Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks

Several approximate inference methods have been proposed for deep discrete latent variable models. However, non-parametric methods which have previously been successfully employed for classical sparse coding models have largely been unexplored in the context of deep models. We propose a non-parametric iterative algorithm for learning discrete latent representations in such deep models. Additionally, to learn scale invariant discrete features, we propose local data scaling variables. Lastly, to encourage sparsity in our representations, we propose a Beta-Bernoulli process prior on the latent factors. We evaluate our spare coding model coupled with different likelihood models. We evaluate our method across datasets with varying characteristics and compare our results to current amortized approximate inference methods.
Delay-SDE-net: A deep learning approach for time series modelling with memory and uncertainty estimates

To model time series accurately is important within a wide range of fields. As the world is generally too complex to be modelled exactly, it is often meaningful to assess the probability of a dynamical system to be in a specific state. This paper presents the Delay-SDE-net, a neural network model based on stochastic delay differential equations (SDDEs). The use of SDDEs with multiple delays as modelling framework makes it a suitable model for time series with memory effects, as it includes memory through previous states of the system. The stochastic part of the Delay-SDE-net provides a basis for estimating uncertainty in modelling, and is split into two neural networks to account for aleatoric and epistemic uncertainty. The uncertainty is provided instantly, making the model suitable for applications where time is sparse. We derive the theoretical error of the Delay-SDE-net and analyze the convergence rate numerically. At comparisons with similar models, the Delay-SDE-net has consistently the best performance, both in predicting time series values and uncertainties.
Sharp lower bound on the"number of nodal decomposition"of graphs

Urschel introduced a notion of nodal partitioning to prove an upper bound on the number of nodal decomposition of discrete Laplacian eigenvectors. The result is an analogue to the well-known Courant's nodal domain theorem on continuous Laplacian. In this article, using the same notion of partitioning, we discuss the lower bound (or lack thereof) on the number of nodal decomposition of eigenvectors in the class of all graphs with a fixed number of vertices (however large). This can be treated as a discrete analogue to the results of Stern and Lewy in the continuous Laplacian case.
Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Information

The function call graph (FCG) based Android malware detection methods have recently attracted increasing attention due to their promising performance. However, these methods are susceptible to adversarial examples (AEs). In this paper, we design a novel black-box AE attack towards the FCG based malware detection system, called BagAmmo. To mislead its target system, BagAmmo purposefully perturbs the FCG feature of malware through inserting"never-executed"function calls into malware code. The main challenges are two-fold. First, the malware functionality should not be changed by adversarial perturbation. Second, the information of the target system (e.g., the graph feature granularity and the output probabilities) is absent. To preserve malware functionality, BagAmmo employs the try-catch trap to insert function calls to perturb the FCG of malware. Without the knowledge about feature granularity and output probabilities, BagAmmo adopts the architecture of generative adversarial network (GAN), and leverages a multi-population co-evolution algorithm (i.e., Apoem) to generate the desired perturbation. Every population in Apoem represents a possible feature granularity, and the real feature granularity can be achieved when Apoem converges. Through extensive experiments on over 44k Android apps and 32 target models, we evaluate the effectiveness, efficiency and resilience of BagAmmo. BagAmmo achieves an average attack success rate of over 99.9% on MaMaDroid, APIGraph and GCN, and still performs well in the scenario of concept drift and data imbalance. Moreover, BagAmmo outperforms the state-of-the-art attack SRL in attack success rate.
Workload Behavior Driven Memory Subsystem Design for Hyperscale

Hyperscalars run services across a large fleet of servers, serving billions of users worldwide. These services, however, behave differently than commonly available benchmark suites, resulting in server architectures that are not optimized for cloud workloads. With datacenters becoming a primary server processor market, optimizing server processors for cloud workloads by better understanding their behavior has become crucial. To address this, in this paper, we present MemProf, a memory profiler that profiles the three major reasons for stalls in cloud workloads: code-fetch, memory bandwidth, and memory latency. We use MemProf to understand the behavior of cloud workloads and propose and evaluate micro-architectural and memory system design improvements that help cloud workloads' performance. MemProf's code analysis shows that cloud workloads execute the same code across CPU cores. Using this, we propose shared micro-architectural structures--a shared L2 I-TLB and a shared L2 cache. Next, to help with memory bandwidth stalls, using workloads' memory bandwidth distribution, we find that only a few pages contribute to most of the system bandwidth. We use this finding to evaluate a new high-bandwidth, small-capacity memory tier and show that it performs 1.46x better than the current baseline configuration. Finally, we look into ways to improve memory latency for cloud workloads. Profiling using MemProf reveals that L2 hardware prefetchers, a common solution to reduce memory latency, have very low coverage and consume a significant amount of memory bandwidth. To help improve hardware prefetcher performance, we built a memory tracing tool to collect and validate production memory access traces.
Successive magnetic orderings in the Ising spin chain magnet DyNi$_5$Ge$_3$

In this report, we investigated a new rare earth based one-dimensional Ising spin chain magnet~\DNG~by means of magnetization, specific heat and powder neutron diffraction measurements. Due to the crystalline electrical field splitting, the magnetic Dy ions share an Ising like ground doublet state. Owning to the local point symmetry, these Ising moments form into two canted magnetic sublattices, which were further confirmed by the angle-dependent magnetization measurement. In zero fields, two successive antiferromagnetic phase transitions were found at temperatures $T_{\mathrm{N1}}=6~\rm K$ and $T_{\mathrm{N2}}=5~\rm K$, respectively. Only part of the moments are statically ordered in this intermediate state between $T_{\mathrm{N1}}$ and $T_{\mathrm{N2}}$. Powder neutron diffraction experiments at different temperatures were performed as well. An incommensurate magnetic propagation vector of $\mathbf{k_{\rm m}}=(0.5,0.4,0.5)$ was identified. The refined spin configurations through the irreducible representation analysis confirmed that these Ising spins are canted in the crystal $ab$~plane.
A Ramsey Type problem for highly connected subgraphs

