Miracle-dz/UniFAR-FTUs huggingface dataset:
EMRI_MC: A GPU-based code for Bayesian inference of EMRI waveforms

We describe a simple and efficient Python code to perform Bayesian forecasting for gravitational waves (GW) produced by Extreme-Mass-Ratio-Inspiral systems (EMRIs). The code runs on GPUs for an efficient parallelised computation of thousands of waveforms and sampling of the posterior through a Markov-Chain-Monte-Carlo (MCMC) algorithm. EMRI_MC generates EMRI waveforms based on the so--called kludge scheme, and propagates it to the observer accounting for cosmological effects in the observed waveform due to modified gravity/dark energy. Extending the code to more accurate schemes for the generation of the waveform is straightforward. Despite the known limitations of the kludge formalism, we believe that the code can provide a helpful resource for the community working on forecasts for interferometry missions in the milli-Hz scale, predominantly, the satellite-mission LISA.
Dynamic Change of Amplitude for OCT Functional Imaging

Optical coherence tomography (OCT) is capable of non-destructively obtaining cross-sectional information of samples with micrometer spatial resolution, which plays an important role in ophthalmology and endovascular medicine. Measuring OCT amplitude can obtain three-dimensional structural information of the sample, such as the layered structure of the retina, but is of limited use for functional information such as tissue specificity, blood flow, and mechanical properties. OCT functional imaging techniques based on other optical field properties including phase, polarization state, and wavelength have emerged, such as Doppler OCT, optical coherence elastography, polarization-sensitive OCT, and visible-light OCT. Among them, functional imaging techniques based on dynamic changes of amplitude have significant robustness and complexity advantages, and achieved significant clinical success in label-free blood flow imaging. In addition, dynamic light scattering OCT for 3D blood flow velocity measurement, dynamic OCT with the ability to display label-free tissue/cell specificity, and OCT thermometry for monitoring the temperature field of thermophysical treatments are the frontiers in OCT functional imaging. In this paper, the principles and applications of the above technologies are summarized, the remaining technical challenges are analyzed, and the future development is envisioned.
Renormalization group flows from the Hessian geometry of quantum effective actions

We explore a geometric perspective on quantum field theory by considering the configuration space, where all field configurations reside. Employing $n$-particle irreducible effective actions constructed via Legendre transforms of the Schwinger functional, this configuration space can be associated with a Hessian manifold. This allows for various properties and uses of the $n$-particle irreducible effective actions to be re-cast in geometrical terms. In particular, interpreting the two-point source as a regulator, this approach can be readily connected to the functional renormalization group. Renormalization group flows are then understood in terms of geodesics on this Hessian manifold.
Higher-Order DisCoCat (Peirce-Lambek-Montague semantics)

We propose a new definition of higher-order DisCoCat (categorical compositional distributional) models where the meaning of a word is not a diagram, but a diagram-valued higher-order function. Our models can be seen as a variant of Montague semantics based on a lambda calculus where the primitives act on string diagrams rather than logical formulae. As a special case, we show how to translate from the Lambek calculus into Peirce's system beta for first-order logic. This allows us to give a purely diagrammatic treatment of higher-order and non-linear processes in natural language semantics: adverbs, prepositions, negation and quantifiers. The theoretical definition presented in this article comes with a proof-of-concept implementation in DisCoPy, the Python library for string diagrams.
Discrete Empirical Interpolation Method for nonlinear softening problems involving damage and plasticity

Accurate simulations are essential for engineering applications, and intricate continuum mechanical material models are constructed to achieve this goal. However, the increasing complexity of the material models and geometrical properties lead to a significant increase in computational effort. Model order reduction aims to implement efficient methods for accelerating the simulation process while preserving a high degree of accuracy. Numerous studies have already demonstrated the benefits of this method for linear elastic material modeling. However, in the present work, we investigate a two-surface gradient-extended damage-plasticity model. We conducted complex simulations with this model, demonstrating both damage behavior and softening. The POD-based discrete empirical interpolation method (DEIM) is introduced and implemented. To accomplish simulations with DEIM and softening behaviour, we propose the implementation of a reduced form of the arc-length method. Existing research on calculating models with both damage and softening behavior using the DEIM and arc-length method is limited. To validate the methods, two numerical examples are thoroughly investigated in this study: a plate with a hole and an asymmetrically notched specimen. The results show that the proposed methods can create a reduced order model with high accuracy and a significant speedup of the simulation. For both examples, the analysis is conducted in three steps: first, plasticity without damage is examined, followed by damage without plasticity, and finally, the combination of plasticity and damage is investigated.
Dynamic Dense Graph Convolutional Network for Skeleton-Based Human Motion Prediction

Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task. Nevertheless, how to construct a graph from a skeleton sequence and how to perform message passing on the graph are still open problems, which severely affect the performance of GCN. To solve both problems, this paper presents a Dynamic Dense Graph Convolutional Network (DD-GCN), which constructs a dense graph and implements an integrated dynamic message passing. More specifically, we construct a dense graph with 4D adjacency modeling as a comprehensive representation of motion sequence at different levels of abstraction. Based on the dense graph, we propose a dynamic message passing framework that learns dynamically from data to generate distinctive messages reflecting sample-specific relevance among nodes in the graph. Extensive experiments on benchmark Human 3.6M and CMU Mocap datasets verify the effectiveness of our DD-GCN which obviously outperforms state-of-the-art GCN-based methods, especially when using long-term and our proposed extremely long-term protocol.
Feedback RoI Features Improve Aerial Object Detection

Neuroscience studies have shown that the human visual system utilizes high-level feedback information to guide lower-level perception, enabling adaptation to signals of different characteristics. In light of this, we propose Feedback multi-Level feature Extractor (Flex) to incorporate a similar mechanism for object detection. Flex refines feature selection based on image-wise and instance-level feedback information in response to image quality variation and classification uncertainty. Experimental results show that Flex offers consistent improvement to a range of existing SOTA methods on the challenging aerial object detection datasets including DOTA-v1.0, DOTA-v1.5, and HRSC2016. Although the design originates in aerial image detection, further experiments on MS COCO also reveal our module's efficacy in general detection models. Quantitative and qualitative analyses indicate that the improvements are closely related to image qualities, which match our motivation.
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning

Query-focused Summarization (QfS) deals with systems that generate summaries from document(s) based on a query. Motivated by the insight that Reinforcement Learning (RL) provides a generalization to Supervised Learning (SL) for Natural Language Generation, and thereby performs better (empirically) than SL, we use an RL-based approach for this task of QfS. Additionally, we also resolve the conflict of employing RL in Transformers with Teacher Forcing. We develop multiple Policy Gradient networks, trained on various reward signals: ROUGE, BLEU, and Semantic Similarity, which lead to a 10-point improvement over the State-of-the-Art approach on the ROUGE-L metric for a benchmark dataset (ELI5). We also show performance of our approach in zero-shot setting for another benchmark dataset (DebatePedia) -- our approach leads to results comparable to baselines, which were specifically trained on DebatePedia. To aid the RL training, we propose a better semantic similarity reward, enabled by a novel Passage Embedding scheme developed using Cluster Hypothesis. Lastly, we contribute a gold-standard test dataset to further research in QfS and Long-form Question Answering (LfQA).
FAT-HuBERT: Front-End Adaptive Training of Hidden-Unit BERT For Distortion-Invariant Robust Speech Recognition

Advancements in monaural speech enhancement (SE) techniques have greatly improved the perceptual quality of speech. However, integrating these techniques into automatic speech recognition (ASR) systems has not yielded the expected performance gains, primarily due to the introduction of distortions during the SE process. In this paper, we propose a novel approach called FAT-HuBERT, which leverages distortion-invariant self-supervised learning (SSL) to enhance the robustness of ASR. To address the distortions introduced by the SE frontends, we introduce layer-wise fusion modules that incorporate features extracted from both observed noisy signals and enhanced signals. During training, the SE frontend is randomly selected from a pool of models. We evaluate the performance of FAT-HuBERT on simulated noisy speech generated from LIBRISPEECH as well as real-world noisy speech from the CHIME-4 1-channel dataset. The experimental results demonstrate a significant relative reduction in word error rate (WER).
Adapting OpenAI's Whisper for Speech Recognition on Code-Switch Mandarin-English SEAME and ASRU2019 Datasets

This paper details the experimental results of adapting the OpenAI's Whisper model for Code-Switch Mandarin-English Speech Recognition (ASR) on the SEAME and ASRU2019 corpora. We conducted 2 experiments: a) using adaptation data from 1 to 100/200 hours to demonstrate effectiveness of adaptation, b) examining different language ID setup on Whisper prompt. The Mixed Error Rate results show that the amount of adaptation data may be as low as $1\sim10$ hours to achieve saturation in performance gain (SEAME) while the ASRU task continued to show performance with more adaptation data ($>$100 hours). For the language prompt, the results show that although various prompting strategies initially produce different outcomes, adapting the Whisper model with code-switch data uniformly improves its performance. These results may be relevant also to the community when applying Whisper for related tasks of adapting to new target domains.
Are Large Language Models Good Fact Checkers: A Preliminary Study

