Miracle-dz/UniFAR-FTUs huggingface dataset:
Dengue virus infection modifies mosquito blood-feeding behavior to increase transmission to the host

Significance Because dengue viruses are spread by mosquitoes during biting, transmission capacity depends on mosquito-biting behavior. For this reason, it is critical to understand how infection in mosquitoes influences biting. To answer this question, we deployed a multidisciplinary approach including high-resolution, multivariate biting behavior monitoring on mice, in vivo transmission assay, and mathematical modeling. We demonstrated that infected mosquitoes are more attracted to mice and bite more often to get the same amount of blood as uninfected mosquitoes. While the effect of increased attraction to host on transmission capacity is trivial, we showed that increased number of bites results in successive transmission. Eventually, we calculated that the infection-induced behavior changes tripled transmission capacity of mosquitoes. Mosquito blood-feeding behavior is a key determinant of the epidemiology of dengue viruses (DENV), the most-prevalent mosquito-borne viruses. However, despite its importance, how DENV infection influences mosquito blood-feeding and, consequently, transmission remains unclear. Here, we developed a high-resolution, video-based assay to observe the blood-feeding behavior of Aedes aegypti mosquitoes on mice. We then applied multivariate analysis on the high-throughput, unbiased data generated from the assay to ordinate behavioral parameters into complex behaviors. We showed that DENV infection increases mosquito attraction to the host and hinders its biting efficiency, the latter resulting in the infected mosquitoes biting more to reach similar blood repletion as uninfected mosquitoes. To examine how increased biting influences DENV transmission to the host, we established an in vivo transmission model with immuno-competent mice and demonstrated that successive short probes result in multiple transmissions. Finally, to determine how DENV-induced alterations of host-seeking and biting behaviors influence dengue epidemiology, we integrated the behavioral data within a mathematical model. We calculated that the number of infected hosts per infected mosquito, as determined by the reproduction rate, tripled when mosquito behavior was influenced by DENV infection. Taken together, this multidisciplinary study details how DENV infection modulates mosquito blood-feeding behavior to increase vector capacity, proportionally aggravating DENV epidemiology. By elucidating the contribution of mosquito behavioral alterations on DENV transmission to the host, these results will inform epidemiological modeling to tailor improved interventions against dengue.
Nephron mass determines the excretion rate of urinary extracellular vesicles

Abstract Urinary extracellular vesicles (uEVs) are emerging as non‐invasive biomarkers for various kidney diseases, but it is unknown how differences in nephron mass impact uEV excretion. To address this, uEV excretion was measured before and after human kidney donor nephrectomy and rat nephrectomy. In male and female donors, uEVs were quantified in cell‐free spot and 24‐h urine samples using nanoparticle tracking analysis (NTA), EVQuant, and CD9‐time‐resolved fluorescence immunoassay. Female donors had significantly lower total kidney volume (TKV) and excreted 49% fewer uEVs than male donors. uEV excretion correlated positively with estimated glomerular filtration rate (eGFR), creatinine clearance, and TKV (R's between 0.6 and 0.7). uEV excretion rate could also be predicted from spot urines after multiplying spot uEV/creatinine by 24‐h urine creatinine. Donor nephrectomy reduced eGFR by 36% ± 10%, but the excretion of uEVs by only 16% (CD9+ uEVs ‐37%, CD9‐ uEVs no decrease). Donor nephrectomy increased the podocyte marker WT‐1 and the proximal tubule markers NHE3, NaPi‐IIa, and cubilin in uEVs two‐ to four‐fold when correcting for the nephrectomy. In rats, the changes in GFR and kidney weight correlated with the changes in uEV excretion rate (R = 0.46 and 0.60, P < 0.01). Furthermore, the estimated degree of hypertrophy matched the change in uEV excretion rate (1.4‐ to 1.5‐fold after uninephrectomy and four‐fold after 5/6th nephrectomy). Taken together, our data show that uEV excretion depends on nephron mass, and that nephrectomy reduces uEV excretion less than expected based on nephron loss due to compensatory hypertrophy. The major implication of our findings is that a measure for nephron mass or uEV excretion rate should be included when comparing uEV biomarkers between individuals.
Optimal high-dimensional and nonparametric distributed testing under communication constraints

