Miracle-dz/UniFAR-FTUs huggingface dataset:
Dependence Model Assessment and Selection with DecoupleNets

Abstract Neural networks are suggested for learning a map from d-dimensional samples with any underlying dependence structure to multivariate uniformity in dimensions. This map, termed DecoupleNet, is used for dependence model assessment and selection. If the data-generating dependence model was known, and if it was among the few analytically tractable ones, one such transformation for is Rosenblatt’s transform. DecoupleNets have multiple advantages. For example, they only require an available sample and are applicable to , in particular . This allows for simpler model assessment and selection, both numerically and, because , especially graphically. A graphical assessment method has the advantage of being able to identify why, or in which region of the domain, a candidate model does not provide an adequate fit, thus, leading to model selection in particular regions of interest or improved model building strategies in such regions. Through simulation studies with data from various copulas, the feasibility and validity of this novel DecoupleNet approach is demonstrated. Applications to real world data illustrate its usefulness for model assessment and selection. Supplementary materials for this article are available online.
A Review of Modern Fashion Recommender Systems

The textile and apparel industries have grown tremendously over the past few years. Customers no longer have to visit many stores, stand in long queues, or try on garments in dressing rooms, as millions of products are now available in online catalogs. However, given the plethora of options available, an effective recommendation system is necessary to properly sort, order, and communicate relevant product material or information to users. Effective fashion recommender systems (RSs) can have a noticeable impact on billions of customers’ shopping experiences and increase sales and revenues on the provider side. The goal of this survey is to provide a review of RSs that operate in the specific vertical domain of garment and fashion products. We have identified the most pressing challenges in fashion RS research and created a taxonomy that categorizes the literature according to the objective they are trying to accomplish (e.g., item or outfit recommendation, size recommendation, and explainability, among others) and type of side information (users, items, context). We have also identified the most important evaluation goals and perspectives (outfit generation, outfit recommendation, pairing recommendation, and fill-in-the-blank outfit compatibility prediction) and the most commonly used datasets and evaluation metrics.
Variance reduced stochastic optimization over directed graphs with row and column stochastic weights

This paper proposes AB-SAGA, a first-order distributed stochastic optimization method to minimize a finite-sum of smooth and strongly convex functions distributed over an arbitrary directed graph. AB-SAGA removes the uncertainty caused by the stochastic gradients using a node-level variance reduction and subsequently employs network-level gradient tracking to address the data dissimilarity across the nodes. Unlike existing methods that use the nonlinear push-sum correction to cancel the imbalance caused by the directed communication, the consensus updates in AB-SAGA are linear and uses both row and column stochastic weights. We show that for a constant step-size, AB-SAGA converges linearly to the global optimal. We quantify the directed nature of the underlying graph using an explicit directivity constant and characterize the regimes in which AB-SAGA achieves a linear speed-up over its centralized counterpart. Numerical experiments illustrate the convergence of AB-SAGA for strongly convex and nonconvex problems.
Causal Disentanglement for Semantic-Aware Intent Learning in Recommendation

Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users’ true intent and thus deteriorate the recommendation effectiveness. Existing methods track this problem as eliminating bias for the robust recommendation, e.g., by re-weighting training samples or learning disentangled representations. The disentangled representation methods as the state-of-the-art eliminate bias by revealing cause-effect of the bias generation. However, how to design the semantic-aware and unbiased representations for users’ true intents is largely unexplored. To bridge the gap, we are the first to propose an unbiased and semantic-aware disentanglement learning called CaDSI(Causal Disentanglement for Semantics-Aware Intent Learning) from a causal perspective. Particularly, CaDSI explicitly models the causal relations underlying recommendation task, and thus produces semantic-aware representations via disentangling users’ true intents aware of specific item context. Moreover, the causal intervention mechanism is designed to eliminate confounding bias stemming from context information, which further aligns the semantic-aware representation with users’ true intent. Extensive experiments and case studies both validate the robustness and interpretability of our proposed model.
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences

Modern graph neural networks (GNNs) use a message passing scheme and have achieved great success in many fields. However, this recursive design inherently leads to excessive computation and memory requirements, making it not applicable to massive real-world graphs. In this work, we propose the Neighbor2Seq to transform the hierarchical neighborhood of each node into a sequence. This novel transformation enables the subsequent mini-batch training for general deep learning operations, such as convolution and attention, that are designed for grid-like data and are shown to be powerful in various domains. Therefore, our Neighbor2Seq naturally endows GNNs with the efficiency and advantages of deep learning operations on grid-like data by precomputing the Neighbor2Seq transformations. We evaluate our method on a massive graph, with more than 111 million nodes and 1.6 billion edges, as well as several medium-scale graphs. Results show that our proposed method is scalable to massive graphs and achieves superior performance across massive and medium-scale graphs. Our code is available at https://github.com/divelab/Neighbor2Seq.
PrivPAS: A real time Privacy-Preserving AI System and applied ethics

