Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameterized Reasoning for Distributed Systems with Consensus

Programmers make building distributed systems tractable by using a wide variety of abstractions to compose complex systems out of smaller components---for example, managing coordination between distributed processes using libraries that provide locking or consensus. Unfortunately, verification techniques for distributed systems typically do not match this style of programming, and modeling distributed systems often requires erasing these layers of abstraction to represent an entire program as a monolithic whole. This paper presents a new approach to automatically verifying distributed systems that use consensus as a component. We first define a new system model that offers a carefully designed consensus abstraction to provide global semantics for consensus while eliding specific algorithmic details. We then present a sound and relatively complete parameterized verification procedure for safety properties of distributed systems that use consensus. We further show how to use this procedure to synthesize correct systems given partial process definitions. We build a tool, Treaty, and show on several case studies that we are able to successfully synthesize and verify distributed systems that build on consensus without having to reason directly about the internals of consensus.
State-Dependent Regulation of Cortical Processing Speed via Gain Modulation

To thrive in dynamic environments, animals must be capable of rapidly and flexibly adapting behavioral responses to a changing context and internal state. Examples of behavioral flexibility include faster stimulus responses when attentive and slower responses when distracted. Contextual or state-dependent modulations may occur early in the cortical hierarchy and may be implemented via top-down projections from corticocortical or neuromodulatory pathways. However, the computational mechanisms mediating the effects of such projections are not known. Here, we introduce a theoretical framework to classify the effects of cell type-specific top-down perturbations on the information processing speed of cortical circuits. Our theory demonstrates that perturbation effects on stimulus processing can be predicted by intrinsic gain modulation, which controls the timescale of the circuit dynamics. Our theory leads to counterintuitive effects, such as improved performance with increased input variance. We tested the model predictions using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where we found that a decrease in single-cell intrinsic gain during locomotion led to an acceleration of visual processing. Our results establish a novel theory of cell type-specific perturbations, applicable to top-down modulation as well as optogenetic and pharmacological manipulations. Our theory links connectivity, dynamics, and information processing via gain modulation. SIGNIFICANCE STATEMENT To thrive in dynamic environments, animals adapt their behavior to changing circumstances and different internal states. Examples of behavioral flexibility include faster responses to sensory stimuli when attentive and slower responses when distracted. Previous work suggested that contextual modulations may be implemented via top-down inputs to sensory cortex coming from higher brain areas or neuromodulatory pathways. Here, we introduce a theory explaining how the speed at which sensory cortex processes incoming information is adjusted by changes in these top-down projections, which control the timescale of neural activity. We tested our model predictions in freely running mice, revealing that locomotion accelerates visual processing. Our theory is applicable to internal modulation as well as optogenetic and pharmacological manipulations and links circuit connectivity, dynamics, and information processing.
Near-Optimal Decremental SSSP in Dense Weighted Digraphs

