Miracle-dz/UniFAR-FTUs huggingface dataset:
Relax the Non-Collusion Assumption for Multi-Server PIR

—For distributed protocols involving many servers, assuming that they do not collude with each other makes some secrecy problems solvable and reduces overheads and computa- tional hardness assumptions in others. While the non-collusion assumption is pervasive among privacy-preserving systems, it re- mains highly susceptible to covert, undetectable collusion among computing parties. This work stems from an observation that if the number of available computing parties is much higher than the number of parties required to perform a secure computation, collusion attempts could be deterred. We focus on the standard problem of multi-server private information retrieval (PIR) that inherently assumes that servers do not collude. For PIR application scenarios, such as those for blockchain light clients, where the available servers are plentiful, a single server’s deviating action is not tremendously beneﬁcial to itself. We can make deviations undesired through small amounts of rewards and penalties, thus raising the bar for collusion signiﬁcantly. For any given multi-server 1-private PIR (i.e., the base PIR scheme is constructed assuming no pairwise collusion), we provide a collusion mitigation mechanism. We ﬁrst deﬁne a two-stage sequential game that captures how rational servers interact with each other during collusion, then determine the payment rules such that the game realizes the unique sequential equilibrium: a non-collusion outcome. We also offer privacy protection for an extended period after the query executions, and guarantee user compensation in case of a reported privacy breach. Overall, we conjecture that the incentive structure for collusion mitigation to be functional towards relaxing the strong non-collusion assumptions across a variety of multi-party computation tasks.
A Taxonomy of HTML5 Canvas Bugs

—The HTML5 <canvas> is widely used to display high quality graphics in web applications. However, the combination of web, GUI, and visual techniques that are required to build <canvas> applications, together with the lack of testing and debugging tools, makes developing such applications very challenging. To help direct future research on testing <canvas> applications, in this paper we present a taxonomy of <canvas> bugs. First, we extracted 2,403 <canvas> -related bug reports from 123 open source GitHub projects that use the HTML5 <canvas> . Second, we constructed our taxonomy by manually classifying a random sample of 332 bug reports. Our manual classiﬁcation identiﬁed ﬁve broad categories of <canvas> bugs, such as Visual and Performance bugs. We found that Visual bugs are the most frequent (35%), while Performance bugs are relatively infrequent (5%). We also found that many <canvas> bugs that present themselves visually on the <canvas> are actually caused by other components of the web application. Our taxonomy of <canvas> bugs can be used to steer future research into <canvas> bugs and testing.
The Evolution of Assistive Technology: A Literature Review of Technology Developments and Applications

The term “Assistive Technology” (AT) has evolved over the years and identifies equipment or product systems, whether acquired, modified, or customized, that are used to increase, maintain, or improve functional capabilities of individuals with disabilities. Considering the advances that have been made, what trends can be identified to provide evidence of the evolution of AT as devices that foster accessibility and empower users with different abilities? Through a systematic literature review, we identify research items that offer evidence of the evolution of the meaning, purpose, and applications of AT throughout the history. This paper provides evidence that AT evolved from products to improve functional capabilities of individuals with disabilities toward enabling technologies that facilitate tasks for people with different needs, abilities, gender, age, and culture. This evolution will lead to a positive demystification of the meaning and applications of AT toward broader usage acceptance among mainstream users.
NSGZero: Efficiently Learning Non-Exploitable Policy in Large-Scale Network Security Games with Neural Monte Carlo Tree Search

How resources are deployed to secure critical targets in networks can be modelled by Network Security Games (NSGs). While recent advances in deep learning (DL) provide a powerful approach to dealing with large-scale NSGs, DL methods such as NSG-NFSP suffer from the problem of data inefficiency. Furthermore, due to centralized control, they cannot scale to scenarios with a large number of resources. In this paper, we propose a novel DL-based method, NSGZero, to learn a non-exploitable policy in NSGs. NSGZero improves data efficiency by performing planning with neural Monte Carlo Tree Search (MCTS). Our main contributions are threefold. First, we design deep neural networks (DNNs) to perform neural MCTS in NSGs. Second, we enable neural MCTS with decentralized control, making NSGZero applicable to NSGs with many resources. Third, we provide an efficient learning paradigm, to achieve joint training of the DNNs in NSGZero. Compared to state-of-the-art algorithms, our method achieves significantly better data efficiency and scalability.
Including STDP to eligibility propagation in multi-layer recurrent spiking neural networks

