Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximal self-orthogonal modules and a new generalization of tilting modules

We introduce a generalization of tilting modules of finite projective dimension, projectively Wakamatsu tilting modules, which are self-orthogonal and Ext-progenerators in their Ext-perpendicular categories. Under a certain finiteness condition, we prove that the following modules coincide: projectively Wakamatsu tilting, Wakamatsu tilting, maximal self-orthogonal, and self-orthogonal modules with the same rank as the algebra. This provides another proof of the weak Gorensteinness of representation-finite algebras. To prove this, we introduce Bongartz completion of self-orthogonal modules and characterize its existence. Moreover, we study a binary relation on Wakamatsu tilting modules which extends the poset of tilting modules, and use it to prove that every self-orthogonal module over a representation-finite Iwanaga-Gorenstein algebra has finite projective dimension. Finally, we discuss several conjectures related to self-orthogonal modules and their connections to famous homological conjectures.
Combinatorial Causal Bandits without Graph Skeleton

In combinatorial causal bandits (CCB), the learning agent chooses a subset of variables in each round to intervene and collects feedback from the observed variables to minimize expected regret or sample complexity. Previous works study this problem in both general causal models and binary generalized linear models (BGLMs). However, all of them require prior knowledge of causal graph structure. This paper studies the CCB problem without the graph structure on binary general causal models and BGLMs. We first provide an exponential lower bound of cumulative regrets for the CCB problem on general causal models. To overcome the exponentially large space of parameters, we then consider the CCB problem on BGLMs. We design a regret minimization algorithm for BGLMs even without the graph skeleton and show that it still achieves $O(\sqrt{T}\ln T)$ expected regret. This asymptotic regret is the same as the state-of-art algorithms relying on the graph structure. Moreover, we sacrifice the regret to $O(T^{\frac{2}{3}}\ln T)$ to remove the weight gap covered by the asymptotic notation. At last, we give some discussions and algorithms for pure exploration of the CCB problem without the graph structure.
On the Statistical Benefits of Temporal Difference Learning

Given a dataset on actions and resulting long-term rewards, a direct estimation approach fits value functions that minimize prediction error on the training data. Temporal difference learning (TD) methods instead fit value functions by minimizing the degree of temporal inconsistency between estimates made at successive time-steps. Focusing on finite state Markov chains, we provide a crisp asymptotic theory of the statistical advantages of this approach. First, we show that an intuitive inverse trajectory pooling coefficient completely characterizes the percent reduction in mean-squared error of value estimates. Depending on problem structure, the reduction could be enormous or nonexistent. Next, we prove that there can be dramatic improvements in estimates of the difference in value-to-go for two states: TD's errors are bounded in terms of a novel measure - the problem's trajectory crossing time - which can be much smaller than the problem's time horizon.
Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning

This work revisits the joint beamforming (BF) and antenna selection (AS) problem, as well as its robust beamforming (RBF) version under imperfect channel state information (CSI). Such problems arise due to various reasons, e.g., the costly nature of the radio frequency (RF) chains and energy/resource-saving considerations. The joint (R)BF&AS problem is a mixed integer and nonlinear program, and thus finding optimal solutions is often costly. The vast majority of the prior works tackled these problems using techniques such as continuous approximations, greedy methods, and supervised machine learning—yet these approaches do not ensure optimality or even feasibility of the solutions. The main contribution of this work is threefold. First, an effective branch and bound (B&B) framework is proposed for the considered problem that guarantees global optimality by leveraging existing BF and RBF solvers. Second, to expedite the potentially costly B&B algorithm, a machine learning (ML)-based scheme is proposed to help skip intermediate states of the B&B search tree. The learning model features a graph neural network (GNN)-based design that is resilient to a commonly encountered challenge in wireless communications, namely, the change of problem size (e.g., the number of users) across the training and test stages. Third, comprehensive performance characterizations are presented, showing that the GNN-based method retains the global optimality of B&B with provably reduced complexity, under reasonable conditions. Numerical simulations also show that the ML-based acceleration can often achieve an order-of-magnitude speedup relative to B&B.
OPTIMIZATION OF PIN-FIN HEAT SINKS USING ANISOTROPIC LOCAL THERMAL NONEQUILIBRIUM POROUS MODEL IN A JET IMPINGING CHANNEL

