric leveling using a mediocre clock laser with linewidth of 200 Hz. The relative frequency instability reaches $6.0\times10{^{-15}}/\sqrt{\tau/s}$, which is about 20 times better than the result with Rabi spectroscopy using the same clock laser. This research greatly reduces the harsh requirements on the performance of the clock laser, so that an ordinary stable-laser can also be employed in the construction of optical clock network, which is essential for the field applications, especially for the chronometric leveling.
The frictional strength of granular fault gouge: application of theory to the mechanics of low-angle normal faults

Abstract There is a persistent body of literature that suggests low-angle normal faults (LANF) form and slip seismically; if true, the effective friction coefficient is much lower (<0.3) than that found in laboratory tests of rock friction (c. 0.8) and in low-displacement faults that lack well-developed fault cores. This paper summarizes and discusses the mechanisms proposed to explain the low apparent friction of crustal-scale faults with low resolved shear stresses. Emphasis is placed on differentiating static weakening mechanisms, operating at strain rates c. 10−12 s−1–10−15 s−1, from dynamic weakening mechanisms, operating at strain rates >10−1 s−1. Previous published explanations for low fault friction do not appear to meet the key requirements of (i) reducing both static and dynamic frictional strength of LANF and (ii) operating only along crustal-scale faults. Fault rock assemblages in quartzo-feldspathic continental crust reveal that grain size reduction, or comminution, plays a fundamental role in fault zone development. As a fault accrues displacement, a fault core forms that contains granular material. We postulate that dynamic rock fragmentation occurs during the shearing of confined granular material; dynamic fragmentation is a volume-dependent mechanism responsible for reducing the static and dynamic frictional strengths of faults.
Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction

Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due to their resource bottlenecks. Recent research efforts have focused on model-heterogeneous FL, by extracting reduced-size models from the global model and applying them to local clients accordingly. Despite the empirical success, general theoretical guarantees of convergence on this method remain an open question. This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time. In particular, we prove that under certain sufficient conditions and for both IID and non-IID data, these algorithms converge to a stationary point of standard FL for general smooth cost functions. Moreover, we introduce the concept of minimum coverage index, together with model reduction noise, which will determine the convergence of heterogeneous federated learning, and therefore we advocate for a holistic approach that considers both factors to enhance the efficiency of heterogeneous federated learning.
Geometry of unimodular systems

A collection of vectors in a real vector space is called a unimodular system if any of its maximal linearly independent subsets generates the same free abelian group. This notion is closely connected with totally unimodular matrices: rows or columns of a totally unimodular matrix form a unimodular system and the matrix of coefficients of expansions of all vectors of a unimodular system with respect to its maximal linearly independent subset is totally unimodular. In this paper we show that a unimodular system defines the following geometric data: a Euclidean space, an integral lattice in it, and a reflexive lattice zonotope. The discriminant of the lattice is equal to the number of maximal linearly independent subsystems, and we call this number the complexity of the unimodular system. For a unimodular system $\Omega $ we also define the Gale dual unimodular system $\Omega ^{\bot}$ which has the same complexity. These notions may be illustrated by the well-known graphic and cographic unimodular systems of a graph. Both graphic and cographic unimodular systems have the same complexity which is equal to the complexity of the graph. For graphs without loops and bridges the graphic and the cographic unimodular systems are Gale dual to each other. We describe this geometric data for certain examples: for the graphic and the cographic unimodular systems of a generalized theta-graph, consisting of two vertices connected by $N$ edges, for the cographic system of the complete graph $K_N$, and for the famous Bixby-Seymour unimodular system, which is neither graphic nor cographic.
Chiral Majorana hinge modes on a curved surface with magnetic impurities

Chiral Majorana one-dimensional modes have been proposed as they key component for topological quantum computing. In this study, we explore their potential realization as hinge modes in higher-order topological superconductors. To create such phases, we engineer a sign-changing, time-reversal symmetry-breaking mass term through an ensemble of magnetic impurities on the surface of a sphere. The magnetization of this ensemble arises from the competition between the external magnetic field and the Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction among the impurities, mediated by the surface Majorana modes. We determine the magnetic phase diagram and identify the optimal magnetic field to minimize orbital effects and induce a sign changing mass term. This term opens a gap in the surface spectrum, resulting in a gapless one-dimensional chiral Majorana mode along the nodal line of the mass term, thereby implementing a second-order topological superconductor.
Dont Add, dont Miss: Effective Content Preserving Generation from Pre-Selected Text Spans

