Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying information flow in quantum processes

We present a framework for quantifying information flow within general quantum processes. For this purpose, we introduce the signaling power of quantum channels and discuss its relevant operational properties. This function supports extensions to higher order maps, enabling the evaluation of information flow in general quantum causal networks and also processes with indefinite causal order. Furthermore, our results offer a rigorous approach to information dynamics in open systems that applies also in the presence of initial system-environment correlations, and allows for the distinction between classical and quantum information backflow.
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with Compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic.
MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats

In response to the evolving challenges posed by small unmanned aerial vehicles (UAVs), which possess the potential to transport harmful payloads or independently cause damage, we introduce MMAUD: a comprehensive Multi-Modal Anti-UAV Dataset. MMAUD addresses a critical gap in contemporary threat detection methodologies by focusing on drone detection, UAV-type classification, and trajectory estimation. MMAUD stands out by combining diverse sensory inputs, including stereo vision, various Lidars, Radars, and audio arrays. It offers a unique overhead aerial detection vital for addressing real-world scenarios with higher fidelity than datasets captured on specific vantage points using thermal and RGB. Additionally, MMAUD provides accurate Leica-generated ground truth data, enhancing credibility and enabling confident refinement of algorithms and models, which has never been seen in other datasets. Most existing works do not disclose their datasets, making MMAUD an invaluable resource for developing accurate and efficient solutions. Our proposed modalities are cost-effective and highly adaptable, allowing users to experiment and implement new UAV threat detection tools. Our dataset closely simulates real-world scenarios by incorporating ambient heavy machinery sounds. This approach enhances the dataset's applicability, capturing the exact challenges faced during proximate vehicular operations. It is expected that MMAUD can play a pivotal role in advancing UAV threat detection, classification, trajectory estimation capabilities, and beyond. Our dataset, codes, and designs will be available in https://github.com/ntu-aris/MMAUD.
Extended Doubled Structures of Algebroids for Gauged Double Field Theory

We study an analogue of the Drinfel'd double for algebroids associated with the $O(D,D+n)$ gauged double field theory (DFT). We show that algebroids defined by the twisted C-bracket in the gauged DFT are built out of a direct sum of three (twisted) Lie algebroids. They exhibit a"tripled", which we call the extended double, rather than the"doubled"structure appeared in (ungauged) DFT. We find that the compatibilities of the extended doubled structure result not only in the strong constraint but also the additional condition in the gauged DFT. We establish a geometrical implementation of these structures in a $(2D+n)$-dimensional product manifold and examine the relations to the generalized geometry for heterotic string theories and non-Abelian gauge symmetries in DFT.
QQMR: A Structure-Preserving Quaternion Quasi-Minimal Residual Method for Non-Hermitian Quaternion Linear Systems

The quaternion biconjugate gradient (QBiCG) method, as a novel variant of quaternion Lanczos-type methods for solving the non-Hermitian quaternion linear systems, does not yield a minimization property. This means that the method possesses a rather irregular convergence behavior, which leads to numerical instability. In this paper, we propose a new structure-preserving quaternion quasi-minimal residual method, based on the quaternion biconjugate orthonormalization procedure with coupled two-term recurrences, which overcomes the drawback of QBiCG. The computational cost and storage required by the proposed method are much less than the traditional QMR iterations for the real representation of quaternion linear systems. Some convergence properties of which are also established. Finally, we report the numerical results to show the robustness and effectiveness of the proposed method compared with QBiCG.
Single system based generation of certified randomness using Leggett-Garg inequality

We theoretically formulate and experimentally demonstrate a secure scheme for semi-device-independent quantum random number generation by utilizing Leggett-Garg inequality violations, within a loophole-free photonic architecture. The quantification of the generated randomness is rigorously estimated by analytical as well as numerical approaches, both of which are in perfect agreement. We securely generate $9,19,118$ truly unpredictable bits. This opens up an unexplored avenue towards an empirically convenient class of reliable random number generators harnessing the quantumness of single systems.
Multi-color Wavefront Sensor using Talbot effect for High-order Harmonic Generation

We present a novel method for multi-color wavefront measurement of high-order harmonic generation beams using the Talbot effect, validated both theoretically and experimentally for the first time. Each harmonic maintains a unique wavefront and produces an independent set of self-images along the optical axis.We achieved the wavefronts reconstruction of three harmonics in a single measurement scan, expanding the spectrally-resolved capability of the conventional Talbot effect wavefront sensor. This breakthrough introduces a novel tool for studying the multi-color wavefront in high-order harmonic generation, unlocking the potential to investigate spatiotemporal ultrafast nonlinear dynamics in attosecond pulse formation on a shot-by-shot basis.
Redex -> Coq: towards a theory of decidability of Redex's reduction semantics

