Miracle-dz/UniFAR-FTUs huggingface dataset:
Audiobox: Unified Audio Generation with Natural Language Prompts

Audio is an essential part of our life, but creating it often requires expertise and is time-consuming. Research communities have made great progress over the past year advancing the performance of large scale audio generative models for a single modality (speech, sound, or music) through adopting more powerful generative models and scaling data. However, these models lack controllability in several aspects: speech generation models cannot synthesize novel styles based on text description and are limited on domain coverage such as outdoor environments; sound generation models only provide coarse-grained control based on descriptions like"a person speaking"and would only generate mumbling human voices. This paper presents Audiobox, a unified model based on flow-matching that is capable of generating various audio modalities. We design description-based and example-based prompting to enhance controllability and unify speech and sound generation paradigms. We allow transcript, vocal, and other audio styles to be controlled independently when generating speech. To improve model generalization with limited labels, we adapt a self-supervised infilling objective to pre-train on large quantities of unlabeled audio. Audiobox sets new benchmarks on speech and sound generation (0.745 similarity on Librispeech for zero-shot TTS; 0.77 FAD on AudioCaps for text-to-sound) and unlocks new methods for generating audio with novel vocal and acoustic styles. We further integrate Bespoke Solvers, which speeds up generation by over 25 times compared to the default ODE solver for flow-matching, without loss of performance on several tasks. Our demo is available at https://audiobox.metademolab. com/
CARSS: Cooperative Attention-guided Reinforcement Subpath Synthesis for Solving Traveling Salesman Problem

This paper introduces CARSS (Cooperative Attention-guided Reinforcement Subpath Synthesis), a novel approach to address the Traveling Salesman Problem (TSP) by leveraging cooperative Multi-Agent Reinforcement Learning (MARL). CARSS decomposes the TSP solving process into two distinct yet synergistic steps:"subpath generation"and"subpath merging. "In the former, a cooperative MARL framework is employed to iteratively generate subpaths using multiple agents. In the latter, these subpaths are progressively merged to form a complete cycle. The algorithm's primary objective is to enhance efficiency in terms of training memory consumption, testing time, and scalability, through the adoption of a multi-agent divide and conquer paradigm. Notably, attention mechanisms play a pivotal role in feature embedding and parameterization strategies within CARSS. The training of the model is facilitated by the independent REINFORCE algorithm. Empirical experiments reveal CARSS's superiority compared to single-agent alternatives: it demonstrates reduced GPU memory utilization, accommodates training graphs nearly 2.5 times larger, and exhibits the potential for scaling to even more extensive problem sizes. Furthermore, CARSS substantially reduces testing time and optimization gaps by approximately 50% for TSP instances of up to 1000 vertices, when compared to standard decoding methods.
Robust Stochastically-Descending Unrolled Networks

Deep unrolling, or unfolding, is an emerging learning-to-optimize method that unrolls a truncated iterative algorithm in the layers of a trainable neural network. However, the convergence guarantees and generalizability of the unrolled networks are still open theoretical problems. To tackle these problems, we provide deep unrolled architectures with a stochastic descent nature by imposing descending constraints during training. The descending constraints are forced layer by layer to ensure that each unrolled layer takes, on average, a descent step toward the optimum during training. We theoretically prove that the sequence constructed by the outputs of the unrolled layers is then guaranteed to converge for unseen problems, assuming no distribution shift between training and test problems. We also show that standard unrolling is brittle to perturbations, and our imposed constraints provide the unrolled networks with robustness to additive noise and perturbations. We numerically assess unrolled architectures trained under the proposed constraints in two different applications, including the sparse coding using learnable iterative shrinkage and thresholding algorithm (LISTA) and image inpainting using proximal generative flow (GLOW-Prox), and demonstrate the performance and robustness benefits of the proposed method.
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization

Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of domains. However, DNNs are becoming computationally intensive and energy hungry at an exponential pace, while at the same time, there is a vast demand for running sophisticated DNN-based services on resource constrained embedded devices. In this paper, we target energy-efficient inference on embedded DNN accelerators. To that end, we propose an automated framework to compress DNNs in a hardware-aware manner by jointly employing pruning and quantization. We explore, for the first time, per-layer fine- and coarse-grained pruning, in the same DNN architecture, in addition to low bit-width mixed-precision quantization for weights and activations. Reinforcement Learning (RL) is used to explore the associated design space and identify the pruning-quantization configuration so that the energy consumption is minimized whilst the prediction accuracy loss is retained at acceptable levels. Using our novel composite RL agent we are able to extract energy-efficient solutions without requiring retraining and/or fine tuning. Our extensive experimental evaluation over widely used DNNs and the CIFAR-10/100 and ImageNet datasets demonstrates that our framework achieves $39\%$ average energy reduction for $1.7\%$ average accuracy loss and outperforms significantly the state-of-the-art approaches.
Fast KV-Switching and Dual-Layer Flat-Panel Detector Enabled Cone-Beam CT Joint Spectral Imaging

Purpose: Fast kV-switching (FKS) and dual-layer flat-panel detector (DL-FPD) technologies have been actively studied as promising dual-energy solutions for FPD-based cone-beam computed tomography (CBCT). However, CBCT spectral imaging is known to face challenges in obtaining accurate and robust material discrimination performance due to the limited energy separation. To further improve CBCT spectral imaging capability, this work aims to promote a source-detector joint spectral imaging solution which takes advantages of both FKS and DL-FPD, and to conduct a feasibility study on the first tabletop CBCT system with the joint spectral imaging capability developed. Methods: In this work, the first FKS and DL-FPD jointly enabled multi-energy tabletop CBCT system has been developed in our laboratory. To evaluate its spectral imaging performance, a set of physics experiments are conducted, where the multi-energy and head phantoms are scanned using the 80/105/130kVp switching pairs and projection data are collected using a prototype DL-FPD. To compensate for the slightly angular mismatch between the low- and high-energy projections in FKS, a dual-domain projection completion scheme is implemented. Afterwards material decomposition is carried out by using the maximum-likelihood method, followed by reconstruction of basis material and virtual monochromatic images. Results: The physics experiments confirmed the feasibility and superiority of the joint spectral imaging, whose CNR of the multi-energy phantom were boosted by an average improvement of 21.9%, 20.4% for water and 32.8%, 62.8% for iodine when compared with that of the FKS and DL-FPD in fan-beam and cone-beam experiments, respectively. Conclusions: A feasibility study of the joint spectral imaging for CBCT by utilizing both the FKS and DL-FPD was conducted, with the first tabletop CBCT system having such a capability being developed.
A Survey on Open-Set Image Recognition

Open-set image recognition (OSR) aims to both classify known-class samples and identify unknown-class samples in the testing set, which supports robust classifiers in many realistic applications, such as autonomous driving, medical diagnosis, security monitoring, etc. In recent years, open-set recognition methods have achieved more and more attention, since it is usually difficult to obtain holistic information about the open world for model training. In this paper, we aim to summarize the up-to-date development of recent OSR methods, considering their rapid development in recent two or three years. Specifically, we firstly introduce a new taxonomy, under which we comprehensively review the existing DNN-based OSR methods. Then, we compare the performances of some typical and state-of-the-art OSR methods on both coarse-grained datasets and fine-grained datasets under both standard-dataset setting and cross-dataset setting, and further give the analysis of the comparison. Finally, we discuss some open issues and possible future directions in this community.
Dotless Representation of Arabic Text: Analysis and Modeling