In the decremental Single-Source Shortest Path problem (SSSP), we are given a weighted directed graph $G= (V, E, w)$ undergoing edge deletions and a source vertex $r\in V$; let $n=\vert V\vert, m=\vert E\vert$ and $W$ be the aspect ratio of the graph. The goal is to obtain a data structure that maintains shortest paths from $r$ to all vertices in $V$ and can answer distance queries in $O(1)$ time, as well as return the corresponding path $P$ in $O(\vert P\vert)$ time. This problem was first considered by Even and Shiloach [JACM'81], who provided an algorithm with total update time $O(mn)$ for unweighted undirected graphs; this was later extended to directed weighted graphs [FOCS'95, STOC'99]. There are conditional lower bounds showing that $O(mn)$ is in fact near-optimal [ESA'04, FOCS'14, STOC'15, STOC'20]. In a breakthrough result, Forster et al. showed that total update time $\min\{m^{7/6}n^{2/3+o(1)}, m^{3/4}n^{5/4+o(1)}\} \text{polylog}(W)= mn^{0.9+o(1)}\text{polylog} (W)$, is possible if the algorithm is allowed to return ($1 +\epsilon$)-approximate paths, instead of exact ones [STOC'14, ICALP'15]. No further progress was made until Probst Gutenberg and Wulff-Nilsen [SODA'20] provided a new approach for the problem, which yields total time $\tilde{O}(\min\{m^{2/3}n^{4/3}\log W, (mn)^{7/8}\log W\})= \tilde{O}(\min\{n^{8/3}\log W,\ mn^{3/4}\log W\})$. Our result builds on this recent approach, but overcomes its limitations by introducing a significantly more powerful abstraction, as well as a different core subroutine. Our new framework yields a decremental ($1+\epsilon$)-approximate SSSP data structure with total update time $\tilde{O}(n^{2} \log^{4}W/\epsilon)$. Our algorithm is thus near-optimal for dense graphs with polynomial edge-weights. Our framework can also be applied to sparse graphs to obtain total update time $\tilde{O}(mn^{2/3} \log^{3}W/\epsilon)$. Combined, these data structures dominate all previous results. Like all previous $o(mn)$ algorithms that can return a path (not just a distance estimate), our result is randomized and assumes an oblivious adversary. Our framework effectively allows us to reduce SSSP in general graphs to the same problem in directed acyclic graphs (DAGs). We believe that our framework has significant potential to influence future work on directed SSSP, both in the dynamic model and in others.
Saliency-based Weighted Multi-label Linear Discriminant Analysis

In this paper, we propose a new variant of Linear Discriminant Analysis (LDA) to solve multi-label classification tasks. The proposed method is based on a probabilistic model for defining the weights of individual samples in a weighted multi-label LDA approach. Linear Discriminant Analysis is a classical statistical machine learning method, which aims to find a linear data transformation increasing class discrimination in an optimal discriminant subspace. Traditional LDA sets assumptions related to Gaussian class distributions and single-label data annotations. To employ the LDA technique in multi-label classification problems, we exploit intuitions coming from a probabilistic interpretation of class saliency to redefine the between-class and within-class scatter matrices. The saliency-based weights obtained based on various kinds of affinity encoding prior information are used to reveal the probability of each instance to be salient for each of its classes in the multi-label problem at hand. The proposed Saliency-based weighted Multi-label LDA approach is shown to lead to performance improvements in various multi-label classification problems.
On the Ethics of Building AI in a Responsible Manner

The AI-alignment problem arises when there is a discrepancy between the goals that a human designer specifies to an AI learner and a potential catastrophic outcome that does not reflect what the human designer really wants. We argue that a formalism of AI alignment that does not distinguish between strategic and agnostic misalignments is not useful, as it deems all technology as un-safe. We propose a definition of a strategic-AI-alignment and prove that most machine learning algorithms that are being used in practice today do not suffer from the strategic-AI-alignment problem. However, without being careful, today's technology might lead to strategic misalignment.
Model-based actor-critic: GAN + DRL (actor-critic) => AGI

Our effort is toward unifying GAN and DRL algorithms into a unifying AI model (AGI or general-purpose AI or artificial general intelligence which has general-purpose applications to: (A) offline learning (of stored data) like GAN in (un/semi-/fully-)SL setting such as big data analytics (mining) and visualization; (B) online learning (of real or simulated devices) like DRL in RL setting (with/out environment reward) such as (real or simulated) robotics and control; Our core proposal is adding an (generative/predictive) environment model to the actor-critic (model-free) architecture which results in a model-based actor-critic architecture with temporal-differencing (TD) error and an episodic memory. The proposed AI model is similar to (model-free) DDPG and therefore it's called model-based DDPG. To evaluate it, we compare it with (model-free) DDPG by applying them both to a variety (wide range) of independent simulated robotic and control task environments in OpenAI Gym and Unity Agents. Our initial limited experiments show that DRL and GAN in model-based actor-critic results in an incremental goal-driven intellignce required to solve each task with similar performance to (model-free) DDPG. Our future focus is to investigate the proposed AI model potential to: (A) unify DRL field inside AI by producing competitive performance compared to the best of model-based (PlaNet) and model-free (D4PG) approaches; (B) bridge the gap between AI and robotics communities by solving the important problem of reward engineering with learning the reward function by demonstration;
Storing Set Families More Compactly with Top ZDDs