Spiking neural networks (SNNs) in neuromorphic systems are more energy efficient compared to deep learning–based methods, but there is no clear competitive learning algorithm for training such SNNs. Eligibility propagation (e-prop) offers an efficient and biologically plausible way to train competitive recurrent SNNs in low-power neuromorphic hardware. In this report, previous performance of e-prop on a speech classification task is reproduced, and the effects of including STDP-like behavior are analyzed. Including STDP to the ALIF neuron model improves the classification performance, but this is not the case for the Izhikevich e-prop neuron. Finally, it was found that e-prop implemented in a singlelayer recurrent SNN consistently outperforms a multi-layer variant.
Simplified Algorithms for Order-Based Core Maintenance

Graph analytics attract much attention from both research and industry communities. Due to the linear time complexity, the $k$-core decomposition is widely used in many real-world applications such as biology, social networks, community detection, ecology, and information spreading. In many such applications, the data graphs continuously change over time. The changes correspond to edge insertion and removal. Instead of recomputing the $k$-core, which is time-consuming, we study how to maintain the $k$-core efficiently. That is, when inserting or deleting an edge, we need to identify the affected vertices by searching for more vertices. The state-of-the-art order-based method maintains an order, the so-called $k$-order, among all vertices, which can significantly reduce the searching space. However, this order-based method is complicated for understanding and implementation, and its correctness is not formally discussed. In this work, we propose a simplified order-based approach by introducing the classical Order Data Structure to maintain the $k$-order, which significantly improves the worst-case time complexity for both edge insertion and removal algorithms. Also, our simplified method is intuitive to understand and implement; it is easy to argue the correctness formally. Additionally, we discuss a simplified batch insertion approach. The experiments evaluate our simplified method over 12 real and synthetic graphs with billions of vertices. Compared with the existing method, our simplified approach achieves high speedups up to 7.7x and 9.7x for edge insertion and removal, respectively.
Spatiotemporal Modeling of Nursery Habitat Using Bayesian Inference: Environmental Drivers of Juvenile Blue Crab Abundance

Nursery grounds provide conditions favorable for growth and survival of juvenile fish and crustaceans through abundant food resources and refugia, and enhance secondary production of populations. While small-scale studies remain important tools to assess nursery value of structured habitats and environmental factors, targeted applications that unify survey data over large spatial and temporal scales are vital to generalize inference of nursery function, identify highly productive regions, and inform management strategies. Using 21 years of spatio-temporally indexed survey data (i.e., water chemistry, turbidity, blue crab, and predator abundance) and GIS information on potential nursery habitats (i.e., seagrass, salt marsh, and unvegetated shallow bottom), we constructed five Bayesian hierarchical models with varying spatial and temporal dependence structures to infer variation in nursery habitat value for young juveniles (20–40 mm carapace width) of the blue crab Callinectes sapidus within three tributaries (James, York and Rappahannock Rivers) in lower Chesapeake Bay. Out-of-sample predictions of juvenile blue crab counts from a model considering fully nonseparable spatiotemporal dependence outperformed predictions from simpler models. Salt marsh surface area and turbidity were the strongest determinants of crab abundance (positive association in both cases). Highest crab abundances occurred near the turbidity maximum where relative salt marsh area was greatest. Relative seagrass area, which has been emphasized as the most valuable nursery in studies conducted at small spatial scales, was not associated with high crab abundance within the three tributaries. Hence, salt marshes should be considered a key nursery habitat for the blue crab, even where extensive seagrass beds occur. The patterns between juvenile blue crab abundance and environmental variables also indicated that identification of nurseries should be based on investigations at broad spatial and temporal scales incorporating multiple potential nursery habitats, and based on statistical analyses that address spatial and temporal statistical dependence.
Antiferromagnetic fluctuations and dominant 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mi>y</mml:mi></mml:mrow></mml:msub></mml:math>
-wave pairing symmetry in nickelate-based superconductors

Motivated by recent experimental studies on superconductivity found in nickelate-based materials, we study the temperature dependence of the spin correlation and the superconducting pairing interaction within an effective two-band Hubbard model by the quantum Monte Carlo method. Based on parameters extracted from first-principles calculations, our intensive numerical results reveal that the pairing with a $d_{xy}$-wave symmetry firmly dominates over other pairings at low temperature, which is mainly determined by the Ni 3$d$ orbital. It is also found that the effective pairing interaction is enhanced as the on-site interaction increases, demonstrating that the superconductivity is driven by strong electron-electron correlation. Even though the $(\pi,\pi)$ antiferromagnetic correlation could be enhanced by electronic interaction, there is no evidence for long-range antiferromagnetic order exhibited in nickelate-based superconductors. Moreover, our results offer possible evidence that the pure electron correlation may not account for the charge density wave state observed in nickelates.
Consistency of MLE for partially observed diffusions, with application in market microstructure modeling

