Miracle-dz/UniFAR-FTUs huggingface dataset:
A multi-channel TMS system enabling accurate stimulus orientation control during concurrent ultra-high-field MRI for preclinical applications

Monitoring cortical responses to neuromodulation protocols on preclinical models can elucidate fundamental mechanisms of brain function. Concurrent brain stimulation and imaging is challenging, usually compromising spatiotemporal resolution, accuracy, and versatility. Here we report a non-invasive brain stimulation system with electronic control of neuromodulation parameters in a 9.4-T magnetic resonance imaging (MRI) environment. In the imaging scanner, transcranial magnetic stimulation is delivered with a set of two coils and the MRI signals are recorded with a radiofrequency coil. The coil set provides millisecond-scale electronic control of the stimulus orientation with 1° resolution. Without physically rotating the coils, we evoked orientation-specific muscle responses after cortical stimulation on an anesthetized rat. We show that the stimulation pulses do not affect the anatomical imaging quality, and imaging signals are disrupted only if recorded before 10 ms after pulse delivery. Concurrent electronically targeted brain stimulation and neuroimaging sets the stage for the causal investigation of whole-brain network functions, endorsing more efficient treatment protocols.
Amyloidosis and ageing have differential effects on microglial Ca2+ activity in the mouse brain

In microglia, changes in intracellular calcium concentration ([Ca2+]i) may regulate process motility, inflammasome activation, and phagocytosis. However, while neurons and astrocytes exhibit frequent spontaneous Ca2+ activity, microglial Ca2+ signals are much rarer and poorly understood. Here, we studied [Ca2+]i changes of microglia in acute brain slices using Fluo-4–loaded cells and mice expressing GCaMP5g in microglia. Spontaneous Ca2+ transients occurred ∼5 times more frequently in individual microglial processes than in their somata. We assessed whether microglial Ca2+ responses change in Alzheimer’s disease (AD) using AppNL- G-F knock-in mice. Proximity to Aβ plaques strongly affected microglial Ca2+ activity. Although spontaneous Ca2+ transients were unaffected in microglial processes, they were 5-fold more frequent in microglial somata near Aβ plaques than in wild-type microglia. Microglia away from Aβ plaques in AD mice showed intermediate properties for morphology and Ca2+ responses, partly resembling those of wild-type microglia. By contrast, somatic Ca2+ responses evoked by tissue damage were less intense in microglia near Aβ plaques than in wild-type microglia, suggesting different mechanisms underlying spontaneous vs. damage-evoked Ca2+ signals. Finally, as similar processes occur in neurodegeneration and old age, we studied whether ageing affected microglial [Ca2+]i. Somatic damage-evoked Ca2+ responses were greatly reduced in microglia from old mice, as in the AD mice. In contrast to AD, however, old age did not alter the occurrence of spontaneous Ca2+ signals in microglial somata but reduced the rate of events in processes. Thus, we demonstrate distinct compartmentalised Ca2+ activity in microglia from healthy, aged and AD-like brains.
Bacterial degradation of ctenophore Mnemiopsis leidyi organic matter

Blooms of gelatinous zooplankton, an important source of protein-rich biomass in coastal waters, often collapse rapidly, releasing large amounts of labile detrital organic matter (OM) into the surrounding water. Although these blooms have the potential to cause major perturbations in the marine ecosystem, their effects on the microbial community and hence on the biogeochemical cycles have yet to be elucidated. We conducted microcosm experiments simulating the scenario experienced by coastal bacterial communities after the decay of a ctenophore (Mnemiopsis leidyi) bloom in the northern Adriatic Sea. Within 24 h, a rapid response of bacterial communities to the M. leidyi OM was observed, characterized by elevated bacterial biomass production and respiration rates. Compared to our previous microcosm study of jellyfish (Aurelia aurita s.l.), M. leidyi OM degradation revealed that, despite the fundamental genetic and biochemical differences between the two species, a similar pattern in the bacterial community response was observed. Combined metagenomic and metaproteomic analysis indicated that the degradation activity was mainly performed by Pseudoalteromonas, producing a large amount of proteolytic exoenzymes and exhibiting high metabolic activity. Interestingly, the reconstructed metagenome-assembled genome (MAG) of Pseudoalteromonas phenolica was almost identical (average nucleotide identity >99%) to the MAG previously reconstructed in our A. aurita microcosm study. Taken together our data suggest that blooms of different gelatinous zooplankton are likely triggering a consistent response from natural bacterial communities, with specific bacterial lineages driving the remineralization of the gelatinous OM. Importance Jellyfish blooms are increasingly becoming a recurring seasonal event in marine ecosystems, characterised by a rapid build-up of gelatinous biomass that collapses rapidly. Although these blooms have the potential to cause major perturbations, their impact on marine microbial communities is largely unknown. We conducted an incubation experiment simulating a bloom of the ctenophore Mnemiopsis leidyi in the Northern Adriatic, where we investigated the bacterial response to the gelatinous biomass. We found that the bacterial communities actively degraded the gelatinous organic matter, and overall showed a striking similarity to the dynamics previously observed after a simulated bloom of the jellyfish Aurelia aurita s.l. In both cases we found that a single bacterial species, Pseudoalteromonas phenolica, was responsible for most of the degradation activity. This suggests that blooms of different jellyfish are likely to trigger a consistent response from natural bacterial communities, with specific bacterial species driving the remineralisation of gelatinous biomass.
Content Sharing Design for Social Welfare in Networked Disclosure Game