With 3.78 billion social media users worldwide in 2021 (48% of the human population), almost 3 billion images are shared daily. At the same time, a consistent evolution of smartphone cameras has led to a photography explosion with 85% of all new pictures being captured using smartphones. However, lately, there has been an increased discussion of privacy concerns when a person being photographed is unaware of the picture being taken or has reservations about the same being shared. These privacy violations are amplified for people with disabilities, who may find it challenging to raise dissent even if they are aware. Such unauthorized image captures may also be misused to gain sympathy by third-party organizations, leading to a privacy breach. Privacy for people with disabilities has so far received comparatively less attention from the AI community. This motivates us to work towards a solution to generate privacy-conscious cues for raising awareness in smartphone users of any sensitivity in their viewfinder content. To this end, we introduce PrivPAS (A real time Privacy-Preserving AI System) a novel framework to identify sensitive content. Additionally, we curate and annotate a dataset to identify and localize accessibility markers and classify whether an image is sensitive to a featured subject with a disability. We demonstrate that the proposed lightweight architecture, with a memory footprint of a mere 8.49MB, achieves a high mAP of 89.52% on resource-constrained devices. Furthermore, our pipeline, trained on face anonymized data. achieves an F1-score of 73.1%.
Quantized MIMO: Channel Capacity and Spectrospatial Power Distribution

Millimeter wave systems suffer from high power consumption and are constrained to use low resolution quantizers —digital to analog and analog to digital converters (DACs and ADCs). However, low resolution quantization leads to reduced data rate and increased out-of-band emission noise. In this paper, a multiple-input multiple-output (MIMO) system with linear transceivers using low resolution DACs and ADCs is considered. An information-theoretic analysis of the system to model the effect of quantization on spectrospatial power distribution and capacity of the system is provided. It is shown that the impact of quantization can be accurately described via a linear model with additive independent Gaussian noise. This model in turn leads to simple and intuitive expressions for spectrospatial power distribution of the transmitter and a lower bound on the achievable rate of the system. The derived model is validated through simulations and numerical evaluations, where it is shown to accurately predict both spectral and spatial power distributions.
$\mu AFL$: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware

Fuzzing is one of the most effective approaches to finding software flaws. However, applying it to microcontroller firmware incurs many challenges. For example, rehosting-based solutions cannot accurately model peripheral behaviors and thus cannot be used to fuzz the corresponding driver code. In this work, we present $\mu$ AFL, a hardware-in-the-loop approach to fuzzing microcontroller firmware. It leverages debugging tools in existing embedded system development to construct an AFL-compatible fuzzing framework. Specifically, we use the debug dongle to bridge the fuzzing environment on the PC and the target firmware on the microcontroller device. To collect code coverage information without costly code instrumentation, $\mu$ AFL relies on the ARM ETM hardware debugging feature, which transparently collects the instruction trace and streams the results to the PC. However, the raw ETM data is obscure and needs enormous computing resources to recover the actual instruction flow. We therefore propose an alternative representation of code coverage, which retains the same path sensitivity as the original AFL algorithm, but can directly work on the raw ETM data without matching them with disassembled instructions. To further reduce the workload, we use the DWT hardware feature to selectively collect runtime information of interest. We evaluated $\mu$ AFL on two real evaluation boards from two major vendors: NXP and STMicroelectronics. With our prototype, we discovered ten zero-day bugs in the driver code shipped with the SDK of STMicroelectronics and three zero-day bugs in the SDK of NXP. Eight CVEs have been allocated for them. Considering the wide adoption of vendor SDKs in real products, our results are alarming.
Deep Residual Shrinkage Networks for EMG-based Gesture Identification

—This work introduces a method for high-accuracy EMG based gesture identification. A newly developed deep learning method, namely, deep residual shrinkage network is applied to perform gesture identification. Based on the feature of EMG signal resulting from gestures, optimizations are made to improve the identification accuracy. Finally, three different algorithms are applied to compare the accuracy of EMG signal recognition with that of DRSN. The result shows that DRSN excel traditional neural networks in terms of EMG recognition accuracy. This paper provides a reliable way to classify EMG signals, as well as exploring possible applications of DRSN. propagate back layer by layer, but also flows directly back to the original layer by identifying shortcuts. Whereas it exists the disadvantages. When processing high noise vibration signals, the DRN’s feature learning ability tends to be reduced. The convolution kernel used in DRN as a local feature extractor may not be able to detect fault related features due to noise
Generalized Causal Tree for Uplift Modeling