This paper presents a tractable sufficient condition for the consistency of maximum likelihood estimators (MLEs) in partially observed diffusion models, stated in terms of stationary distribution of the associated fully observed diffusion, under the assumption that the set of unknown parameter values is finite. This sufficient condition is then verified in the context of a latent price model of market microstructure, yielding consistency of maximum likelihood estimators of the unknown parameters in this model. Finally, we compute the latter estimators using historical financial data taken from the NASDAQ exchange.
Molecular and Materials Basic Ontology: Development and First Steps

Advanced materials and their applications have become a key field of research, and it looks like this trend is not going to change soon. For that reason, the need for systematic and efficient methods for organizing knowledge in the field and conduct computational or experimental investigations is stronger than ever. In this work, we present a basic implementation of MAMBO - an ontology for molecular materials and their applications in real-life scenarios. The development of MAMBO has been guided by the needs of the research community involved in the development of novel materials with functional properties, with particular attention to the nanoscale. MAMBO aims at extending the current work in the field, while retaining a modular nature in order to allow straightforward extension of concepts and relations to neighboring domains. Our work is expected to enable the systematic integration of computational and experimental data in specific domains of interest (nanomaterials, molecular materials, organic an polymeric materials, supramolecular and bio-organic systems, etc.). Moreover, MAMBO is developed with a strong focus on the applications of data-driven frameworks for the design of novel materials with tailored characteristics.
Coupled support tensor machine classification for multimodal neuroimaging data

Multimodal data arise in various applications where information about the same phenomenon is acquired from multiple sensors and across different imaging modalities. Learning from multimodal data is of great interest in machine learning and statistics research as this offers the possibility of capturing complementary information among modalities. Multimodal modeling helps to explain the interdependence between heterogeneous data sources, discovers new insights that may not be available from a single modality, and improves decision‐making. Recently, coupled matrix–tensor factorization has been introduced for multimodal data fusion to jointly estimate latent factors and identify complex interdependence among the latent factors. However, most of the prior work on coupled matrix–tensor factors focuses on unsupervised learning and there is little work on supervised learning using the jointly estimated latent factors. This paper considers the multimodal tensor data classification problem. A coupled support tensor machine (C‐STM) built upon the latent factors jointly estimated from the advanced coupled matrix–tensor factorization is proposed. C‐STM combines individual and shared latent factors with multiple kernels and estimates a maximal‐margin classifier for coupled matrix–tensor data. The classification risk of C‐STM is shown to converge to the optimal Bayes risk, making it a statistically consistent rule. C‐STM is validated through simulation studies as well as a simultaneous analysis on electroencephalography with functional magnetic resonance imaging data. The empirical evidence shows that C‐STM can utilize information from multiple sources and provide a better classification performance than traditional single‐mode classifiers.
Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning

In this work, we investigate stochastic quasi-Newton methods for minimizing a finite sum of cost functions over a decentralized network. We first develop a general algorithmic framework, in which each node constructs a local, inexact quasi-Newton direction that asymptotically approaches the global, exact one at each time step. To do so, a local gradient approximation is constructed using dynamic average consensus to track the average of variance-reduced local stochastic gradients over the entire network, followed by a proper local Hessian inverse approximation. We show that under standard convexity and smoothness assumptions on the cost functions, the methods obtained from our framework converge linearly to the optimal solution if the local Hessian inverse approximations used have uniformly bounded positive eigenvalues. To construct the Hessian inverse approximations with the said boundedness property, we design two fully decentralized stochastic quasi-Newton methods—namely, the damped regularized limited-memory DFP (Davidon-Fletcher-Powell) and the damped limited-memory BFGS (Broyden-Fletcher-Goldfarb-Shanno)—which use a fixed moving window of past local gradient approximations and local decision variables to adaptively construct Hessian inverse approximations. A noteworthy feature of these methods is that they do not require extra sampling or communication. Numerical results show that the proposed decentralized stochastic quasi-Newton methods are much faster than the existing decentralized stochastic first-order algorithms.
Landau-Zener formula and non-secular transitions

The relationship between the Landau–Zener model (which describes transitions between energy levels for a time-dependent Hamiltonian) and non-secular master equations is discussed. This approach allows to describe the widely discussed in the literature on quantum photosynthesis resonance of the energy of the vibron and the diﬀerence of exciton energies for transitions between exciton states coupled to the vibron. but by presence of vibrons — special modes of vibration of nuclei in molecules. Vibrons are generated with excitation of electronic states according to the Franck–Condon principle — upon exciton excitation, the nuclei are in a nonequilibrium state and begin to vibrate. This causes transitions between electronic states according to the Landau–Zener mechanism. These transitions can be seen as generated by non-secular terms in the master equation.
Spatio-Temporal Analysis of SINR Meta Distribution for mmWave Heterogeneous Networks Under Geo/G/1 Queues