This paper presents a novel dotless representation of Arabic text as an alternative to the standard Arabic text representation. We delve into its implications through comprehensive analysis across five diverse corpora and four different tokenization techniques. We explore the impact of dotless representation on the relationships between tokenization granularity and vocabulary size and compare them with standard text representation. Moreover, we analyze the information density of dotless versus standard text using text entropy calculations. To delve deeper into the implications of the dotless representation, statistical and neural language models are constructed using the various text corpora and tokenization techniques. A comparative assessment is then made against language models developed using the standard Arabic text representation. This multifaceted analysis provides valuable insights into the potential advantages and challenges associated with the dotless representation. Last but not the least, utilizing parallel corpora, we draw comparisons between the text analysis of Arabic and English to gain further insights. Our findings shed light on the potential benefits of dotless representation for various NLP tasks, paving the way for further exploration for Arabic natural language processing.
Communication protocol for a satellite-swarm interferometer

Orbiting low frequency antennas for radio astronomy (OLFAR) that capture cosmic signals in the frequency range below 30MHz could provide valuable insights on our Universe. These wireless swarms of satellites form a connectivity graph that allows data exchange between most pairs of satellites. Since this swarm acts as an interferometer, the aim is to compute the cross-correlations between most pairs of satellites. We propose a k-nearest-neighbour communication protocol, and investigate the minimum neighbourhood size of each satellite that ensures connectivity of at least 95% of the swarm. We describe the proportion of cross-correlations that can be computed in our method given an energy budget per satellite. Despite the method's apparent simplicity, it allows us to gain insight into the requirements for such satellite swarms. In particular, we give specific advice on the energy requirements to have sufficient coverage of the relevant baselines.
Deformable Image Registration with Stochastically Regularized Biomechanical Equilibrium

Numerous regularization methods for deformable image registration aim at enforcing smooth transformations, but are difficult to tune-in a priori and lack a clear physical basis. Physically inspired strategies have emerged, offering a sound theoretical basis, but still necessitating complex discretization and resolution schemes. This study introduces a regularization strategy that does not require discretization, making it compatible with current registration frameworks, while retaining the benefits of physically motivated regularization for medical image registration. The proposed method performs favorably in both synthetic and real datasets, exhibiting an accuracy comparable to current state-of-the-art methods.
Attribution of Arctic Sea Ice Decline from 1953 to 2012 to Influences from Natural, Greenhouse Gas, and Anthropogenic Aerosol Forcing

The paper presents results from a climate change detection and attribution study on the decline of Arctic sea ice extent in September for the 1953–2012 period. For this period three independently derived observational datasets and simulations from multiple climate models are available to attribute observed changes in the sea ice extent to known climate forcings. Here we direct our attention to the combined cooling effect from other anthropogenic forcing agents (mainly aerosols), which has potentially masked a fraction of greenhouse gas–induced Arctic sea ice decline. The presented detection and attribution framework consists of a regression model, namely, regularized optimal fingerprinting, where observations are regressed onto model-simulated climate response patterns (i.e., fingerprints). We show that fingerprints from greenhouse gas, natural, and other anthropogenic forcings are detected in the three observed records of Arctic sea ice extent. Beyond that, our findings indicate that for the 1953–2012 period roughly 23% of the greenhouse gas–induced negative sea ice trend has been offset by a weak positive sea ice trend attributable to other anthropogenic forcing. We show that our detection and attribution results remain robust in the presence of emerging nonstationary internal climate variability acting upon sea ice using a perfect model experiment and data from two large ensembles of climate simulations.
What drives the continuous adoption of mobile stock trading applications among Gen-Z traders? An investigation of the TAM, social influence, trust, and perceived security

This work investigates the factors influencing the continuous adoption of mobile stock trading applications among Gen-Z traders in Malaysia. A research model was proposed based on the Technology Acceptance Model, social influence, trust, and perceived security. We employed the Partial Least Squares Structural Equation Modelling to assess the model based on 220 valid responses from Malaysian Gen-Z traders. The results endorsed perceived usefulness, perceived ease of use, social influence, and perceived security as significant drivers of the continuous adoption of mobile stock trading applications. Trust does not impact Malaysian Gen-Z traders' intention to continuously use mobile stock trading applications. We discuss how these findings implicate service providers to leverage functional elements, social influence, and perceived security to promote the technology's long-term adoption among Gen-Z traders.
Genotypic and Phenotypic Analyses of Two Distinct Sets of Pseudomonas aeruginosa Urinary Tract Isolates