We derive minimax testing errors in a distributed framework where the data is split over multiple machines and their communication to a central machine is limited to $b$ bits. We investigate both the $d$- and infinite-dimensional signal detection problem under Gaussian white noise. We also derive distributed testing algorithms reaching the theoretical lower bounds. Our results show that distributed testing is subject to fundamentally different phenomena that are not observed in distributed estimation. Among our findings, we show that testing protocols that have access to shared randomness can perform strictly better in some regimes than those that do not. We also observe that consistent nonparametric distributed testing is always possible, even with as little as $1$-bit of communication and the corresponding test outperforms the best local test using only the information available at a single local machine. Furthermore, we also derive adaptive nonparametric distributed testing strategies and the corresponding theoretical lower bounds.
A Survey on Retrieval-Augmented Text Generation

Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community. Compared with conventional generation models, retrieval-augmented text generation has remarkable advantages and particularly has achieved state-of-the-art performance in many NLP tasks. This paper aims to conduct a survey about retrieval-augmented text generation. It firstly highlights the generic paradigm of retrieval-augmented generation, and then it reviews notable approaches according to different tasks including dialogue response generation, machine translation, and other generation tasks. Finally, it points out some important directions on top of recent methods to facilitate future research.
Pop Quiz! Can a Large Language Model Help With Reverse Engineering?

Large language models (such as OpenAI's Codex) have demonstrated impressive zero-shot multi-task capabilities in the software domain, including code explanation. In this work, we examine if this ability can be used to help with reverse engineering. Specifically, we investigate prompting Codex to identify the purpose, capabilities, and important variable names or values from code, even when the code is produced through decompilation. Alongside an examination of the model's responses in answering open-ended questions, we devise a true/false quiz framework to characterize the performance of the language model. We present an extensive quantitative analysis of the measured performance of the language model on a set of program purpose identification and information extraction tasks: of the 136,260 questions we posed, it answered 72,754 correctly. A key takeaway is that while promising, LLMs are not yet ready for zero-shot reverse engineering.
X-Ray Coronal Properties of Swift/BAT-selected Seyfert 1 Active Galactic Nuclei

The corona is an integral component of active galactic nuclei (AGNs) which produces the bulk of the X-ray emission above 1–2 keV. However, many of its physical properties and the mechanisms powering this emission remain a mystery. In particular, the temperature of the coronal plasma has been difficult to constrain for large samples of AGNs, as constraints require high-quality broadband X-ray spectral coverage extending above 10 keV in order to measure the high-energy cutoff, which provides constraints on the combination of coronal optical depth and temperature. We present constraints on the coronal temperature for a large sample of Seyfert 1 AGNs selected from the Swift/BAT survey using high-quality hard X-ray data from the NuSTAR observatory combined with simultaneous soft X-ray data from Swift/XRT or XMM-Newton. When applying a physically motivated, nonrelativistic disk-reflection model to the X-ray spectra, we find a mean coronal temperature kT e = 84 ± 9 keV. We find no significant correlation between the coronal cutoff energy and accretion parameters such as the Eddington ratio and black hole mass. We also do not find a statistically significant correlation between the X-ray photon index, Γ, and Eddington ratio. This calls into question the use of such relations to infer properties of supermassive black hole systems.
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB

While DeepMind has tentatively solved protein folding, its inverse problem -- protein design which predicts protein sequences from their 3D structures -- still faces significant challenges. Particularly, the lack of large-scale standardized benchmark and poor accuray hinder the research progress. In order to standardize comparisons and draw more research interest, we use AlphaFold DB, one of the world's largest protein structure databases, to establish a new graph-based benchmark -- AlphaDesign. Based on AlphaDesign, we propose a new method called ADesign to improve accuracy by introducing protein angles as new features, using a simplified graph transformer encoder (SGT), and proposing a confidence-aware protein decoder (CPD). Meanwhile, SGT and CPD also improve model efficiency by simplifying the training and testing procedures. Experiments show that ADesign significantly outperforms previous graph models, e.g., the average accuracy is improved by 8\%, and the inference speed is 40+ times faster than before.
On the Effect of Log-Barrier Regularization in Decentralized Softmax Gradient Play in Multiagent Systems

Softmax policy gradient is a popular algorithm for policy optimization in single-agent reinforcement learning, particularly since projection is not needed for each gradient update. However, in multi-agent systems, the lack of central coordination introduces signiﬁcant additional diﬃculties in the convergence analysis. Even for a stochastic game with identical interest, there can be multiple Nash Equilibria (NEs), which disables proof techniques that rely on the existence of a unique global optimum. Moreover, the softmax parameterization introduces non-NE policies with zero gradient, making NE-seeking diﬃcult for gradient-based algorithms. In this paper, we study the ﬁnite time convergence of decentralized softmax gradient play in a special form of game, Markov Potential Games (MPGs), which includes the identical interest game as a special case. We investigate both gradient play and natural gradient play, with and without log-barrier regularization. Establishing convergence for the unregularized cases relies on an assumption that the stationary policies are isolated , and yields convergence bounds that contain a trajectory dependent constant that can be arbitrarily large . We introduce the log-barrier regularization to overcome these drawbacks, with the cost of slightly worse dependence on other factors such as the action set size. An empirical study on an identical interest matrix game conﬁrms the theoretical ﬁndings.
Ballistic transport in periodic and random media