A fine-grained analysis of network performance is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference-plus-noise-ratio (SINR) in the mmWave heterogeneous networks where the base stations (BS) in each tier are modeled as a Poisson point process (PPP). By utilizing stochastic geometry and queueing theory, we characterize the spatial and temporal randomness while the special characteristics of mmWave communications, including different path loss laws for line-of-sight and non-line-of-sight links and directional beamforming, are incorporated into the analysis. We derive the moments of the conditional successful transmission probability (STP). By taking the temporal random arrival of traffic into consideration, an equation on the meta distribution is formulated and the meta distribution can be obtained in a recursive manner. The numerical results reveal the impact of the key network parameters, such as the SINR threshold and the blockage parameter, on the network performance.
Parallel database search and prime factorization with magnonic holographic memory devices

In this work, we describe the capabilities of Magnonic Holographic Memory (MHM) for parallel database search and prime factorization. MHM is a type of holographic device, which utilizes spin waves for data transfer and processing. Its operation is based on the correlation between the phases and the amplitudes of the input spin waves and the output inductive voltage. The input of MHM is provided by the phased array of spin wave generating elements allowing the producing of phase patterns of an arbitrary form. The latter makes it possible to code logic states into the phases of propagating waves and exploit wave superposition for parallel data processing. We present the results of numerical modeling illustrating parallel database search and prime factorization. The results of numerical simulations on the database search are in agreement with the available experimental data. The use of classical wave interference may results in a significant speedup over the conventional digital logic circuits in special tas.
Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks

Despite the rapid progress of neuromorphic computing, the inadequate depth and the resulting insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice. Residual learning and shortcuts have been evidenced as an important approach for training deep neural networks, but rarely did previous work assess their applicability to the characteristics of spike-based communication and spatiotemporal dynamics. This negligence leads to impeded information flow and the accompanying degradation problem. In this paper, we identify the crux and then propose a novel residual block for SNNs, which is able to significantly extend the depth of directly trained SNNs, e.g., up to 482 layers on CIFAR-10 and 104 layers on ImageNet, without observing any slight degradation problem. We validate the effectiveness of our methods on both frame-based and neuromorphic datasets, and our SRM-ResNet104 achieves a superior result of 76.02% accuracy on ImageNet, the first time in the domain of directly trained SNNs. The great energy efficiency is estimated and the resulting networks need on average only one spike per neuron for classifying an input sample. We believe our powerful and scalable modeling will provide a strong support for further exploration of SNNs.
Bayesian active learning for closed-loop synaptic characterization

Model ﬁtting methods have been widely used in neuroscience to infer the parameters of a biophysical system from its responses to experimental stimulations. For instance, the parameters of a chemical synapse can be estimated from its postsynaptic responses to evoked stimuli. However, these estimates critically depend on the stimulation protocol being used. Experiments are often conducted with non-adaptative stimulation protocols that may not yield enough information about the parameters. Here, we propose using Bayesian active learning (BAL) for synaptic characterization, and to choose the most informative stimuli by maximizing the mutual information between the data and the unknown parameters. This requires performing high-dimensional integrations and optimizations in real time. Current methods are either too time consuming, or only applicable to speciﬁc models. We build on recent developments in non-linear ﬁltering and parallel computing to provide a general framework for online BAL, which is fast enough to be used in real-time biological experiments and can be applied to a wide range of statistical models. Using synthetic data, we show that our method has the potential to signiﬁ-cantly improve the precision of inferred synaptic parameters. Finally, we explore the situation where the constraint is not given by the total number of observations but by the duration of the experiment.
Autoencoding Video Latents for Adversarial Video Generation

Given the three dimensional complexity of a video signal, training a robust and diverse GAN based video generative model is onerous due to large stochasticity involved in data space. Learning disentangled representations of the data help to improve robustness and provide control in the sampling process. For video generation, there is a recent progress in this area by considering motion and appearance as orthogonal information and designing architectures that efficiently disentangle them. These approaches rely on handcrafting architectures that impose structural priors on the generator to decompose appearance and motion codes in the latent space. Inspired from the recent advancements in the autoencoder based image generation, we present AVLAE (Adversarial Video Latent AutoEncoder) which is a two stream latent autoencoder where the video distribution is learned by adversarial training. In particular, we propose to autoencode the motion and appearance latent vectors of the video generator in the adversarial setting. We demonstrate that our approach learns to disentangle motion and appearance codes even without the explicit structural composition in the generator. Several experiments with qualitative and quantitative results demonstrate the effectiveness of our method.