Uplift modeling is crucial in various applications ranging from marketing and policy-making to personalized recommendations. The main objective is to learn optimal treatment allocations for a heterogeneous population. A primary line of existing work modifies the loss function of the decision tree algorithm to identify cohorts with heterogeneous treatment effects. Another line of work estimates the individual treatment effects separately for the treatment group and the control group using off-the-shelf supervised learning algorithms. The former approach that directly models the heterogeneous treatment effect is known to outperform the latter in practice. However, the existing tree-based methods are mostly limited to a single treatment and a single control use case, except for a handful of extensions to multiple discrete treatments. In this paper, we propose a generalization of tree-based approaches to tackle multiple discrete and continuous-valued treatments. We focus on a generalization of the well-known causal tree algorithm due to its desirable statistical properties, but our generalization technique can be applied to other tree-based approaches as well. The efficacy of our proposed method is demonstrated using experiments and real data examples.
Consistency and Convergence of a High Order Accurate Meshless Method for Solution of Incompressible Fluid Flows

Computations of incompressible flows with all velocity boundary conditions require solution of a Poisson equation for pressure or pressure-corrections with all Neumann boundary conditions. Discretization of such a Poisson equation results in a rank-deficient ill-conditioned matrix of coefficients. When a non-conservative discretization method such as finite difference, finite element, or spectral scheme is used, the ill-conditioned matrix also generates an inconsistency which makes the residuals in the iterative solution to saturate at a threshold level that depends on the spatial resolution and the order of the discretization scheme. In this paper, we examine this inconsistency for a high-order meshless discretization scheme suitable for solving the equations on a complex domain. The high order meshless method uses polyharmonic spline radial basis functions (PHS-RBF) with appended polynomials to interpolate scattered data and constructs the discrete equations by collocation. Corresponding Author Email Address: shahaneshantanu@gmail.com Preprint submitted to Journal February 8, 2022 ar X iv :2 20 2. 02 82 8v 1 [ m at h. N A ] 6 F eb 2 02 2 The PHS-RBF provides the flexibility to vary the order of discretization by increasing the degree of the appended polynomial. In this study, we examine the convergence of the inconsistency for different spatial resolutions and for different degrees of the appended polynomials by solving the Poisson equation for a manufactured solution as well as the Navier-Stokes equations for several fluid flows. We observe that the inconsistency decreases faster than the error in the final solution, and eventually becomes vanishing small at sufficient spatial resolution. The rate of convergence of the inconsistency is observed to be similar or better than the rate of convergence of the discretization errors. This beneficial observation makes it unnecessary to regularize the Poisson equation by fixing either the mean pressure or pressure at an arbitrary point. A simple point solver such as the SOR is seen to be wellconvergent, although it can be further accelerated using multilevel methods.
Normal Mode Analysis of the Linear Viscoelastic Response of Dissipative Systems: Application to Vertex Model

Sijie Tong, Rastko Sknepnek, 3, ∗ and Andrej Košmrlj 4, † Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, USA School of Science and Engineering, University of Dundee, Dundee DD1 4HN, United Kingdom School of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom Princeton Institute of Materials, Princeton University, Princeton, New Jersey 08544, USA (Dated: February 8, 2022)
Combining Deep Learning and Reasoning for Address Detection in Unstructured Text Documents

Extracting information from unstructured text documents is a demanding task, since these documents can have a broad variety of different layouts and a non-trivial reading order, like it is the case for multi-column documents or nested tables. Additionally, many business documents are received in paper form, meaning that the textual contents need to be digitized before further analysis. Nonetheless, automatic detection and capturing of crucial document information like the sender address would boost many companies' processing efficiency. In this work we propose a hybrid approach that combines deep learning with reasoning for finding and extracting addresses from unstructured text documents. We use a visual deep learning model to detect the boundaries of possible address regions on the scanned document images and validate these results by analyzing the containing text using domain knowledge represented as a rule based system.
Malleable Agents for Re-Configurable Robotic Manipulators