We prove ballistic transport of all orders, that is, ‖xme−itHψ‖ ≍ t, for the following models: the adjacency matrix on Z, the Laplace operator on R, periodic Schrödinger operators on R, and discrete periodic Schrödinger operators on periodic graphs. In all cases we give the exact expression of the limit of ‖xme−itHψ‖/tm as t → +∞. We then move to universal covers of finite graphs (these are infinite trees) and prove ballistic transport in mean when the potential is lifted naturally, giving a periodic model, and when the tree is endowed with random i.i. d. potential, giving an Anderson model. The limiting distributions are then discussed, enriching the transport theory. Some general upper bounds are detailed in the appendix.
Hypothesis-Driven Automated Experiment in Scanning Probe Microscopy: Exploring the Domain Growth Laws in Ferroelectric Materials

We report the development and implementation of a hypothesis learning based automated experiment, in which the microscope operating in the autonomous mode identifies the physical laws behind the material's response. Specifically, we explore the bias induced transformations that underpin the functionality of broad classes of devices and functional materials from batteries and memristors to ferroelectrics and antiferroelectrics. Optimization and design of these materials require probing the mechanisms of these transformations on the nanometer scale as a function of the broad range of control parameters such as applied potential and time, often leading to experimentally intractable scenarios. At the same time, often the behaviors of these systems are understood within potentially competing theoretical models, or hypotheses. Here, we develop a hypothesis list that covers the possible limiting scenarios for the domain growth, including thermodynamic, domain wall pinning, and screening limited. We further develop and experimentally implement the hypothesis driven automated experiment in Piezoresponse Force Microscopy, autonomously identifying the mechanisms of the bias induced domain switching. This approach can be applied for a broad range of physical and chemical experiments with relatively low dimensional control parameter space and for which the possible competing models of the system behavior that ideally cover the full range of physical eventualities are known or can be created. These include other scanning probe microscopy modalities such as force distance curve measurements and nanoindentation, as well as materials synthesis and optimization.
Axisymmetric gravity currents in anisotropic porous media

Abstract We explore the motion of an axisymmetric gravity current in an anisotropic porous medium in which the horizontal permeability is larger than the vertical permeability. It is well known that the classical axisymmetric gravity current supplied by a constant point source of fluid has an unphysical singularity near the origin. We address this by considering a pressure-dominated region near the origin which allows for vertical flow from the source, such that the current remains of finite depth, whilst beyond this region the flow is gravity dominated. At early times the inner pressure-driven region controls the spreading of the current, but at late times the inner region occupies a progressively smaller fraction of the current such that the radius increases as ${\sim }t^{3/7}$, while the depth near the origin increases approximately as ${\sim }t^{1/7}$. The presence of anisotropy highlights this phenomenon, since the vertical permeability maintains an effect on the flow at late times through the pressure-driven flow near the origin. Using these results we provide some quantitative insights into the dominant dynamics which controls CO$_2$ migration through permeable aquifers, as occurs in the context of carbon capture and storage.
Weighing cosmic structures with clusters of galaxies and the intergalactic medium

We present an analysis aimed at combining cosmological constraints from number counts of galaxy clusters identified through the Sunyaev-Zeldovich effect, obtained with the South Pole Telescope (SPT), and from Lyman-α spectra obtained with the MIKE/HIRES and X-shooter spectrographs. The SPT cluster analysis relies on mass calibration based on weak lensing measurements, while the Lyman-α analysis is built over mock spectra extracted from hydrodynamical simulations. The resulting constraints exhibit a tension (∼3.3σ) between the low σ8 values preferred by the low-redshift cluster data, $\sigma _8=0.74 ^{+0.03}_{-0.04}$, and the higher one preferred by the high-redshift Lyman-α data, $\sigma _8=0.91 ^{+0.03}_{-0.03}$. We present a detailed analysis to understand the origin of this tension and to establish whether it arises from systematic uncertainties related to the assumptions underlying the analyses of cluster counts and/or Lyman-α forest. We found this tension to be robust with respect to the choice of modelling of the IGM, even when including possible systematics from unaccounted sub-Damped Lyman-α (DLA) and Lyman-limit systems (LLS) in the Lyman-α data. We conclude that to solve this tension would require a large bias on the cluster mass estimate, or large unaccounted errors on the Lyman-α mean fluxes. Our results have important implications for future analyses based on cluster number counts from future large photometric surveys (e.g. Euclid and LSST) and on larger samples of high-redshift quasar spectra (e.g. DESI and WEAVE surveys). If confirmed at the much higher statistical significance reachable by such surveys, this tension could represent a significant challenge for the standard ΛCDM paradigm.
Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing

Task-oriented semantic parsing models have achieved strong results in recent years, but unfortunately do not strike an appealing balance between model size, runtime latency, and cross-domain generalizability. We tackle this problem by introducing scenario-based semantic parsing: a variant of the original task which first requires disambiguating an utterance’s “scenario” (an intent-slot template with variable leaf spans) before generating its frame, complete with ontology and utterance tokens. This formulation enables us to isolate coarse-grained and fine-grained aspects of the task, each of which we solve with off-the-shelf neural modules, also optimizing for the axes outlined above. Concretely, we create a Retrieve-and-Fill (RAF) architecture comprised of (1) a retrieval module which ranks the best scenario given an utterance and (2) a filling module which imputes spans into the scenario to create the frame. Our model is modular, differentiable, interpretable, and allows us to garner extra supervision from scenarios. RAF achieves strong results in high-resource, low-resource, and multilingual settings, outperforming recent approaches by wide margins despite, using base pre-trained encoders, small sequence lengths, and parallel decoding.
Exploring the consequences of cyber attacks on Powertrain Cyber Physical Systems

This paper proposes a novel approach for the study of cyber-attacks against the powertrain of a generic vehicle. The proposed model is composed of a generic Internal Combustion engine and a speed controller, that communicate through a Controller Area Network (CAN) bus. We consider a threat model composed of three representative attack scenarios designed to modify the output of the model, thus affecting the rotational speed of the engine. Two attack scenarios target both vehicle sensor systems and CAN communication, while one attack scenario only requires injection of CAN messages. To the best of our knowledge, this is the first attempt of modeling the consequences of realistic cyber attacks against a modern vehicle.
VOS: Learning What You Don't Know by Virtual Outlier Synthesis

Out-of-distribution (OOD) detection has received much attention lately due to its importance in the safe deployment of neural networks. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on OOD data. Previous approaches rely on real outlier datasets for model regularization, which can be costly and sometimes infeasible to obtain in practice. In this paper, we present VOS, a novel framework for OOD detection by adaptively synthesizing virtual outliers that can meaningfully regularize the model's decision boundary during training. Specifically, VOS samples virtual outliers from the low-likelihood region of the class-conditional distribution estimated in the feature space. Alongside, we introduce a novel unknown-aware training objective, which contrastively shapes the uncertainty space between the ID data and synthesized outlier data. VOS achieves competitive performance on both object detection and image classification models, reducing the FPR95 by up to 9.36% compared to the previous best method on object detectors. Code is available at https://github.com/deeplearning-wisc/vos.
Streaming Multi-Talker ASR with Token-Level Serialized Output Training

This paper proposes a token-level serialized output training (t-SOT), a novel framework for streaming multi-talker automatic speech recognition (ASR). Unlike existing streaming multi-talker ASR models using multiple output branches, the t-SOT model has only a single output branch that generates recognition tokens (e.g., words, subwords) of multiple speakers in chronological order based on their emission times. A special token that indicates the change of ``virtual'' output channels is introduced to keep track of the overlapping utterances. Compared to the prior streaming multi-talker ASR models, the t-SOT model has the advantages of less inference cost and a simpler model architecture. Moreover, in our experiments with LibriSpeechMix and LibriCSS datasets, the t-SOT-based transformer transducer model achieves the state-of-the-art word error rates by a significant margin to the prior results. For non-overlapping speech, the t-SOT model is on par with a single-talker ASR model in terms of both accuracy and computational cost, opening the door for deploying one model for both single- and multi-talker scenarios.
Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers

Much of recent Deep Reinforcement Learning success is owed to the neural architecture's potential to learn and use effective internal representations of the world. While many current algorithms access a simulator to train with a large amount of data, in realistic settings, including while playing games that may be played against people, collecting experience can be quite costly. In this paper, we introduce a deep reinforcement learning architecture whose purpose is to increase sample efficiency without sacrificing performance. We design this architecture by incorporating advances achieved in recent years in the field of Natural Language Processing and Computer Vision. Specifically, we propose a visually attentive model that uses transformers to learn a self-attention mechanism on the feature maps of the state representation, while simultaneously optimizing return. We demonstrate empirically that this architecture improves sample complexity for several Atari environments, while also achieving better performance in some of the games.