Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance

The exponentially large discrete search space in mixed-precision quantization (MPQ) makes it hard to determine the optimal bit-width for each layer. Previous works usually resort to iterative search methods on the training set, which consume hundreds or even thousands of GPU-hours. In this study, we reveal that some unique learnable parameters in quantization, namely the scale factors in the quantizer, can serve as importance indicators of a layer, reflecting the contribution of that layer to the final accuracy at certain bit-widths. These importance indicators naturally perceive the numerical transformation during quantization-aware training, which can precisely provide quantization sensitivity metrics of layers. However, a deep network always contains hundreds of such indicators, and training them one by one would lead to an excessive time cost. To overcome this issue, we propose a joint training scheme that can obtain all indicators at once. It considerably speeds up the indicators training process by parallelizing the original sequential training processes. With these learned importance indicators, we formulate the MPQ search problem as a one-time integer linear programming (ILP) problem. That avoids the iterative search and significantly reduces search time without limiting the bit-width search space. For example, MPQ search on ResNet18 with our indicators takes only 0.06 s, which improves time efficiency exponentially compared to iterative search methods. Also, extensive experiments show our approach can achieve SOTA accuracy on ImageNet for far-ranging models with various constraints (e.g., BitOps, compress rate). Code is available on https://github.com/1hunters/LIMPQ.
Shepherd Pre-trained Language Models to Develop a Train of Thought: An Iterative Prompting Approach

While Pre-trained Language Models (PLMs) 001 internalize a great amount of world knowledge, 002 they have been shown incapable of recalling 003 these knowledge to solve tasks requiring com-004 plex & multi-step inference procedures. Simi-005 lar to how humans develop a “train of thought” 006 for these tasks, how can we equip PLMs with 007 such abilities? In this work, we explore an iter-008 ative prompting framework, a new prompting 009 paradigm which progressively elicits relevant 010 knowledge from PLMs for multi-step inference 011 tasks. We identify key limitations of existing 012 prompting methods, namely they are either re-013 stricted to queries with a single identifiable re-014 lation/predicate, or being agnostic to input con-015 texts, which makes it difficult to capture vari-016 abilities across different inference steps. We 017 propose an iterative context-aware prompter, 018 which addresses these limitations by learning 019 to dynamically synthesize prompts conditioned 020 on the current step’s contexts. Experiments on 021 three datasets involving multi-step inference 022 show the effectiveness of the iterative scheme 023 and the context-aware prompter design. 1 024
The Pierre Auger exotic events and axion quark nuggets

The Pierre Auger Observatory have reported [1–3] observation of several exotic cosmic ray -like events which apparently related to thunderstorms. These events are much larger in size than conventional cosmic ray (CR) events, and they have very distinct timing features. A possible nature of the observed phenomenon is still a matter of active research and debates as many unusual features of these exotic events are hard to explain. In particular, the frequency of appearance of these exotic events is very low (less than 2 events/year), in huge contrast with a typical rate of a conventional lightning strikes in the area. We propose that the observed exotic events can be explained within the so-called axion quark nugget (AQN) dark matter model. The idea is that the AQNs may trigger and initiate a special and unique class of lightning strikes during a thunderstorm as a result of ionization of the atmospheric molecules along its path. The corresponding AQN-induced lighting ﬂashes may show some speciﬁc features not shared by typical and much more frequent conventional ﬂashes. We support this proposal by demonstrating that the observations [1–3], including the frequency of appearance and time duration are consistent with observations. We also comment on possible relation of AUGER exotic events with the Telescope Array bursts and the terrestrial gamma ray ﬂashes. We list a number of features of the AQN-induced exotic events (such as speciﬁc radio pulses synchronized with these events) which can be directly tested by future experiments. We also suggest to use distributed acoustic sensing instruments to detect the acoustic pulses which must be synchronized with AUGER exotic events.
Three-dimensional simulation of unsteady flows in a pump-turbine during start-up transient up to speed no-load condition in generating mode

Three-dimensional numerical simulation for a prototype pump-turbine has been performed to investigate the transient characteristics and flow behaviors in the startup process. The rotational speed, which varies with runner torque and inertial of the rotating systems, is obtained by hydraulic-force coupling method. To simulate the guide vane motions, dynamic overset mesh technique is applied with good maintenance of mesh quality. Comparisons with the existing experimental results verify the validity of the current method in predicting the startup transient characteristics of a pump-turbine with good accuracy. During the startup transient, the guide vanes open according to a prescribed sequence, which brings in increasing flow rate to the unit. The runner accelerates due to growing water power energy exerted on the runner blades. When approaching the speed no-load condition, a ring-shaped flow enlacing the entire vaneless space, that we term “water-ring,” is found to block the through-flow along the circumference, and the pump-turbine features a prominent partial pump flow state in the runner, which equalizes the torque to no load. Thus, the runner rotational speed stops increasing and stabilizes toward rated speed. The prediction shows that the pressure fluctuation amplitude of the speed no-load can be 3.8 to 8 times that of the full load, presenting evidently stronger instability at the speed no-load.
Sharpness-Aware Minimization with Dynamic Reweighting