The recently introduced Controlled Text Reduction (CTR) task isolates the text generation step within typical summarization-style tasks. It does so by challenging models to generate coherent text conforming to pre-selected content within the input text (``highlights''). This framing enables increased modularity in summarization-like tasks, allowing to couple a single CTR model with various content-selection setups and modules. However, there are currently no reliable CTR models, while the performance of the existing baseline for the task is mediocre, falling short of practical utility. Here, we address this gap by introducing a high-quality, open-source CTR model that tackles two prior key limitations: inadequate enforcement of the content-preservation constraint, and suboptimal silver training data. Addressing these, we amplify the content-preservation constraint in both training, via RL, and inference, via a controlled decoding strategy. Further, we substantially improve the silver training data quality via GPT-4 distillation. Overall, pairing the distilled dataset with the highlight-adherence strategies yields marked gains over the current baseline, of up to 30 ROUGE-L points, providing a reliable CTR model for downstream use.
A Completeness Theorem for Probabilistic Regular Expressions

We introduce Probabilistic Regular Expressions (PRE), a probabilistic analogue of regular expressions denoting probabilistic languages in which every word is assigned a probability of being generated. We present and prove the completeness of an inference system for reasoning about probabilistic language equivalence of PRE based on Salomaa's axiomatisation of Kleene Algebra.
Targeted computation of nonlocal closure operators via an adjoint-based macroscopic forcing method

Reynolds-averaged Navier--Stokes (RANS) closure must be sensitive to the flow physics, including nonlocality and anisotropy of the effective eddy viscosity. Recent approaches used forced direct numerical simulations to probe these effects, including the macroscopic forcing method (MFM) of Mani and Park ($\textit{Phys. Rev. Fluids}$ $\textbf{6}$, 054607 (2021)) and the Green's function approach of Hamba ($\textit{Phys. Fluids}$ $\textbf{17}$, 115102 (2005)). The resulting nonlocal and anisotropic eddy viscosities are exact and relate Reynolds stresses to mean velocity gradients at all locations. They can be used to inform RANS models of the sensitivity to the mean velocity gradient and the suitability of local and isotropic approximations. However, these brute-force approaches are expensive. They force the mean velocity gradient at each point in the averaged space and measure the Reynolds stress response, requiring a separate simulation for each mean velocity gradient location. Thus, computing the eddy viscosity requires as many simulations as degrees of freedom in the averaged space, which can be cost-prohibitive for problems with many degrees of freedom. In this work, we develop an adjoint-based MFM to obtain the eddy viscosity at a given Reynolds stress location using a single simulation. This approach recovers the Reynolds stress dependence at a location of interest, such as a separation point or near a wall, on the mean velocity gradient at all locations. We demonstrate using adjoint MFM to compute the eddy viscosity for a specified wall-normal location in an incompressible turbulent channel flow using one simulation. In contrast, a brute-force approach for the same problem requires $N=144$ simulations (the number of grid points in the non-averaged coordinate direction). We show that a local approximation for the eddy viscosity would have been inappropriate.
An Efficient Resilient MPC Scheme via Constraint Tightening against Cyberattacks: Application to Vehicle Cruise Control

We propose a novel framework for designing a resilient Model Predictive Control (MPC) targeting uncertain linear systems under cyber attack. Assuming a periodic attack scenario, we model the system under Denial of Service (DoS) attack, also with measurement noise, as an uncertain linear system with parametric and additive uncertainty. To detect anomalies, we employ a Kalman filter-based approach. Then, through our observations of the intensity of the launched attack, we determine a range of possible values for the system matrices, as well as establish bounds of the additive uncertainty for the equivalent uncertain system. Leveraging a recent constraint tightening robust MPC method, we present an optimization-based resilient algorithm. Accordingly, we compute the uncertainty bounds and corresponding constraints offline for various attack magnitudes. Then, this data can be used efficiently in the MPC computations online. We demonstrate the effectiveness of the developed framework on the Adaptive Cruise Control (ACC) problem.
A Two-Stage 2D Channel Extrapolation Scheme for TDD 5G NR Systems