A numerical study has been carried out to optimize the thermal performance of a pin-fin heat sink. A pin-fin heat sink, which is placed horizontally in a channel, is modeled as a hydraulically and thermally anisotropic porous medium. A uniform heat flux is prescribed at the bottom of the heat sink. Cool air is supplied from the top opening of the channel and exhausts to the channel outlet. Comprehensive numerical solutions are derived from the governing Navier-Stokes and energy equations using the Brinkman-Forchheimer extended Darcy model and the local thermal nonequilibrium (LTNE) porous model for the region occupied by the heat sink. Results from this study indicate that the anisotropy in permeability and solid-phase effective thermal conductivity changes substantially with the variation of porosity. Optimum porosity for maximum heat dissipation depends on the pin-fin thickness, the pin-fin height, and the Reynolds number. A correlation for predicting the optimum porosity for a pin-fin heat sink is proposed. Generally, in the case of thin pin-fins the heat sink should be designed to have a high porosity, while in the case of thick pin-fins the heat sink should be designed to have a relatively low porosity.
Angular momentum loss and the evolution of binaries of extreme mass ratio

Results are presented for 10 evolutionary sequences of close binary systems of extreme mass ratio. Angular momentum loss associated with material outflow from the system, a consequence of the ineffectiveness of tidal torques, as well as gravitational radiation is included in the calculations. We find that a previously described mass transfer instability is extremely sensitive to the amount of angular momentum loss by material means. The application of our results to the millisecond pulsar PSR 1937+214 suggests that its origin does not involve the continuous accretion of matter in a low-mass X-ray binary system.
Two identities for the Bernoulli–Euler numbers

Two identities for the Bernoulli and for the Euler numbers are derived. These identities involve two special cases of central combinatorial numbers. The approach is based on a set of differential identities for the powers of the secant. Generalizations of the Mittag–Leffler series for the secant are introduced and used to obtain closed-form expressions for the coefficients.
Crossbar-Aligned & Integer-Only Neural Network Compression for Efficient In-Memory Acceleration

Crossbar-based In-Memory Computing (IMC) accelerators preload the entire Deep Neural Network (DNN) into crossbars before in-ference. However, devices with limited crossbars cannot infer in-creasingly complex models. IMC-pruning can reduce the usage of crossbars, but current methods need expensive extra hardware for data alignment. Meanwhile, quantization can represent weights of DNNs by integers, but they employ non-integer scaling factors to ensure accuracy, requiring costly multipliers. In this paper, we first propose crossbar-aligned pruning to reduce the usage of crossbars without hardware overhead. Then, we introduce a quantization scheme to avoid multipliers in IMC devices. Finally, we design a learning method to complete above two schemes and cultivate an optimal compact DNN with high accuracy and large sparsity during training. Experiments demonstrate that our framework, compared to state-of-the-art methods, achieves larger sparsity and lower power consumption with higher accuracy. We even improve the accuracy by 0.43% for VGG-16 with an 88.25% sparsity rate on the Cifar-10 dataset. Compared to the original model, we reduce computing power and area by 19.8x and 18.8x, respectively.
Robust Hyperdimensional Computing Against Cyber Attacks and Hardware Errors: A Survey

Hyperdimensional Computing (HDC), also known as Vector Symbolic Architecture (VSA), is an emerging AI algorithm inspired by the way the human brain functions. Compared with deep neural networks (DNNs), HDC possesses several advantages such as smaller model size, less computation cost, and one/few-shot learning, making it a promising alternative computing paradigm. With the increasing deployment of AI in safety-critical systems such as healthcare and robotics, it is not only important to strive for high accuracy, but also to ensure its robustness under even highly uncertain and adversarial environments. However, recent studies show that HDC, just like DNNs, is vulnerable to both cyber attacks (e.g., adversarial attacks) and hardware errors (e.g., memory failures). While a growing body of research has been studying the robustness of HDC, there is a lack of systematic review of research efforts on this increasingly-important topic. To the best of our knowledge, this paper presents the first survey dedicated to review the research efforts made to the robustness of HDC against cyber attacks and hardware errors. While the performance and accuracy of HDC as an AI method still expects future theoretical advancement, this survey paper aims to shed light and call for community efforts on robustness research of HDC.
Data Sanitization on eMMCs

Data sanitization of modern digital devices is an important issue given that electronic wastes are being recycled and repurposed. The embedded Multi Media Card (eMMC), one of the NAND flash memory-based commodity devices, is one of the popularly recy-cled products in the current recycling ecosystem. We analyze a repurposed devices and evaluate its sanitization practice. Data from the formerly used device can still be recovered, which may lead to an unintentional leakage of sensitive data such as personally iden-tifiable information (PII). Since the internal storage of an eMMC is the NAND flash memory, sanitization practice of the NAND flash memory-based systems should apply to the eMMC. However, proper sanitize operation is obviously not always performed in the current recycling ecosystem. We discuss how data stored in eMMC and other flash memory-based devices need to be deleted in order to avoid the potential data leakage. We also review the NAND flash memory data sanitization schemes and discuss how they should be applied in eMMCs.
Novel CPLANE1 c.8948dupT (p.P2984Tfs*7) variant in a child patient with Joubert syndrome