Deep neural networks are often overparameterized and may not easily achieve model generalization. Adversarial training has shown effectiveness in improving generalization by regularizing the change of loss on top of adversarially chosen perturbations. The recently proposed sharpness-aware minimization (SAM) algorithm conducts adversarial weight perturbation, encouraging the model to converge to a flat minima. SAM finds a common adversarial weight perturbation per-batch. Although per-instance adversarial weight perturbations are stronger adversaries and can potentially lead to better generalization performance, their computational cost is very high and thus it is impossible to use per-instance perturbations efficiently in SAM. In this paper, we tackle this efficiency bottleneck and propose sharpness-aware minimization with dynamic reweighting (delta-SAM). Our theoretical analysis motivates that it is possible to approach the stronger, per-instance adversarial weight perturbations using reweighted per-batch weight perturbations. delta-SAM dynamically reweights perturbation within each batch according to the theoretically principled weighting factors, serving as a good approximation to per-instance perturbation. Experiments on various natural language understanding tasks demonstrate the effectiveness of delta-SAM.
Motion Planning of Multi-Robots Object Transport with Deformable Sheet

—Using a deformable sheet to handle objects is con- venient and found in many practical applications. For object manipulation with multi-mobile robot team through deformable sheets, modeling of the robots-sheet-object system is a main challenge, since there are complex interactions among the robot team formation, deformable sheet and the object. Thus, in this paper, a virtual variable cables model (VVCM) is proposed to simplify the modeling of the robots-sheet-object system with N robots. With the VVCM, we can compute the kinematics of the robot team and manipulate the object with the changing of robot team formations. We further extend the VVCM to a motion planner with the capabilities of both bypassing and crossing obstacles, since the height of the object is controllable in this case. The motion planner can be formulated as a formation optimization problem. Then, local paths can be planned with the computed formation. Simulation and experimental results with different robot team sizes show the effectiveness and versatility of the proposed VVCM. The motion planner can successfully plan an obstacle crossing path in complex scenarios where other benchmark planners fail.
On the mixed-model analysis of covariance in cluster-randomized trials

In the analyses of cluster-randomized trials, mixed-model analysis of covariance (ANCOVA) is a standard approach for covariate adjustment and handling within-cluster correlations. However, when the normality, linearity, or the random-intercept assumption is violated, the validity and efficiency of the mixed-model ANCOVA estimators for estimating the average treatment effect remain unclear. Under the potential outcomes framework, we prove that the mixed-model ANCOVA estimators for the average treatment effect are consistent and asymptotically normal under arbitrary misspecification of its working model. If the probability of receiving treatment is 0.5 for each cluster, we further show that the model-based variance estimator under mixed-model ANCOVA1 (ANCOVA without treatment-covariate interactions) remains consistent, clarifying that the confidence interval given by standard software is asymptotically valid even under model misspecification. Beyond robustness, we discuss several insights on precision among classical methods for analyzing cluster-randomized trials, including the mixed-model ANCOVA, individual-level ANCOVA, and cluster-level ANCOVA estimators. These insights may inform the choice of methods in practice. Our analytical results and insights are illustrated via simulation studies and analyses of three cluster-randomized trials.
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning

In this paper, we present a reduction-based framework for conservative bandits and RL, in which our core technique is to calculate the necessary and sufficient budget obtained from running the baseline policy. For lower bounds, we improve the existing lower bound for conservative multi-armed bandits and obtain new lower bounds for conservative linear bandits, tabular RL and low-rank MDP, through a black-box reduction that turns a certain lower bound in the nonconservative setting into a new lower bound in the conservative setting. For upper bounds, in multi-armed bandits, linear bandits and tabular RL, our new upper bounds tighten or match existing ones with significantly simpler analyses. We also obtain a new upper bound for conservative low-rank MDP.
Modelling Gossip Interactions in Open Multi-Agent Systems

We consider open multi-agent systems, which are systems subject to frequent arrivals and departures of agents while the studied process takes place. We study the behavior of all-to-all pairwise gossip interactions in such open systems. Arrivals and departures of agents imply that the composition and size of the system evolve with time, and in particular prevent convergence. We describe the expected behavior of the system by showing that the evolution of scale-independent quantities can be characterized exactly by a fixed-size linear dynamical system. We apply this approach to characterize the evolution of the two first moments (and thus also of the variance) for open systems of fixed and variable size. Our approach is based on the continuous-time modelling of random asynchronous events impacting the systems (gossip steps, arrivals, departures, and replacements), and can be extended to other types of events.
The SARS-CoV-2 Delta variant induces an antibody response largely focused on class 1 and 2 antibody epitopes