Recently, channel extrapolation has been widely investigated in frequency division duplex (FDD) massive MIMO systems. However, in time division duplex (TDD) fifth generation (5G) new radio (NR) systems, the channel extrapolation problem also arises due to the hopping uplink pilot pattern, which has not been fully researched yet. This paper addresses this gap by formulating a channel extrapolation problem in TDD massive MIMO-OFDM systems for 5G NR, incorporating imperfection factors. A novel two-stage two-dimensional (2D) channel extrapolation scheme in both frequency and time domain is proposed, designed to mitigate the negative effects of imperfection factors and ensure high-accuracy channel estimation. Specifically, in the channel estimation stage, we propose a novel multi-band and multi-timeslot based high-resolution parameter estimation algorithm to achieve 2D channel extrapolation in the presence of imperfection factors. Then, to avoid repeated multi-timeslot based channel estimation, a channel tracking stage is designed during the subsequent time instants, in which a sparse Markov channel model is formulated to capture the dynamic sparsity of massive MIMO-OFDM channels under the influence of imperfection factors. Next, an expectation-maximization (EM) based compressive channel tracking algorithm is designed to jointly estimate unknown imperfection and channel parameters by exploiting the high-resolution prior information of the delay/angle parameters from the previous timeslots. Simulation results underscore the superior performance of our proposed channel extrapolation scheme over baselines.
End-to-end Online Speaker Diarization with Target Speaker Tracking

This paper proposes an online target speaker voice activity detection system for speaker diarization tasks, which does not require a priori knowledge from the clustering-based diarization system to obtain the target speaker embeddings. By adapting the conventional target speaker voice activity detection for real-time operation, this framework can identify speaker activities using self-generated embeddings, resulting in consistent performance without permutation inconsistencies in the inference phase. During the inference process, we employ a front-end model to extract the frame-level speaker embeddings for each coming block of a signal. Next, we predict the detection state of each speaker based on these frame-level speaker embeddings and the previously estimated target speaker embedding. Then, the target speaker embeddings are updated by aggregating these frame-level speaker embeddings according to the predictions in the current block. Our model predicts the results for each block and updates the target speakers' embeddings until reaching the end of the signal. Experimental results show that the proposed method outperforms the offline clustering-based diarization system on the DIHARD III and AliMeeting datasets. The proposed method is further extended to multi-channel data, which achieves similar performance with the state-of-the-art offline diarization systems.
A Critical Review of Large Language Model on Software Engineering: An Example from ChatGPT and Automated Program Repair

Large Language Models (LLMs) have been gaining increasing attention and demonstrated promising performance across a variety of Software Engineering (SE) tasks, such as Automated Program Repair (APR), code summarization, and code completion. For example, ChatGPT, the latest black-box LLM, has been investigated by numerous recent research studies and has shown impressive performance in various tasks. However, there exists a potential risk of data leakage since these LLMs are usually close-sourced with unknown specific training details, e.g., pre-training datasets. In this paper, we seek to review the bug-fixing capabilities of ChatGPT on a clean APR benchmark with different research objectives. We first introduce {\benchmark}, a new benchmark with buggy and the corresponding fixed programs from competitive programming problems starting from 2023, after the training cutoff point of ChatGPT. The results on {\benchmark} show that ChatGPT is able to fix 109 out of 151 buggy programs using the basic prompt within 35 independent rounds, outperforming state-of-the-art LLMs CodeT5 and PLBART by 27.5\% and 62.4\% prediction accuracy. We also investigate the impact of three types of prompts, i.e., problem description, error feedback, and bug localization, leading to additional 34 fixed bugs. Besides, we provide additional discussion from the interactive nature of ChatGPT to illustrate the capacity of a dialog-based repair workflow with 9 additional fixed bugs. Inspired by the findings, we further pinpoint various challenges and opportunities for advanced SE study equipped with such LLMs (e.g.,~ChatGPT) in the near future. More importantly, our work calls for more research on the reevaluation of the achievements obtained by existing black-box LLMs across various SE tasks, not limited to ChatGPT on APR.
Welfare Diplomacy: Benchmarking Language Model Cooperation

The growing capabilities and increasingly widespread deployment of AI systems necessitate robust benchmarks for measuring their cooperative capabilities. Unfortunately, most multi-agent benchmarks are either zero-sum or purely cooperative, providing limited opportunities for such measurements. We introduce a general-sum variant of the zero-sum board game Diplomacy -- called Welfare Diplomacy -- in which players must balance investing in military conquest and domestic welfare. We argue that Welfare Diplomacy facilitates both a clearer assessment of and stronger training incentives for cooperative capabilities. Our contributions are: (1) proposing the Welfare Diplomacy rules and implementing them via an open-source Diplomacy engine; (2) constructing baseline agents using zero-shot prompted language models; and (3) conducting experiments where we find that baselines using state-of-the-art models attain high social welfare but are exploitable. Our work aims to promote societal safety by aiding researchers in developing and assessing multi-agent AI systems. Code to evaluate Welfare Diplomacy and reproduce our experiments is available at https://github.com/mukobi/welfare-diplomacy.