This work models the costs and beneﬁts of personal information sharing, or self-disclosure, in online social networks as a networked disclosure game. In a networked population where edges represent visibility amongst users, we assume a leader can inﬂuence network structure through content promotion, and we seek to optimize social welfare through network design. Our approach considers user interaction non-homogeneously, where pairwise engagement amongst users can involve or not involve sharing personal information. We prove that this problem is NP-hard. As a solution, we develop a Mixed-integer Linear Programming algorithm, which can achieve an exact solution, and also develop a time-efﬁcient heuristic algo-rithm that can be used at scale. We conduct numerical experiments to demonstrate the properties of the algorithms and map theoretical results to a dataset of posts and comments in 2020 and 2021 in a COVID-related Subreddit community where privacy risks and sharing tradeoffs were particularly pronounced
Establishing Markov equivalence in cyclic directed graphs

We present a new, efficient procedure to establish Markov equivalence between directed graphs that may or may not contain cycles under the \textit{d}-separation criterion. It is based on the Cyclic Equivalence Theorem (CET) in the seminal works on cyclic models by Thomas Richardson in the mid '90s, but now rephrased from an ancestral perspective. The resulting characterization leads to a procedure for establishing Markov equivalence between graphs that no longer requires tests for d-separation, leading to a significantly reduced algorithmic complexity. The conceptually simplified characterization may help to reinvigorate theoretical research towards sound and complete cyclic discovery in the presence of latent confounders. This version includes a correction to rule (iv) in Theorem 1, and the subsequent adjustment in part 2 of Algorithm 2.
A constrained Bayesian approach to out-of-distribution prediction

Consider the problem of out-of-distribution prediction given data from multiple environments. While a sufﬁciently diverse collection of training environments will facilitate the identiﬁcation of an invariant predictor, with an optimal generalization performance, many applications only provide us with a limited number of environments. It is thus necessary to consider adapting to distribution shift using a handful of labeled test samples. We propose a constrained Bayesian approach for this task, which restricts to models with a worst-group training loss above a prespeciﬁed threshold. Our method avoids a pathology of the standard Bayesian posterior, which occurs when spurious correlations improve in-distribution prediction. We also show that on certain high-dimensional linear problems, constrained modeling improves the sample efﬁciency of adaptation. Synthetic and real-world experiments demonstrate the robust performance of our approach.
GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming

The latest two-stage optimization framework based on graph neural network (GNN) and large neighborhood search (LNS) is the most popular framework in solving large-scale integer programs (IPs). However, the framework can not effectively use the embedding spatial information in GNN and still highly relies on large-scale solvers in LNS, resulting in the scale of IP being limited by the ability of the current solver and performance bottlenecks. To handle these issues, this paper presents a GNN&GBDT-guided fast optimizing framework for large-scale IPs that only uses a small-scale optimizer to solve large-scale IPs efficiently. Specifically, the proposed framework can be divided into three stages: Multi-task GNN Embedding to generate the embedding space, GBDT Prediction to effectively use the embedding spatial information, and Neighborhood Optimization to solve large-scale problems fast using the small-scale optimizer. Extensive experiments show that the proposed framework can solve IPs with millions of scales and surpass SCIP and Gurobi in the specified wall-clock time using only a small-scale optimizer with 30% of the problem size. It also shows that the proposed framework can save 99% of running time in achieving the same solution quality as SCIP, which verifies the effectiveness and efficiency of the proposed framework in solving large-scale IPs.
The diverse electronic properties of C/BN heteronanotubes with polar discontinuity