Exposure histories to SARS-CoV-2 variants and vaccinations will shape the specificity of antibody responses. To understand the specificity of Delta-elicited antibody immunity, we characterize the polyclonal antibody response elicited by primary or mRNA vaccine-breakthrough Delta infections. Both types of infection elicit a neutralizing antibody response focused heavily on the receptor-binding domain (RBD). We use deep mutational scanning to show that mutations to the RBD’s class 1 and class 2 epitopes, including sites 417, 478, and 484–486 often reduce binding of these Delta-elicited antibodies. The anti-Delta antibody response is more similar to that elicited by early 2020 viruses than the Beta variant, with mutations to the class 1 and 2, but not class 3 epitopes, having the largest effects on polyclonal antibody binding. In addition, mutations to the class 1 epitope (e.g., K417N) tend to have larger effects on antibody binding and neutralization in the Delta spike than in the D614G spike, both for vaccine- and Delta-infection-elicited antibodies. These results help elucidate how the antigenic impacts of SARS-CoV-2 mutations depend on exposure history.
Revealing the microbial heritage of traditional Brazilian cheeses through metagenomics

Brazilian artisanal cheeses date from the first Portuguese settlers and evolved via local factors, resulting in unique products that are now part of the patrimony and identity of different Brazilian regions. In this study, we combined several culture-independent approaches, including 16S/ITS metagenetics, assembly- and deep profiling-metagenomics to characterize the originality of the microbiota of five varieties of Brazilian artisanal cheeses from the South and Southeast regions of Brazil. Their core microbiota contained mainly lactic acid bacteria (LAB), of which Lactococcus lactis subsp. lactis was the most frequent, followed by Streptococcus thermophilus in the South region. Moreover, several samples from the Southeast region contained, as dominant LAB, two other food Streptococci belonging to a new species of the salivarius group and S. infantarius. Rinds of samples from the Southeast region were dominated by the halotolerant bacterium Corynebacterium variabile and the yeasts Diutina catenulata and, to a lesser extent, by Debaryomyces hansenii and Kodamaea ohmeri. Rinds from the South region contained mainly LAB due to their short ripening time, and the predominant yeast was D. hansenii. Phylogenomic analysis based on L. lactis metagenome-assembled genomes (MAGs) showed that most Brazilian strains are closely related and form a different clade from those whose genomes are available at this time, indicating that they belong to a specific group. Lastly, functional analysis showed that S. infantarius acquired a ∼26 kb DNA fragment from S. thermophilus starter strains that carry the LacSZ system, allowing fast lactose assimilation, an adaptation advantage for growth in milk. Finally, our study identified several areas of concern, such as the presence of somatic cell DNA and high levels of antibiotic resistance genes in several cheese microbiota, implying that the milk used was from diseased herds. Overall, the data from this study highlight the potential value of the traditional and artisanal cheese production network in Brazil, and provide a metagenomic-based scheme to help manage this resource safely.
Direct and indirect neurogenesis generate a mosaic of distinct glutamatergic projection neuron types in cerebral cortex

Variations in size and complexity of the cerebral cortex result from differences in neuron number and composition, which are rooted in evolutionary changes in direct and indirect neurogenesis (dNG and iNG) mediated by radial glial progenitors and intermediate progenitors, respectively. How dNG and iNG differentially contribute to cortical neuronal number, diversity, and connectivity are unknown. Establishing a genetic fate-mapping method to differentially visualize dNG and iNG in mice, we found that while both dNG and iNG contribute to all cortical structures, iNG contributes the largest relative proportions to the hippocampus and neocortex compared to insular and piriform cortex, claustrum, and the pallial amygdala. Within the neocortex, whereas dNG generates all major glutamatergic projection neuron (PN) classes, iNG differentially amplifies and diversifies PNs within each class; the two neurogenic pathways generate distinct PN types and assemble fine mosaics of lineage-based cortical subnetworks. Our results establish a ground-level lineage framework for understanding cortical development and evolution by linking foundational progenitor types and neurogenic pathways to PN types. Highlights - A genetic strategy for differential visualization of direct and indirect neurogenesis in the same animal. - dNG and iNG differentially contribute to piriform cortex, basolateral amygdala, hippocampus, and neocortex - Whereas dNG generates all major PN classes, iNG differentially amplifies and diversifies PNs within each class - dNG and iNG construct distinct cortical projection subnetworks.
BrainQCNet: a Deep Learning attention-based model for multi-scale detection of artifacts in brain structural MRI scans