Zero-suppressed Binary Decision Diagrams (ZDDs) are data structures for representing set families in a compressed form. With ZDDs, many valuable operations on set families can be done in time polynomial in ZDD size. In some cases, however, the size of ZDDs for representing large set families becomes too huge to store them in the main memory. This paper proposes top ZDD, a novel representation of ZDDs which uses less space than existing ones. The top ZDD is an extension of the top tree, which compresses trees, to compress directed acyclic graphs by sharing identical subgraphs. We prove that navigational operations on ZDDs can be done in time poly-logarithmic in ZDD size, and show that there exist set families for which the size of the top ZDD is exponentially smaller than that of the ZDD. We also show experimentally that our top ZDDs have smaller sizes than ZDDs for real data.
On the asymptotical regularization for linear inverse problems in presence of white noise

We interpret steady linear statistical inverse problems as artificial dynamic systems with white noise and introduce a stochastic differential equation (SDE) system where the inverse of the ending time $T$ naturally plays the role of the squared noise level. The time-continuous framework then allows us to apply classical methods from data assimilation, namely the Kalman-Bucy filter and 3DVAR, and to analyze their behaviour as a regularization method for the original problem. Such treatment offers some connections to the famous asymptotical regularization method, which has not yet been analyzed in the context of random noise. We derive error bounds for both methods in terms of the mean-squared error under standard assumptions and discuss commonalities and differences between both approaches. If an additional tuning parameter $\alpha$ for the initial covariance is chosen appropriately in terms of the ending time $T$, one of the proposed methods gains order optimality. Our results extend theoretical findings in the discrete setting given in the recent paper Iglesias et al. (2017). Numerical examples confirm our theoretical results.
The pandemic of viruses with a long incubation phase in the small world

A model of the spread of viruses in selected city and in a network of cities is considered, taking into account the delay caused by the long incubation period of the virus. The effect of delay effects is shown in comparison with pandemics without such delay. A temporary asymmetry of the spread of infection has been identified, which means that the time for a pandemic to develop significantly exceeds the time for its completion. Model calculations of the spread of viruses in a network of interconnected large and small cities were carried out, and dynamics features were revealed in comparison with the spread of viruses in a single city, including the possibility of reinfection of megalopolis.
Discrimination against or in favor of qubits in quantum theory

Within context of quantum logic, it is possible to assign dispersion-free probabilities to experimental propositions pertaining to qubits. This makes qubits distinct from the rest of quantum systems since the latter do not admit probabilities having only values 0 and 1. The present paper shows that erasing qubit discrimination leads to a model of computation which permits execution of many primitive operations in a massive parallel way. In the paper, it is demonstrated that such a model (that can be called a quantum parallel random-access machine, QPRAM) is quantum mechanically plausible.
Music Artist Classification with WaveNet Classifier for Raw Waveform Audio Data

Models for music artist classification usually were operated in the frequency domain, in which the input audio samples are processed by the spectral transformation. The WaveNet architecture, originally designed for speech and music generation. In this paper, we propose an end-to-end architecture in the time domain for this task. A WaveNet classifier was introduced which directly models the features from a raw audio waveform. The WaveNet takes the waveform as the input and several downsampling layers are subsequent to discriminate which artist the input belongs to. In addition, the proposed method is applied to singer identification. The model achieving the best performance obtains an average F1 score of 0.854 on benchmark dataset of Artist20, which is a significant improvement over the related works. In order to show the effectiveness of feature learning of the proposed method, the bottleneck layer of the model is visualized.
ARCH: Animatable Reconstruction of Clothed Humans