Abstract Joubert syndrome (JBTS) is a class of heterogeneous ciliopathy genetically associated with CPLANE1 mutations. The characteristics of clinical phenotypes and CPLANE1 variants were analyzed in a 2-month-old patient. A 2-month-old patient with JBTS was diagnosed after clinical evaluation including family history, physical examination, cerebral MRI, ultrasonography imaging, VEGG, ocular fundus examination, and comprehensive blood and urine testing. Whole exome sequencing (WES) was performed to detect CPLANE1 variants, and Sanger sequencing was used to confirm the variants. This JBTS patient presented with oculomotor apraxia, dysregulation of breathing pattern, and ataxia. MRI revealed poor continuity of cerebelli, batwing appearance, and molar tooth sign. This patient was noted with abnormal hematology, dysregulation of hepatic function, thyroid function, immunity, and renal function, and encephalopathy. CPLANE1 (c.8948dupT (p.P2984Tfs*7) and c.247G > T (p.G83X)) variants were noticed in the patient as a pathogenic variant and caused autosomal recessive inheritance. The JBTS patient with mutations in CPLANE1 (c.8948dupT (p.P2984Tfs*7) and c.247G > T (p.G83X)) developed JBTS phenotypes. The novel CPLANE1 c.8948dupT (p.P2984Tfs*7) variant will assist clinicians and geneticists in reaching a precise diagnosis for JBTS.
A New Technique to Compute Coupled Coincidence Points

We compute coupled coincidence points without assuming the condition of compatibility of the pair of maps and relaxing the continuity condition of both the maps. In fact, our technique improves the technique introduced by Sintunavarat et al. (2011) which was then used by Hussain et al. (2012) to obtain coupled coincidence points.
Agile Hardware and Software Co-design for RISC-V-based Multi-precision Deep Learning Microprocessor

Recent network architecture search (NAS) has been widely applied to simplify deep learning neural networks, which typically result in a multi-precision network. Many multi-precision accelerators have been developed as well to support computing multi-precision networks manually. A software-hardware interface is thereby needed to automatically map multi-precision networks onto multi-precision accelerators. In this paper, we have developed an agile hardware and software co-design for RISC-V-based multi-precision deep learning microprocessor. We have designed custom RISC-V instructions with a framework to automatically compile multi-precision CNN networks onto multi-precision CNN acceler-ators, demonstrated on FPGA. Experiments show that with NAS optimized multi-precision CNN models (LeNet, VGG 16, ResNet, MobileNet), the RISC-V core with multi-precision accelerators can reach the highest throughput in 2,4,8-bit precisions respectively on a Xilinx ZCU102 FPGA.
Latent Weight-based Pruning for Small Binary Neural Networks

Binary neural networks (BNNs) substitute complex arithmetic oper-ations with simple bit-wise operations. The binarized weights and activations in BNN s can drastically reduce memory requirement and energy consumption, making it attractive for edge ML applications with limited resources. However, the severe memory capacity and energy constraints of low-power edge devices call for further reduction of BNN models beyond binarization. Weight pruning is a proven solution for reducing the size of many neural network (NN) models, but the binary nature of BNN weights make it difficult to identify insignificant weights to remove. In this paper, we present a pruning method based on latent weight with layer-level pruning sensitivity analysis which reduces the over-parameterization of BNN s, allowing for accuracy gains while drastically reducing the model size. Our method advocates for a heuristics that distinguishes weights by their latent weights, a real-valued vector used to compute the pseduogradient during backpropagation. It is tested using three different convolutional NNs on the MNIST, CIFAR-10, and Imagenette datasets with results indicating a 33%-46% reduction in operation count, with no accu-racy loss, improving upon previous works in accuracy, model size, and total operation count.
Discovering the In-Memory Kernels of 3D Dot-Product Engines

The capability of resistive random access memory (ReRAM) to implement multiply-and-accumulate operations promises unprece-dented efficiency in the design of scientific computing applications. While the use of two-dimensional (2D) ReRAM crossbar has been well investigated in the last few years, the design of in-memory dot-product engines using three-dimensional (3D) ReRAM crossbars remains a topic of active investigations. In this paper, we holisti-cally explore how to leverage 3D ReRAM crossbars with several (2 to 7) stacked crossbar layers. In contrast, previous studies have focused on 3D ReRAM with at most 2 stacked crossbar layers. We first discover the in-memory compute kernels that can be realized using 3D ReRAM with multiple stacked crossbar layers. We discover that matrices with different sparsity patterns can be realized by appropriately assigning the inputs and outputs to the perpendicular metal wires within the 3D stack. We present a design automation tool to map sparse matrices within scientific computing applications to the discovered 3D kernels. The proposed framework is evaluated using 20 applications from the SuitSparse Matrix Collection. Compared with 2D crossbars, the proposed approach using 3D crossbars improves area, energy, and latency with 2.02X, 2.37X, 2.45X, respectively.
Peirce's "Pragmatic" Method