Recently, Large Language Models (LLMs) have drawn significant attention due to their outstanding reasoning capabilities and extensive knowledge repository, positioning them as superior in handling various natural language processing tasks compared to other language models. In this paper, we present a preliminary investigation into the potential of LLMs in fact-checking. This study aims to comprehensively evaluate various LLMs in tackling specific fact-checking subtasks, systematically evaluating their capabilities, and conducting a comparative analysis of their performance against pre-trained and state-of-the-art low-parameter models. Experiments demonstrate that LLMs achieve competitive performance compared to other small models in most scenarios. However, they encounter challenges in effectively handling Chinese fact verification and the entirety of the fact-checking pipeline due to language inconsistencies and hallucinations. These findings underscore the need for further exploration and research to enhance the proficiency of LLMs as reliable fact-checkers, unveiling the potential capability of LLMs and the possible challenges in fact-checking tasks.
Autonomous Intersection Management for Mixed Traffic Flow.

Recently, researchers have proposed various autonomous intersection management techniques that enable autonomous vehicles to cross the intersection without traffic lights or stop signs. In particular, a priority-based coordination system with provable collision-free and deadlock-free features has been presented. In this paper, we extend the priority-based approach to support legacy vehicles without compromising above-mentioned features. We make the hypothesis that legacy vehicles are able to keep a safe distance from their leading vehicles. Then we explore some special configurations of system that ensures the safe crossing of legacy vehicles. We implement the extended system in a realistic traffic simulator SUMO. Simulations are performed to demonstrate the safety and efficiency of the system.
SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata

The unstructured nature of data used in foundation model development is a challenge to systematic analyses for making data use and documentation decisions. From a Responsible AI perspective, these decisions often rely upon understanding how people are represented in data. We propose a framework designed to guide analysis of human representation in unstructured data and identify downstream risks. We apply the framework in two toy examples using the Common Crawl web text corpus (C4) and LAION-400M. We also propose a set of hypothetical action steps in service of dataset use, development, and documentation.
Building a black hole-wormhole-black hole combination

In this paper, we present the spherically symmetric Proca star in the presence of a phantom field and obtain a traversable wormhole solution for non-trivial topological spacetime. Using numerical methods, symmetric solutions and asymmetric solutions are obtained in two asymptotically flat regions. We find that when changing the throat size $r_{0}$, both the mass $M$ and the Noether charge $Q$ no longer have the spiral characteristics of an independent Proca star, furthermore, the asymmetric solution can be turned into the symmetric solution at some frequency $\omega$ in certain $r_{0}$. In particular, we find that when the frequency takes a certain value, for each solution, there is an extremely approximate black hole solution, and there is even a case where an event horizon appears on both sides of the wormhole throat.
A Bayesian model for dynamic mass reconstruction from PET listmode data

Positron emission tomography (PET) is a classical imaging technique to reconstruct the mass distribution of a radioactive material. If the mass distribution is static, this essentially leads to inversion of the X-ray transform. However, if the mass distribution changes temporally, the measurement signals received over time (the so-called listmode data) belong to different spatial configurations. We suggest and analyse a Bayesian approach to solve this dynamic inverse problem that is based on optimal transport regularization of the temporally changing mass distribution. Our focus lies on a rigorous derivation of the Bayesian model and the analysis of its properties, treating both the continuous as well as the discrete (finitely many detectors and time binning) setting.
A Simple and General Operational Framework to Deploy Optimal Routes with Source Routing

Source Routing, currently facilitated by Segment Routing (SR), enables precise control of forwarding paths by specifying detours (or segments) to deviate IP packets along routes with advanced properties beyond typical shortest IGP paths. Computing the desired optimal segment lists, known as encoding, leads to interesting challenges as the number of detours is tightly constrained for hardware performance. Existing solutions either lack generality, correctness, optimality, or practical computing efficiency-in particular for sparse realistic networks. In this paper, we address all such challenges with GOFOR-SR. Our framework extends usual path computation algorithms to inherently look at optimal and feasible segment lists, streamlining the deployment of TE-compliant paths. By integrating encoding within the path computation itself and modifying the distance comparison method, GOFOR allows algorithms with various optimization objectives to efficiently compute optimal segment lists. Despite the loss of substructure optimality induced by SR, GOFOR proves particularly efficient, inducing only a linear overhead at worst. It also offers different strategies and path diversity options for intricate TE-aware loadbalancing. We formally prove the correctness and optimality of GOFOR, implement our framework for various practical usecases, and demonstrate its performance and benefits on both real and challenging topologies.
Identifying Naturalistic Movies from Human Brain Activity with High-Density Diffuse Optical Tomography