We propose the first steps in the development of a tool to automate the translation of Redex models into a (hopefully) semantically equivalent model in Coq, and to provide tactics to help in the certification of fundamental properties of such models. The work is heavily based on a model of Redex's semantics developed by Klein et al. By means of a simple generalization of the matching problem in Redex, we obtain an algorithm suitable for its mechanization in Coq, for which we prove its soundness properties and its correspondence with the original solution proposed by Klein et al. In the process, we also adequate some parts of our mechanization to better prepare it for the future inclusion of Redex features absent in the present model, like its Kleene-star operator. Finally, we discuss future avenues of development that are enabled by this work.
ICED: Zero-Shot Transfer in Reinforcement Learning via In-Context Environment Design

Autonomous agents trained using deep reinforcement learning (RL) often lack the ability to successfully generalise to new environments, even when they share characteristics with the environments they have encountered during training. In this work, we investigate how the sampling of individual environment instances, or levels, affects the zero-shot generalisation (ZSG) ability of RL agents. We discover that, for deep actor-critic architectures sharing their base layers, prioritising levels according to their value loss minimises the mutual information between the agent's internal representation and the set of training levels in the generated training data. This provides a novel theoretical justification for the implicit regularisation achieved by certain adaptive sampling strategies. We then turn our attention to unsupervised environment design (UED) methods, which have more control over the data generation mechanism. We find that existing UED methods can significantly shift the training distribution, which translates to low ZSG performance. To prevent both overfitting and distributional shift, we introduce in-context environment design (ICED). ICED generates levels using a variational autoencoder trained over an initial set of level parameters, reducing distributional shift, and achieves significant improvements in ZSG over adaptive level sampling strategies and UED methods.
Expand-and-Cluster: Parameter Recovery of Neural Networks

Can we identify the parameters of a neural network by probing its input-output mapping? Usually, there is no unique solution because of permutation, overparameterisation and activation function symmetries. Yet, we show that the incoming weight vector of each neuron is identifiable up to sign or scaling, depending on the activation function. For all commonly used activation functions, our novel method 'Expand-and-Cluster' identifies the size and parameters of a target network in two phases: (i) to relax the non-convexity of the problem, we train multiple student networks of expanded size to imitate the mapping of the target network; (ii) to identify the target network, we employ a clustering procedure and uncover the weight vectors shared between students. We demonstrate successful parameter and size recovery of trained shallow and deep networks with less than 10% overhead in the neuron number and describe an 'ease-of-identifiability' axis by analysing 150 synthetic problems of variable difficulty.
Older individuals do not show task specific variations in EEG band power and finger force coordination

Background Controlling and coordinating finger force is crucial for performing everyday tasks and maintaining functional independence. Aging naturally weakens neural, muscular, and musculoskeletal systems, leading to compromised hand motor function. This decline reduces cortical activity, finger force control and coordination in older adults. Objective To examine the relationship between EEG band power and finger force coordination in older individuals and compare the results with young adults. Methods Twenty healthy young adults aged 20–30 (26.96±2.68) and fourteen older adults aged 58–72 (62.57±3.58) participated in this study. Participants held the instrumented handle gently for five seconds then lifted and held it for an additional five seconds in the two conditions: fixed (thumb platform secured) and free condition (thumb platform may slide on slider). Results In the older individuals there was no difference observed in the finger force synergy indices, and EEG beta band power between the two task conditions. However, in the young group synergy indices and EEG beta band power were less in free condition compared to fixed condition. Additionally, in the fixed condition, older adults showed a reduced synergy indices and reduced EEG beta band power than the young adults. Conclusion Older participants showed consistent synergy indices and beta band power across conditions, unlike young adults who adjusted strategies based on tasks. Young adults exhibited task-dependent finger force synergy indices contrasting with older individuals. These results suggest that the EEG beta band power may serve as a marker for finger force coordination rather than indicating the magnitude of force.
Deep Learning Prediction of Glycopeptide Tandem Mass Spectra Powers Glycoproteomics