Urinary tract infections (UTIs) are associated with a high burden of morbidity, mortality, and cost. Pseudomonas aeruginosa employs a myriad of virulence factors, including biofilm formation and motility mechanisms, to cause infections including persistent UTIs. P. aeruginosa is highly resistant to antibiotics and the World Health Organization has identified it as a pathogen for which novel antimicrobials are urgently required. Genotypic and phenotypic characterization of P. aeruginosa from UTIs are underreported. In addition, the rise of antimicrobial resistance (AMR) is a cause for concern, particularly in many countries where surveillance is severely lacking. 22 P. aeruginosa UTI isolates were sourced from the United Kingdom (UK) and Kuwait. To establish the phenotypes of UK isolates, growth analysis, biofilm formation assays, motility assays, and antibiotic disc diffusion assays were performed. Whole genome sequencing, antimicrobial susceptibility assays, and in silico detection of AMR-associated genes were conducted on both sets of isolates. In terms of their phenotypic characteristics and genomic composition, the UTI isolates varied. Multiple resistance genes associated with resistance to various classes of antibiotics, such as aminoglycosides, fluoroquinolones, and β-lactams, particularly in isolates from Kuwait. Extreme antibiotic resistance was detected in the isolates obtained from Kuwait, indicating that the country may be an antibiotic resistance hotspot. This study highlights that isolates from UTIs are diverse and can display extremely high resistance. Surveillance in countries such as Kuwait are currently limited and this study suggest the need for greater surveillance.
Identification of new proviral and antiviral factors through the study of the Dicer-2 interactome in vivo during viral infection in Drosophila melanogaster

RNA interference, which has a major role in the control of viral infection in insects, is initialized by the sensing of double stranded RNA (dsRNA) by the RNAse III enzyme Dicer-2. Although many in vitro studies have helped understand how Dicer-2 is able to discriminate between different dsRNA substrate termini, much less is known about how this translates to the in vivo recognition of viral dsRNA. Indeed, although Dicer-2 associates with several dsRNA-binding proteins (dsRBPs) that can modify its specificity for a substrate, it remains unknown how Dicer-2 is able to recognize the protected termini of viral dsRNAs. In order to study how the ribonucleoprotein network of Dicer-2 impacts antiviral immunity, we used an IP-MS approach to identify in vivo interactants of different versions of GFP::Dicer-2 in transgenic lines. We provide a global overview of the partners of Dicer-2 in vivo, and reveal how this interactome is modulated by different factors such as the viral infection and/or different point mutations inactivating the helicase or RNase III domains of GFP::Dicer-2. Our analysis uncovers several previously unknown Dicer-2 interactants associated with RNA granules (i.e. Me31B, Rump, eIF4E1 & Syp). Functional characterization of the candidates reveals pro- and antiviral factors in the context of the infection by the picorna-like DCV virus. In particular, the protein Rasputin has been identified as a novel antiviral candidate. The resources provided by this work can be used to gain a better understanding of the molecular complexes assembled around Dicer-2 in the context of antiviral RNAi and beyond.
Connecting Hippo Pathway and Cytoophidia in Drosophila Posterior Follicle Cells

CTP synthase (CTPS), the rate-limiting enzyme in de novo synthesis of CTP, assembles into filamentous structures termed cytoophidia. Here we study the impact of Hippo pathway on the posterior follicle cells (PFCs) in Drosophila egg chambers. We find that the inactivation of Hippo pathway correlates with a reduction in cytoophidium length and number within PFCs. During the overexpression of CTPS, the presence of Hippo mutations also reduces the length of cytoophidia in PFCs. In addition, we observe that knocking down CTPS mitigates hpo (Hippo)-associated over-proliferation. In summary, our results suggest a connection between the Hippo pathway and the nucleotide biosynthesis enzyme CTPS in PFCs.
The Zea mays PeptideAtlas – a new maize community resource