Analyses of structural MRI (sMRI) data depend on robust upstream data quality control (QC). It is also crucial that researchers retain the maximum amount of usable data to ensure reproducible, generalisable models. The time-consuming task of manual QC evaluation has prompted the development of tools for the automatic assessment of brain sMRI scans. Such tools are particularly valuable in this age of big data. One limitation of the most commonly used tools is that execution time is long, which poses a challenge in terms of duration and resource usage, particularly when processing large datasets. Further, evaluation is global (pass/fail) rather than localized. Having a tool that localizes areas of low quality could prevent unnecessary data loss. To address these issues, we trained a Deep Learning model, ProtoPNet, to classify minimally preprocessed 2D slices of scans that were manually annotated with a refined quality assessment (ABIDE 1 n = 980 scans). To validate the best model, we assessed 2141 ABCD scans for which gold-standard manual QC annotations were available. We obtained excellent accuracy: 82.4% for good quality scans (Pass), 91.4% for medium to low quality scans (Fail). Further validation using 799 scans from ABIDE 2 and 751 scans from ADHD-200 confirmed the reliability of our model. Accuracy was comparable to or exceeded that of another commonly used tool (MRIQC), but with dramatically reduced processing and prediction time (1 min per scan, GPU machine, CUDA-compatible). To facilitate faster and more accurate QC prediction for the neuroimaging community, we have shared the model that returned the most reliable global quality scores, local predictions of quality, and maps and prototypes of local artifacts as a BIDS-app (https://github.com/garciaml/BrainQCNet).
Combination treatment can hinder the evolution of resistance to antimicrobial peptides

Antibiotic resistant microbial pathogens are becoming a major threat to human health. Therefore, there is an urgent need to develop new alternatives to conventional antibiotics. One such promising alternative is antimicrobial peptides (AMPs), which are produced by virtually all organisms and typically inhibit bacteria via membrane disruption. However, previous studies demonstrated that bacteria can rapidly develop AMP resistance. Here, we study whether combination therapy, known to be able to inhibit the evolution of resistance to conventional antibiotics, can also hinder the evolution of AMP resistance. To do so, we evolved the opportunistic pathogen S. aureus in the presence of individual AMP, AMP pairs, and a combinatorial antimicrobial peptide library. Treatment with some AMP pair indeed hindered the evolution of resistance compared with individual AMPs. In particular, resistance to pairs was delayed when resistance to the individual AMPs came at a cost of impaired bacterial growth, and did not confer cross-resistance to other tested AMPs. The lowest level of resistance evolved during treatment with the combinatorial antimicrobial peptide library termed random antimicrobial peptide mixture, which contains more than a million different peptides. A better understanding of how AMP combinations affect the evolution of resistance is a crucial step in order to design ‘resistant proof’ AMPs cocktails that will offer a sustainable treatment option for antibiotic resistant pathogens.
Novel tuberculosis combination regimens of two and three-months therapy duration

The tuberculosis treatment duration is 4-6 months, but can be 18–20 months with multidrug resistance. Ultrashort regimens that cure regardless of resistance status [“pan-tuberculosis regimens”] would be a major step towards global tuberculosis control. Starting with seven drugs [bedaquiline, delamanid, pretomanid, OPC-167832, sutezolid, moxifloxacin, and pyrazinamide] in clinical testing, we calculated that there were thousands of possible novel combinations to evaluate. We used mathematical and pharmacokinetics/pharmacodynamic-based hollow fiber modeling to reduce this complexity to nine combinations, which we tested. Each regimen’s fast and slow growth bacteria kill-slope trajectories and times-to-extinction were estimated and translated to minimum required therapy duration in patients. Sputum microbial kill trajectories of two combination regimens that have undergone clinical testing were correctly predicted by this approach. Four pan-tuberculosis regimens were predicted to achieve relapse-free cure after 2-3 months therapy in patients. The kill-slopes were used to design expedited clinical trials that minimize patients’ risks.
Phyllochron duration and changes through rice development shape the vertical leaf size profile

Well-irrigated aerobic rice (Oryza sativa L.) plants are often shorter with smaller leaves, and the leaf appearance rate is slower in well-irrigated than in flooded environments. This observation questions the functional relationship between the leaf appearance rate, which is correlated with the apical cell production rate, and leaf length that is in turn correlated with the leaf division zone length. Meristem size, blade size, and appearance were monitored for all leaves from leaf 6 on the main stem of rice plants in field experiments using two varieties, two watering systems, and three sowing dates. Leaf blade and division zone lengths were correlated in all leaves of the main stem of rice plants. New stable linear relationships were found between the leaf division zone growth duration and internal cell production rate, which changed after leaf 11. These stable relationships imply a stable link between the rates of cell production in the stem apex and leaf meristem, explaining the vertical leaf size profile. Overall, faster leaf appearance leads to shorter leaves and organs in rice because of a functional link, which is counterbalanced by additional acclimation in transplanted plants.