Protein glycosylation plays a significant role in numerous physiological and pathological cellular functions. Glycoproteomics based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) studies the protein glycosylation on a proteome-wide scale to get combinational information on glycosylation site, glycosylation level and glycan structure. However, the current sequence searching-based methods for glycoproteomics often fall short in glycan structure determination due to the limited occurrence of structure-determining ions. While spectral searching methods can utilize fragment intensity information to facilitate the identification of glycopeptides, its application is hindered by the difficulties in spectral library construction. In this work, we present DeepGP, a hybrid deep learning framework based on Transformer and graph neural network (GNN), for the prediction of MS/MS spectra and retention time of glycopeptides. Two GNN modules are utilized to capture the branched glycan structure and predict glycan ions intensity, respectively. Additionally, a pre-training strategy is implemented to alleviate the insufficiency of glycoproteomics data. Testing on multiple biological datasets, we demonstrate that DeepGP can predict MS/MS spectra and retention time of glycopeptides closely aligning with the experimental results. Comprehensive benchmarking of DeepGP on synthetic and biological datasets validates its effectiveness in distinguishing similar glycoforms. Remarkably, DeepGP can differentiate isomeric glycopeptides using MS/MS spectra without diagnostic ions. Based on various decoy methods, we demonstrated that DeepGP in combination with database searching can significantly increase the detection sensitivity of glycopeptides. We outlook that DeepGP can inspire extensive future work in glycoproteomics.
Disrupted mitochondrial response to nutrients is a presymptomatic event in the cortex of the APPSAA knock-in mouse model of Alzheimer’s disease

Introduction Reduced brain energy metabolism, mTOR dysregulation, and extracellular amyloid-β oligomer (xcAβO) buildup characterize AD; how they collectively promote neurodegeneration is poorly understood. We previously reported that xcAβOs inhibit Nutrient-induced Mitochondrial Activity (NiMA) in cultured neurons. We now report NiMA disruption in vivo. Methods Brain energy metabolism and oxygen consumption were recorded in APPSAA/+ mice using two-photon fluorescence lifetime imaging and multiparametric photoacoustic microscopy. Results NiMA is inhibited in APPSAA/+ mice before other defects are detected in these amyloid-β-producing animals that do not overexpress APP or contain foreign DNA inserts into genomic DNA. GSK3β signals through mTORC1 to regulate NiMA independently of mitochondrial biogenesis. Inhibition of GSK3β with lithium or TWS119 stimulates NiMA in cultured human neurons, and mitochondrial activity and oxygen consumption in APPSAA mice. Conclusion NiMA disruption in vivo occurs before histopathological changes and cognitive decline in APPSAA mice, and may represent an early stage in human AD.
Taurine pangenome uncovers a segmental duplication upstream of KIT associated with depigmentation in white-headed cattle

Cattle have been selectively bred for coat color, spotting, and depigmentation patterns. The assumed autosomal dominant inherited genetic variants underlying the characteristic white head of Fleckvieh, Simmental, and Hereford cattle have not been identified yet, although the contribution of structural variation upstream the KIT gene has been proposed. Here, we construct a graph pangenome from 24 haplotype assemblies representing seven taurine cattle breeds to identify and characterize the white head-associated locus for the first time based on long-read sequencing data. Through examining assembly path similarities within the graph, we reveal an association between two most likely serial alleles of a complex structural variant 66 kb upstream KIT and facial depigmentation. The complex structural variant contains a variable number of tandemly duplicated 14.3 kb repeats, consisting of LTRs, LINEs, and other repetitive elements, leading to misleading alignments of short and long reads when using a linear reference. We align 250 short-read sequencing samples spanning 15 cattle breeds to the pangenome graph, further validating that the alleles of the structural variant segregate with head depigmentation. We estimate an increased count of repeats in Hereford relative to Simmental and other white-headed cattle breeds from the graph alignment coverage, suggesting a large under-assembly in the current Hereford-based cattle reference genome which had fewer copies. We show that exploiting assembly path similarities within graph pangenomes can reveal trait-associated complex structural variants.
Identifying compositional and density changes across the Murine and Human Dentin-enamel Junction

Human and mouse incisors are both primarily composed of dentin and enamel which meet at an interface called the dentin-enamel junction (DEJ). However, incisors in the two species have very different growth patterns, structures, and loading requirements. Since the DEJ is responsible for minimizing cracking at this at-risk interface between mechanically dissimilar dentin and enamel, its structure is expected to be significantly different between humans and mice. Here, strucutral and compositional gradients across human and murine incisors DEJs were measured via microcomputed tomography and Raman spectroscopy. Density gradients across the DEJ were significantly larger in humans compared to murine teeth likely due to the larger size of the mantle dentin. Multiple gradients in mineral content and crystallinity were found at the murine DEJ while the human DEJ only exhibited gradients in mineral content. Models predicting the modulus across the DEJ according to compositional results show that mineral crystallinity is critical in regulating the mechanical gradient across the murine DEJ. Together these results show the multiple ways in which the DEJ can adapt to variations in loading environment.
Conserved Pseudouridines in Helix 69 of the Ribosome are Important for Ribosome Dynamics in Translation