In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. Existing approaches to digitize 3D humans struggle to handle pose variations and recover details. Also, they do not produce models that are animation ready. In contrast, ARCH is a learned pose-aware model that produces detailed 3D rigged full-body human avatars from a single unconstrained RGB image. A Semantic Space and a Semantic Deformation Field are created using a parametric 3D body estimator. They allow the transformation of 2D/3D clothed humans into a canonical space, reducing ambiguities in geometry caused by pose variations and occlusions in training data. Detailed surface geometry and appearance are learned using an implicit function representation with spatial local features. Furthermore, we propose additional per-pixel supervision on the 3D reconstruction using opacity-aware differentiable rendering. Our experiments indicate that ARCH increases the fidelity of the reconstructed humans. We obtain more than 50% lower reconstruction errors for standard metrics compared to state-of-the-art methods on public datasets. We also show numerous qualitative examples of animated, high-quality reconstructed avatars unseen in the literature so far.
MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion

Robots and other smart devices need efficient object-based scene representations from their on-board vision systems to reason about contact, physics and occlusion. Recognized precise object models will play an important role alongside non-parametric reconstructions of unrecognized structures. We present a system which can estimate the accurate poses of multiple known objects in contact and occlusion from real-time, embodied multi-view vision. Our approach makes 3D object pose proposals from single RGB-D views, accumulates pose estimates and non-parametric occupancy information from multiple views as the camera moves, and performs joint optimization to estimate consistent, non-intersecting poses for multiple objects in contact. We verify the accuracy and robustness of our approach experimentally on 2 object datasets: YCB-Video, and our own challenging Cluttered YCB-Video. We demonstrate a real-time robotics application where a robot arm precisely and orderly disassembles complicated piles of objects, using only on-board RGB-D vision.
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks

Abstract Time series modeling has entered an era of unprecedented growth in the size and complexity of data which require new modeling approaches. While many new general purpose machine learning approaches have emerged, they remain poorly understand and irreconcilable with more traditional statistical modeling approaches. We present a general class of exponential smoothed recurrent neural networks (RNNs) which are well suited to modeling nonstationary dynamical systems arising in industrial applications. In particular, we analyze their capacity to characterize the nonlinear partial autocorrelation structure of time series and directly capture dynamic effects such as seasonality and trends. Application of exponentially smoothed RNNs to forecasting electricity load, weather data, and stock prices highlight the efficacy of exponential smoothing of the hidden state for multistep time series forecasting. The results also suggest that popular, but more complicated neural network architectures originally designed for speech processing are likely over-engineered for industrial forecasting and light-weight exponentially smoothed architectures, trained in a fraction of the time, capture the salient features while being superior and more robust than simple RNNs and autoregressive models. Additionally, uncertainty quantification of Bayesian exponential smoothed RNNs is shown to provide improved coverage.
On the electrostatic potential and electric field of a uniformly charged disk

We calculate the electrostatic potential and electric field of a uniformly charged disk everywhere in space. This electrostatic problem was solved long ago, and its gravitational analogue was solved even earlier. However, it seems that physics students are not aware of the solution, because it is not presented in textbooks. The purpose of the present article is to fill this gap in the pedagogical literature.
Report of Gnathophausia ingens (Dohrn, 1870) from bathypelagic region of Bay of Bengal, corroborated by DNA barcoding and 18S rRNA gene sequencing

Gnathophausia ingens is a cosmopolitan bathypelagic large mysid belonging to the family Gnathophausiidae of the order Lophogastrida. Occurrence of this species in Bay of Bengal is reported in this paper. DNA barcodes as well as18S rRNA gene sequences were used to confirm the identity and to delineate the phylogeny of G. ingens . The mitochondrial CO1 sequence shared 97% similarity with G. ingens previously sequenced from deepsea waters of Canada. Phylogram precisely clustered G. ingens isolated from Bay of Bengal with the same species sequenced from Gulf of Mexico proving the similarity shared in DNA sequences despite the geographical distance. The sequences produced in this study will act as benchmark for precise identification of G. ingens from Indian waters.
JCB_200811147 727..742