i l ^ HARLES SANDERS PEIRCE, who contrib, iuted the term pragmatism to the language of philosophy, insisted always that the term was intended to designate a method. Pragmatism, he warned, is not a Weltanschauung, nor a doctrine of metaphysics, nor even an 'attempt to determine any truth of things. " It is simply a method of ascertaining meanings, of making them clear, and of pointing a way for the successful determination of the truth of things. Peirce's practice consistently belied his preaching in this regard, for his exposition of the method involved a metaphysical system of considerable scope; and this, it seems to me, was unavoidable. Yet it was as a method that Peirce first presented his distinctive point of view, and it is primarily as a method that I wish to consider it here. Into metaphysics, or Weltanschauung, we shall surely be led; despite Peirce's pronouncement, I make no apology for that. Peirce presented his pragmatic method in two of his most famous papers, published in the Popular Science Monthly in 1877 and i878.1 His purpose was to tell what thinking is, in terms of what it aims to do, and to suggest the method most appropriate for the accomplishment of its aim. The name
An Equivalence Checking Framework for Agile Hardware Design

Agile hardware design enables designers to produce new design iterations efficiently. Equivalence checking is critical in ensuring that a new design iteration conforms to its speci-fication. In this paper, we introduce an equivalence checking framework for hardware designs represented in HalideIR. HalideIR is a popular intermediate representation in soft-ware domains such as deep learning and image processing, and it is increasingly utilized in agile hardware design. We have developed a fully automatic equivalence checking work-flow seamlessly integrated with HalideIR and several opti-mizations that leverage the incremental nature of agile hard-ware design to scale equivalence checking. Evaluations of two deep learning accelerator designs show our automatic equivalence checking framework scales to hardware designs of practical sizes and detects inconsistencies that manually crafted tests have missed.
Analysis of low Reynolds number separation bubbles using semiempirical methods

The formation and growth of transitional separation bubbles can significantly affect boundary-layer development on airfoils operating at low chord Reynolds numbers. Of primary concern is the change in boundary-layer thickness between laminar separation and turbulent reattachment. This can be estimated using semiempirical methods, such as the one devised by H. P. Horton, which are based on solutions to the integral forms of the boundary-layer equations. The applicability of these methods at low Reynolds numbers was investigated using hot-wire measurements of bubbles formed on an NACA 663-018 airfoil at chord Reynolds numbers of 50,000-200,000. Analysis of the data revealed significant Reynolds number effects on the evolution of separated laminar flow. The momentum thickness growth between separation and transition was found to be similar to that predicted for a laminar half-jet and appears to be influenced by the momentum thickness Reynolds number at separation. This parameter also was found to have a noticeable effect on the Reynolds number based on the length of a bubble's laminar portion. In the turbulent portion of the bubbles, no Reynolds number effects were evident. This was true for both the momentum thickness growth and for Horton's reattachment criterion.
A Condensed Survey on Condensed Representations of Patterns

Pattern condensed representations are an important concept in inductive databases. They aim at summarizing the information contained in a set of patterns. This paper is not an exhaustive survey of the 20 years of proposals on condensed representations but rather a general overview. We propose a simple formalism modeling a large part of the exact and approximate condensed representations. Through this modeling, we show that many of the condensed representations are based on the same foundations, we highlight the compression and generalization sides of the condensed representations and we describe several trends. Finally, we discuss the current extents of the work on condensed representations within pattern mining.
Performance limits of a compressive sensing application to beamforming on a line array

Compressive sensing is a sampling theorem that exploits the sparsity of a signal in a domain Ψ, while being spread out in a sensing domain Φ. For example, a sinusoid time domain signal in Φ can be represented by one non-zero coefficient in the frequency domain Ψ. The time-frequency relationship is similar to the space-angle relationship that exists in underwater acoustics for an array of hydrophones. Wavefront curvatures that are spread out in the space domain can be represented in the angle domain by a sparse vector. This work investigates the performance limits of using compressive sensing to resolve signals in the angle domain, a task usually accomplished by beamforming. For compressive sensing, it has been shown that the performance of recovering a signal is related to the number of measurements, the number of non-zero coefficients, and the dimension of Ψ [E.J. Candes and M.B. Wakin IEEE Signal Processing Magazine 21-30 (March 2008)]. Typically, in underwater acoustics, the number of hydrophones and their locations are fixed, so that the performance is found to be dependent on the number of non-zero coefficients (signals in the water) and the dimension of the angle domain (beams). [Work supported by ARL:UT IRD]