The widespread distribution of pseudouridine (Ψ), an isomer of the canonical uridine base, in RNA indicates its functional importance to the cell. In eukaryotes, it is estimated that around 2% of ribosomal RNA nucleotides are pseudouridines, most of which are located in functional regions of the ribosome. Defects in RNA pseudouridylation induce a range of detrimental effects from compromised cellular protein biosynthesis to disease phenotypes in humans. However, genome-wide changes to mRNA translation profiles by ribosomes lacking specific conserved pseudouridines have not been extensively studied. Here, using a new genomic method called 5P-Seq and in vitro biochemistry, we investigated changes in ribosome dynamics and cellular translation profiles upon loss of Ψ2258 and Ψ2260 in helix 69, the two most conserved pseudouridines in the ribosomes. We found that inhibiting the formation of these two pseudouridines challenges ribosomes to maintain the correct open reading frame and causes generally faster ribosome dynamics in translation. Furthermore, mutant ribosomes are more prone to pause while translating a subset of GC-rich codons, especially rare codons such as Arg (CGA) and Arg (CGG). Our data suggest the presence of Ψ2258 and Ψ2260 contributes to the dynamics of the H69 RNA stem-loop, and helps to maintain functional interactions with the tRNAs as they move within the ribosome. The optimality of this ribosome-tRNA interaction is likely to be more critical for those limited tRNAs that decode rare codons. Consistent with the changes in ribosome dynamics, our data also show that IRES-mediated translation is compromised in the mutant ribosome. These results explain the importance of Ψ2258 and Ψ2260 in H69 to maintain cellular fitness. The strong conservation of Ψ2258 and Ψ2260 in the ribosomes from bacteria to humans indicates their functional significance in modulating ribosome functions. We anticipate that the identified functions of these covalent modifications will be conserved in other species.
Single-molecule chromatin configurations link transcription factor binding to expression in human cells

The binding of multiple transcription factors (TFs) to genomic enhancers activates gene expression in mammalian cells. However, the molecular details that link enhancer sequence to TF binding, promoter state, and gene expression levels remain opaque. We applied single-molecule footprinting (SMF) to measure the simultaneous occupancy of TFs, nucleosomes, and components of the transcription machinery on engineered enhancer/promoter constructs with variable numbers of TF binding sites for both a synthetic and an endogenous TF. We find that activation domains enhance a TF’s capacity to compete with nucleosomes for binding to DNA in a BAF-dependent manner, TF binding on nucleosome-free DNA is consistent with independent binding between TFs, and average TF occupancy linearly contributes to promoter activation rates. We also decompose TF strength into separable binding and activation terms, which can be tuned and perturbed independently. Finally, we develop thermodynamic and kinetic models that quantitatively predict both the binding microstates observed at the enhancer and subsequent time-dependent gene expression. This work provides a template for quantitative dissection of distinct contributors to gene activation, including the activity of chromatin remodelers, TF activation domains, chromatin acetylation, TF concentration, TF binding affinity, and TF binding site configuration.
Transcriptional responses to priority effects in nectar yeast

Priority effects, where the order and timing of species arrival influence the assembly of ecological communities, have been observed in a variety of taxa and habitats. However, the genetic and molecular basis of priority effects remains unclear in most cases, hindering the mechanistic understanding of priority effects. We sought to gain such understanding for the common nectar yeast Metschnikowia reukaufii, which engages in strong priority effects with other species of nectar yeast, including M. rancensis, another species commonly found in the nectar of our study plant, the hummingbird-pollinated Diplacus (Mimulus) aurantiacus. After inoculation into two contrasting types of synthetic nectar simulating early arrival of M. rancensis, we conducted whole-transcriptome sequencing of 108 genetically diverse strains of M. reukaufii. We found that several genes were differentially expressed in M. reukaufii strains when the nectar had been conditioned by growth of M. rancensis. Many of these genes were associated with amino acid metabolism, consistent with our previous finding that early-arriving species limit late-arriving species’ growth by reducing amino acid availability. Furthermore, investigation of expression quantitative trait loci (eQTLs) revealed that genes involved in amino acid transport and resistance to antifungal compounds were enriched in genetic variants, with differing effects on gene expression based on priority effects of M. rancensis. These results demonstrate that intraspecific variation in the ability of nectar yeasts to respond to nutrient limitation and direct fungal competition may underpin the molecular mechanisms of priority effects.