Modern neuroimaging modalities, particularly functional MRI (fMRI), can decode detailed human experiences. Thousands of viewed images can be identified or classified, and sentences can be reconstructed. Decoding paradigms often leverage encoding models that reduce the stimulus space into a smaller yet generalizable feature set. However, the neuroimaging devices used for detailed decoding are non-portable, like fMRI, or invasive, like electrocorticography, excluding application in naturalistic use. Wearable, non-invasive, but lower-resolution devices such as electroencephalography and functional near-infrared spectroscopy (fNIRS) have been limited to decoding between stimuli used during training. Herein we develop and evaluate model-based decoding with high-density diffuse optical tomography (HD-DOT), a higher-resolution expansion of fNIRS with demonstrated promise as a surrogate for fMRI. Using a motion energy model of visual content, we decoded the identities of novel movie clips outside the training set with accuracy far above chance for single-trial decoding. Decoding was robust to modulations of testing time window, different training and test imaging sessions, hemodynamic contrast, and optode array density. Our results suggest that HD-DOT can translate detailed decoding into naturalistic use.
DNA Methylation (DM) data format and DMtools for efficient DNA methylation data storage and analysis

Summary DNA methylation is a critical epigenetic mechanism that plays a pivotal role in various biological processes. Currently, larger datasets from whole-genome bisulfite sequencing for DNA methylation pose challenges throughout the computational analysis pipeline, including storage and memory constraints. Unfortunately, storage formats and analysis tools have not kept pace with these increased resource demands. In this study, we present a new and efficient design for storing DNA methylation (DM) data after mapping in compressed binary indexed DM format. Our format significantly reduces storage space by 80%-95% compared to commonly used file formats for DNA methylation data after mapping. To enhance the processing of DNA methylation data in DM format, we have developed DMtools, a comprehensive toolkit that offers utilities such as rapid and random access, computation of DNA methylation profiles across genes, and analysis of differential DNA methylation. The analysis speed is improved by over 100 times compared to existing methods. Furthermore, we have created pyDMtools, a Python package that efficiently processes DM format files for Python users. The integration of the DM format and its associated tools represents significant progress in handling and exploring DNA methylation data, offering the potential to significantly reduce storage needs and improve downstream analysis capabilities. Availability and implementation https://dmtools-docs.rtfd. io/ https://github.com/ZhouQiangwei/DMtools and https://github.com/ZhouQiangwei/pyDMtools Contact guoliang.li@mail.hzau. edu.cn; qwzhou@mail.hzau. edu.cn Supplementary information Supplementary data are available at Bioinformatics online.
The cryo-EM structure of trypanosome 3-methylcrotonyl-CoA carboxylase provides mechanistic and dynamic insights into enzymatic function

3-methylcrotonyl-CoA carboxylase (MCC) catalyzes the two-step, biotin-dependent production of 3-methylglutaconyl-CoA, an essential intermediate in leucine catabolism. Given its critical metabolic role, deficiencies in this enzyme associate with organic aciduria, while its overexpression is linked to tumor development. MCC is a dodecameric enzyme composed of six copies of each α- and β-subunit. We present the cryo-EM structure of the endogenous MCC holoenzyme from Trypanosoma brucei in its soluble, non-filamentous state at 2.5 Å resolution. We unambiguously observe the position of biotin, covalently-bound to the BBCP domain of α-subunits and occupying a novel binding pocket next to the active site of a neighboring β-subunit dimer. Moreover, flexibility of key residues at the α/α- and α/β-subunit interfaces enables pivoting of α-subunit trimers to sequentially approach the otherwise distant active sites for the two steps in MCC catalysis. Our results provide a structural framework to understand the enzymatic mechanism of eukaryotic MCCs and assist drug discovery against trypanosome infections.
SOX2 and NR2F1 coordinate the gene expression program of the early postnatal visual thalamus

The thalamic dorsolateral geniculate nucleus, (dLGN) receives visual input from the retina via the optic nerve, and projects to the cortical visual area, where eye-derived signals are elaborated. The transcription factors SOX2 and NR2F1 are directly involved in the differentiation of dLGN neurons, based on mouse work and patient mutations leading to vision defects. However, whether they regulate each other, or control common targets is still unclear. By RNA-seq analysis of neonatal dLGN from thalamo-specific Sox2 and Nr2f1 mouse mutants, we found a striking overlap of deregulated genes. Among them, VGF, a cytokine transported along thalamic-cortical axons is strongly downregulated in both mutants. CUT&RUN analysis of SOX2 binding in dLGN chromatin identified a binding pattern characteristic of the dLGN. Collectively, the SOX2 and NR2F1- coregulated genes, and cognate SOX2 binding sites, contribute as a basis to understand the gene regulatory network driving the differentiation and connectivity of thalamic neurons.