The Rockefeller University Press $30.00 J. Cell Biol. Vol. 185 No. 4 727–741 www.jcb. org/cgi/doi/10.1083/jcb.200811147 JCB 727 F. Yang and H. -S. Je contributed equally to this paper. Correspondence to Bai Lu: bailu@mail.nih. gov Abbreviations used in this paper: -BTX, -bungarotoxin; AChR, acetylcholine receptor; BDNF, brain-derived neurotrophic factor; CNS, central nervous system; COM, center of mass; ESC, evoked synaptic current; LTP, long-term potentiation; mBDNF, mature BDNF; MMP, matrix metalloprotease; NMJ, neuromuscular junction; p75NTR, pan-neurotrophin receptor p75; ROI, region of interest; SSC, spontaneous synaptic current; Trk, tropomyosin-related kinase. Introduction
Does routine exercise induce stress and in consequence serum amyloid A (SAA) response in purebred Arabian racehorses

This study aimed to evaluate whether a stress response to exercise increases the serum amyloid-A (SAA) production in purebred Arabian race horses. Eleven healthy Arabian horses aged 3 to 5 years were examined. All the horses were subjected to the same effort test consisting in 1200m gallop at a speed typical for the daily training programme. Four venous blood samples were collected, namely: at rest (T0), immediately after the end of the exercise (T1), after 30 minutes of rest (T2), and after 24 hours from the first sampling (T3). Serum levels of ACTH, cortisol, and SAA were estimated by the ELISA method. To evaluate a relative intensity of exercise, the plasma lactate (LA) concentration and serum lactic dehydrogenase (LDH) activity were determined. The results obtained revealed that the type of exercise used caused a statistically significant short-term increase in the LA and LDH values. Similarly, the exercise induced a HPA axis response by a significant increase in ACTH and cortisol at T1 (P≤0.01). Then, the LA and hormones concentrations decreased significantly over one of the post-training sampling periods (T2) (P≤0.05 and P≤0.01, respectively). No statistically significant differences were observed in the SAA serum level during the respective steps of the experiment (T0–T3). In conclusion, the exercise-induced stress clearly affected the HPA axis response in horses by transient raising the ACTH and cortisol concentration. In contrast, the routine training session did not promote significant changes in the SAA level in studied horses.
PREVENTING CHRONIC DISEASE

Introduction Retailer noncompliance with limited US tobacco regulations on advertising and labeling was historically patterned by neighborhood in ways that promote health disparities. In 2010, the US Food and Drug Administration (FDA) began enforcing stronger tobacco retailer regulations under the Family Smoking Prevention and Tobacco Control Act of 2009. However, recent research has found no differences in compliance by neighborhood characteristics for FDA advertising and labeling inspections. We sought to investigate the neighborhood characteristics associated with retailer noncompliance with specific FDA advertising and labeling inspections (ie, violations of bans on self-service displays, selling single cigarettes, false or mislabeled products, vending machines, flavored cigarettes, and free samples). Methods We coded FDA advertising and labeling warning letters (n = 718) for type of violations and geocoded advertising and labeling inspections from January 1 through July 31, 2014 (N = 33,543). Using multilevel models, we examined cross-sectional associations between types of violations and neighborhood characteristics previously associated with disparities (ie, percentage black, Latino, under the poverty line, and younger than 18 years). Results Retailer advertising and labeling violations are patterned by who lives in the neighborhood; regulated tobacco products are more likely to be stored behind the counter as the percentage of black or Latino residents increases, and single cigarettes are more often available for purchase in neighborhoods as the percentage of black, poor, or young residents increases. Conclusion Contrary to previous null findings, noncompliance with FDA advertising and labeling regulations is patterned by neighborhood characteristics, sometimes in opposite directions. Given the low likelihood of self-service violations in the same neighborhoods that have high likelihood of single cigarette sales, we suggest targeted approaches to FDA retailer inspections and education campaigns.