Linear interfaces from within the heteronanotubes between carbon nanotubes (CNTs) and h-BN nanotubes (BNNTs) introduce unexpected electronic and spintronic properties that are different from individual components. In particular, for heteronanotubes with zigzag boundaries (ZCBNNTs) polar discontinuities occur at the interfaces, generating an intrinsic electric field acting on the segment CNTs. In turn, the electric field will cause a charge transfer between the two interfaces to compensate for the potential difference, thus the interface states (or boundary states) show new phenomena. For example, the half-metallic can be found in ZCBNNTs. Furthermore, the length and diameter of the ZCBNNTs can effectively adjust the electronic properties, due to the compensatory effects of the free charge to the polarization charge. Our results shed light on the future design of miniature superconducting spintronic devices and the realization of high performance nanoelectronic devices with negligible interface contact resistance barriers.
Adaptive Barrier Smoothing for First-Order Policy Gradient with Contact Dynamics

Differentiable physics-based simulators have witnessed remarkable success in robot learning involving contact dynamics, benefiting from their improved accuracy and efficiency in solving the underlying complementarity problem. However, when utilizing the First-Order Policy Gradient (FOPG) method, our theory indicates that the complementarity-based systems suffer from stiffness, leading to an explosion in the gradient variance of FOPG. As a result, optimization becomes challenging due to chaotic and non-smooth loss landscapes. To tackle this issue, we propose a novel approach called Adaptive Barrier Smooth-ing (ABS), which introduces a class of soft-ened complementarity systems that correspond to barrier-smoothed objectives. With a contact-aware adaptive central-path parameter, ABS reduces the FOPG gradient variance while controlling the gradient bias. We justify the adaptive design by analyzing the roots of the system’s stiffness. Additionally, we establish the convergence of FOPG and show that ABS achieves a reasonable trade-off between the gradient variance and bias by providing their upper bounds. Moreover, we present a variant of FOPG based on complementarity modeling that efficiently fits the contact dynamics by learning the physical parameters. Experimental results on various robotic tasks are provided to support our theory and method.
COLA: Orchestrating Error Coding and Learning for Robust Neural Network Inference Against Hardware Defects

Error correcting output codes (ECOCs) have been proposed to improve the robustness of deep neural networks (DNNs) against hardware defects of DNN hardware accelerators. Unfortunately, existing efforts suffer from drawbacks that would greatly impact their practicality: 1) robust accuracy (with defects) improvement at the cost of degraded clean accuracy (without defects); 2) no guarantee on better robust or clean accuracy using stronger ECOCs. In this paper, we first shed light on the connection between these drawbacks and error correlation, and then propose a novel comprehensive error decorrelation framework, namely COLA . Specifically, we propose to reduce inner layer feature error correlation by 1) adopting a separated architecture, where the last portions of the paths to all output nodes are separated, and 2) orthogonalizing weights in common DNN layers so that the intermediate features are orthogonal with each other. We also propose a regularization technique based on total correlation to mitigate overall error correlation at the outputs. The effectiveness of COLA is first analyzed theoretically, and then evaluated experimentally, e.g., up to 6 . 7% clean accuracy improvement compared with the original DNNs and up to 40% robust accuracy improvement compared to the state-of-the-art ECOC-enhanced DNNs.
Summary of the FESAC Transformative Enabling Capabilities Panel Report

Abstract The U.S. Fusion Energy Sciences Advisory Committee was charged “to identify the most promising transformative enabling capabilities (TEC) for the U.S. to pursue that could promote efficient advance toward fusion energy, building on burning plasma science and technology. ” A subcommittee of U.S. technical experts was formed and received community input in the form of white papers and presentations on the charge questions. The subcommittee identified four “most promising transformative enabling capabilities”: 1. advanced algorithms 2. high critical temperature superconductors 3. advanced materials and manufacturing 4. novel technologies for tritium fuel cycle control. In addition, one second-tier TEC, defined as a “promising transformative enabling capability,” was identified: fast-flowing liquid-metal plasma-facing components. Each of these TECs presents a tremendous opportunity to accelerate fusion science and technology toward power production. Dedicated investment in these TECs for fusion systems is needed to capitalize on the rapid advances being made for a variety of nonfusion applications to fully realize their transformative potential for fusion energy.
Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models

We study a new framework of learning mixture models via automatic clustering called P RESTO , wherein we optimize a joint objective function on the model parameters and the partitioning, with each model tailored to perform well on its specific cluster. In contrast to prior work, we do not assume any generative model for the data. We convert our training problem to a joint parameter estimation and a subset selection problem, subject to a matroid span constraint. This allows us to reduce our problem into a constrained set function minimization problem, where the underlying objective is monotone and approximately submodular. We then propose a new joint discrete-continuous optimization algorithm which achieves a bounded approximation guarantee for our problem. We show that P RESTO outperforms several alternative methods. Finally, we study P RESTO in the context of resource efficient deep learning, where we train smaller resource constrained models on each partition and show that it outperforms existing data partitioning and model pruning/knowledge distillation approaches, which in contrast to P RESTO , require large initial (teacher) models.
SLAMB: Accelerated Large Batch Training with Sparse Communication