We developed the Maize PeptideAtlas resource (www.peptideatlas.org/builds/maize) to help solve questions about the maize proteome. Publicly available raw tandem mass spectrometry (MS/MS) data for maize were collected from ProteomeXchange and reanalyzed through a uniform processing and metadata annotation pipeline. These data are from a wide range of genetic backgrounds, including the inbred lines B73 and W22, many hybrids and their respective parents. Samples were collected from field trials, controlled environmental conditions, a range of (a)biotic conditions and different tissues, cell types and subcellular fractions. The protein search space included different maize genome annotations for the B73 inbred line from MaizeGDB, UniProtKB, NCBI RefSeq and for the W22 inbred line. 445 million MS/MS spectra were searched, of which 120 million were matched to 0.37 million distinct peptides. Peptides were matched to 66.2% of the proteins (one isoform per protein coding gene) in the most recent B73 nuclear genome annotation (v5). Furthermore, most conserved plastid- and mitochondrial-encoded proteins (NCBI RefSeq annotations) were identified. Peptides and proteins identified in the other searched B73 genome annotations will aid to improve maize genome annotation. We also illustrate high confidence detection of unique SNPs in the W22 proteome as compared to the B73 proteome. N-terminal acetylation, phosphorylation, ubiquitination, and three lysine acylations (K-acetyl, K-malonyl, K-hydroxybutyryl) were identified and can be inspected through a PTM viewer in PeptideAtlas. All matched MS/MS-derived peptide data are linked to spectral, technical and biological metadata. This new PeptideAtlas will be integrated with community resources including MaizeGDB at https://www.maizegdb. org/ and its JBrowse tracks.
CEP41, a ciliopathy-linked centrosomal protein, regulates microtubule assembly and cell division

Centrosomal proteins play pivotal roles in orchestrating microtubule dynamics, and their dysregulation leads to various disorders, including cancer and ciliopathies. Understanding the multifaceted roles of centrosomal proteins is vital to comprehend their involvement in disease development. Here, we report novel cellular functions of CEP41, a centrosomal and ciliary protein implicated in Joubert syndrome, a neurodevelopmental ciliopathy. We show that CEP41 is an essential microtubule-associated protein with microtubule-stabilizing activity. In vitro, purified CEP41 binds to preformed microtubules, promotes microtubule nucleation, and suppresses microtubule disassembly. When overexpressed in cultured cells, CEP41 localizes to microtubules and promotes formation of stable microtubule bundles. Conversely, shRNA-mediated knockdown of CEP41 causes disassembly of the interphase microtubule network, emphasizing its role in microtubule organization. Using GFP-tagged deletion constructs, we demonstrate that CEP41’s association with microtubules relies on its conserved rhodanese homology domain and the N- terminal region. Furthermore, CEP41 depletion inhibits cell proliferation and induces G1 arrest, suggesting its potential role in cell cycle regulation. These insights into the cellular functions of CEP41 hold promise for unraveling the impact of its mutations in ciliopathies. Summary A novel role of CEP41 as a microtubule-associated protein that promotes microtubule assembly and regulates cell cycle progression could offer insights into the development of ciliopathies.
Three-step mechanism of promoter escape by RNA polymerase II

The transition from transcription initiation to elongation is highly regulated in human cells but remains incompletely understood at the structural level. In particular, it is unclear how interactions between RNA polymerase (Pol) II and initiation factors are broken to enable promoter escape. Here we reconstitute Pol II promoter escape in vitro, determine high-resolution structures of five transition intermediates, and show that promoter escape occurs in three major steps. First, the growing RNA transcript displaces the B-reader element of the initiation factor TFIIB without evicting TFIIB. Second, rewinding of the upstream edge of the growing DNA bubble evicts TFIIA, TFIIB and TBP and repositions parts of TFIIE and TFIIF. Third, binding of DSIF and NELF evicts TFIIE and TFIIH, establishing the paused elongation complex. This three-step model for promoter escape fills a gap in our understanding of the initiation-elongation transition of Pol II transcription.