Re-configurable robots have more utility and flexibility for many real-world tasks. Designing a learning agent to operate such robots requires adapting to different configurations. Here, we focus on robotic arms with multiple rigid links connected by joints. We propose a deep reinforcement learning agent with sequence neural networks embedded in the agent to adapt to robotic arms that have a varying number of links. Further, with the additional tool of domain randomization, this agent adapts to different configurations. We perform simulations on a 2D N-link arm to show the ability of our network to transfer and generalize efficiently.
A rigorous stochastic theory for spike pattern formation in recurrent neural networks with arbitrary connection topologies

Cortical networks exhibit synchronized activity which often occurs in spontaneous events in the form of spike avalanches. Since synchronization has been causally linked to central aspects of brain function such as selective signal processing and integration of stimulus information, participating in an avalanche is a form of a transient synchrony which temporarily creates neural assemblies and hence might especially be useful for implementing flexible information processing. For understanding how assembly formation supports neural computation, it is therefore essential to establish a comprehensive theory of how network structure and dynamics interact to generate specific avalanche patterns and sequences. Here we derive exact avalanche distributions for a finite network of recurrently coupled spiking neurons with arbitrary non-negative interaction weights, which is made possible by formally mapping the model dynamics to a linear, random dynamical system on the N -torus and by exploiting self-similarities inherent in the phase space. We introduce the notion of relative unique ergodicity and show that this property is guaranteed if the system is driven by a time-invariant Bernoulli process. This approach allows us not only to provide closed-form analytical expressions for avalanche size, but also to determine the detailed set(s) of units firing in an avalanche (i.e., the avalanche assembly). The underlying dependence between network structure and dynamics is made transparent by expressing the distribution of avalanche assemblies in terms of the induced graph Laplacian. We explore analytical consequences of this dependence and provide illustrating examples. In summary, our framework provides a major extension of previous analytical work which was restricted to regularly coupled or discrete state networks in the infinite network limit. For systems with a sufficiently homogeneous or translationally invariant coupling topology, we make an explicit link to critical states and the existence of scale-free distributions.
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning

Federated Learning (FL) enables multiple parties to dis- tributively train a ML model without revealing their private datasets. However, it assumes trust in the centralized aggre- gator which stores and aggregates model updates. This makes it prone to gradient tampering and privacy leakage by a mali- cious aggregator. Malicious parties can also introduce backdoors into the joint model by poisoning the training data or model gradients. To address these issues, we present B EAS , the ﬁrst blockchain-based framework for N -party FL that provides strict privacy guarantees of training data using gradient pruning (showing improved differential privacy compared to existing noise and clipping based techniques). Anomaly detection protocols are used to minimize the risk of data- poisoning attacks, along with gradient pruning that is further used to limit the efﬁcacy of model-poisoning attacks. We also deﬁne a novel protocol to prevent premature convergence in heterogeneous learning environments.We perform exten- sive experiments on multiple datasets with promising results: B EAS successfully prevents privacy leakage from dataset re- construction attacks, and minimizes the efﬁcacy of poisoning attacks. Moreover, it achieves an accuracy similar to cen- tralized frameworks, and its communication and computation overheads scale linearly with the number of participants.
Graphical parameters for classes of tumbling block graphs

The infinite tumbling block graph is a bipartite graph, where each vertex in one partite set is of degree 3 and each vertex in the other partite set is of degree 6. It is a 2-dimensional array of blocks of seven vertices and nine edges, a planar graph that has 3-D looks. This paper introduces tumbling block graphs and considers various graphical parameters for different classes of infinite and finite tumbling blocks.
A Least Square Approach to Semi-supervised Local Cluster Extraction

A least square semi-supervised local clustering algorithm based on the idea of compressed sensing are proposed to extract clusters from a graph with known adjacency matrix. The algorithm is based on a two stage approaches similar to the one in [26]. However, under a weaker assumption and with less computational complexity than the one in [26], the algorithm is shown to be able to ﬁnd a desired cluster with high probability. Several numerical experiments including the synthetic data and real data such as MNIST, AT&T and YaleB human faces data sets are conducted to demonstrate the performance of our algorithm
GMC - Geometric Multimodal Contrastive Representation Learning

Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels. To address it, we present a novel Geometric Multimodal Con-trastive (GMC) representation learning method comprised of two main components: i) a two-level architecture consisting of modality-speciﬁc base encoder, allowing to process an arbitrary number of modalities to an intermediate representation of ﬁxed dimensionality, and a shared projection head, mapping the intermediate representations to a latent representation space; ii) a multimodal contrastive loss function that encourages the geometric alignment of the learned representations. We experimentally demonstrate that GMC representations are semantically rich and achieve state-of-the-art performance with missing modality information on three different learning problems including prediction and reinforcement learning tasks.