Distributed training of large deep neural networks requires frequent exchange of massive data be-tween machines, thus communication efficiency is a major concern. Existing compressed communication methods are either not compatible with large batch optimization algorithms, or do not provide sufficient speedup in large scale. In this paper, we combine sparsification-based gradient compression with the layer-wise adaptive moments optimizer for large batch training (LAMB). We propose SLAMB, a novel communication-efficient optimizer that supports large batch sizes and scales to thousands of GPUs. SLAMB employs momentum masking, local error compensation, and element-wise adaptive rescaling to achieve accurate layer-wise weight updates, which translates to fast convergence for very large batches. Our empirical results show that, compared to the state-of-the-art, SLAMB transmits half the amount of data in large-batch BERT pre-training, without sacrificing accuracy. Moreover, SLAMB achieves excellent scalability in large computing infrastructures. For instance, SLAMB with 128 GPUs reduces the training time of Swin Transformer pre-training on ImageNet to 5.35 hours, which is 2 hours faster than the state-of-the-art. At the extreme, we trained BERT-XL (2.8B parameters) on 1,024 NVIDIA A100 GPUs, where SLAMB achieved 90% scaling efficiency.
Online estimation of similarity matrices with incomplete data

The similarity matrix measures pairwise similarities between a set of data points and is an essential concept in data processing, routinely used in practical applications. Obtaining a similarity matrix is typically straightforward when data points are completely observed. However, incomplete observations can make it challenging to obtain a high-quality similarity matrix, which becomes even more complex in online data. To address this challenge, we propose matrix correction algorithms that leverage the positive semi-deﬁniteness (PSD) of the similarity matrix to improve similarity estimation in both ofﬂine and online scenarios. Our approaches have a solid theoretical guarantee of performance and excellent potential for parallel execution on large-scale data. Empirical evaluations demonstrate their high effectiveness and efﬁciency with signiﬁcantly improved results over classical imputation-based methods, beneﬁting downstream applications with superior performance. Our code is available at https: //github.com/CUHKSZ-Yu/OnMC .
On the decomposition of tabular knowledge systems

Recently there has been a growing interest in the decomposition of knowledge based systems and decision tables. Much work in this area has adopted an informal approach. In this paper, we first formalize the notion of decomposition, and then we study some interesting classes of decompositions. The proposed classification can be used to formulate design goals to master the decomposition of large decision tables into smaller components. Importantly, carrying out a decomposition eliminates redundant information from the knowledge base, thereby taking away -right from the beginning -a possible source of inconsistency. This, in tum, tenders subsequent verification and validation more smoothly.
The Growth of Community Colleges in the American States: An Application of Count Models to Institutional Growth

Background/Context The establishment of community colleges in the American states stands as one of the most unique features of our system of postsecondary education. Four possible explanations have been suggested for the growth of community colleges. An economic perspective argues that the development of community colleges came about as a result of increased demand. The sociological perspective argues that these institutions were developed as a result of broader social forces. The political science literature focuses on the role of lobbying and constituent demands. The organizational ecology literature suggests that community colleges fill a unique niche. Purpose/Objective/Research Question/Focus of Study We seek to understand why certain states created large systems of community colleges, whereas other states developed few or none of these institutions. Research Design We test theories from the literature using a multilevel Poisson regression model, using Bayesian methods for estimation and inference. Data Collection and Analysis The data for this study come from a unique data set, compiled from a variety of sources of state-level data. The data cover all 50 states for the years 1969–2002. We estimate four models: a complete-pooling model with no unit-specific controls, a no-pooling model with controls for each state, a partial pooling model that allows state effects to vary, and a partial pooling model with a state-specific time trend. Findings/Results Our results indicate support for the idea that community colleges grew in response to changes in state populations and that states with a large number of other types of institutions of higher education saw slower growth. Little support is found for theories regarding community colleges as engines of stratification. Conclusions/Recommendations This study provides support for the idea that the supply of higher education institutions is responsive to demand. Little support is found for the role of social stratification in the development of new institutions. Political forces do appear to play at least a small role in the expansion of institutions. Existing institutions may slow the growth of newer forms of postsecondary education.