Bollob\'{a}s and Gy\'{a}rf\'{a}s conjectured that for any $k, n \in \mathbb{Z}^+$ with $n>4(k-1)$, every 2-edge-coloring of the complete graph on $n$ vertices leads to a $k$-connected monochromatic subgraph with at least $n-2k+2$ vertices. We find a counterexample with $n = \lfloor 5k-2.5-\sqrt{8k-\frac{31}{4}} \rfloor$, thus disproving the conjecture, and we show the conclusion holds for $n>5k-2.5-\sqrt{8k-\frac{31}{4}}$ when $k \ge 16$.
On the perfection of algebraic spaces

This paper is a subsequent paper of arXiv:2303.07672. We will continue our research on the subject of perfect algebraic spaces that is developed in arXiv:2303.07672. By means of algebraic Frobenius morphisms, we define the perfection of arbitrary algebraic spaces of prime characteristic. There is a natural perfection functors on algebraic spaces. We prove several desired properties of the perfection functor. This extends nearly all previous results of the perfection functor on schemes, including the recent ones developed by Bertapelle et al. in arXiv:1611.02060. Moreover, our theory extends the previous one developed by Xinwen Zhu in arXiv:1707.05700v1, arXiv:1407.8519. The perfection functor rises the notion of perfection of sites, which enables us to restate some of Zhu's theory in arXiv:1707.05700v1. Then we show that our theory of perfect algebraic spaces in arXiv:2303.07672 is equivalent to Zhu's theory in arXiv:1707.05700v1 when the base scheme is a perfect field of prime characteristic.
High-dimensional multi-view clustering methods

Multi-view clustering has been widely used in recent years in comparison to single-view clustering, for clear reasons, as it offers more insights into the data, which has brought with it some challenges, such as how to combine these views or features. Most of recent work in this field focuses mainly on tensor representation instead of treating the data as simple matrices. This permits to deal with the high-order correlation between the data which the based matrix approach struggles to capture. Accordingly, we will examine and compare these approaches, particularly in two categories, namely graph-based clustering and subspace-based clustering. We will conduct and report experiments of the main clustering methods over a benchmark datasets.
Co-Occurrence Matters: Learning Action Relation for Temporal Action Localization

Temporal action localization (TAL) is a prevailing task due to its great application potential. Existing works in this field mainly suffer from two weaknesses: (1) They often neglect the multi-label case and only focus on temporal modeling. (2) They ignore the semantic information in class labels and only use the visual information. To solve these problems, we propose a novel Co-Occurrence Relation Module (CORM) that explicitly models the co-occurrence relationship between actions. Besides the visual information, it further utilizes the semantic embeddings of class labels to model the co-occurrence relationship. The CORM works in a plug-and-play manner and can be easily incorporated with the existing sequence models. By considering both visual and semantic co-occurrence, our method achieves high multi-label relationship modeling capacity. Meanwhile, existing datasets in TAL always focus on low-semantic atomic actions. Thus we construct a challenging multi-label dataset UCF-Crime-TAL that focuses on high-semantic actions by annotating the UCF-Crime dataset at frame level and considering the semantic overlap of different events. Extensive experiments on two commonly used TAL datasets, \textit{i.e.}, MultiTHUMOS and TSU, and our newly proposed UCF-Crime-TAL demenstrate the effectiveness of the proposed CORM, which achieves state-of-the-art performance on these datasets.
Normal Form Bisimulations By Value

Normal form bisimilarities are a natural form of program equivalence resting on open terms, first introduced by Sangiorgi in call-by-name. The literature contains a normal form bisimilarity for Plotkin's call-by-value $\lambda$-calculus, Lassen's \emph{enf bisimilarity}, which validates all of Moggi's monadic laws and can be extended to validate $\eta$. It does not validate, however, other relevant principles, such as the identification of meaningless terms -- validated instead by Sangiorgi's bisimilarity -- or the commutation of $\letexp$s. These shortcomings are due to issues with open terms of Plotkin's calculus. We introduce a new call-by-value normal form bisimilarity, deemed \emph{net bisimilarity}, closer in spirit to Sangiorgi's and satisfying the additional principles. We develop it on top of an existing formalism designed for dealing with open terms in call-by-value. It turns out that enf and net bisimilarities are \emph{incomparable}, as net bisimilarity does not validate Moggi's laws nor $\eta$. Moreover, there is no easy way to merge them. To better understand the situation, we provide an analysis of the rich range of possible call-by-value normal form bisimilarities, relating them to Ehrhard's relational model.
Robust Preference-Guided Denoising for Graph based Social Recommendation

Graph Neural Network (GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations. However, in terms of both effectiveness and efficiency of recommendation, a large portion of social relations can be redundant or even noisy, e.g., it is quite normal that friends share no preference in a certain domain. Existing models do not fully solve this problem of relation redundancy and noise, as they directly characterize social influence over the full social network. In this paper, we instead propose to improve graph based social recommendation by only retaining the informative social relations to ensure an efficient and effective influence diffusion, i.e., graph denoising. Our designed denoising method is preference-guided to model social relation confidence and benefits user preference learning in return by providing a denoised but more informative social graph for recommendation models. Moreover, to avoid interference of noisy social relations, it designs a self-correcting curriculum learning module and an adaptive denoising strategy, both favoring highly-confident samples. Experimental results on three public datasets demonstrate its consistent capability of improving three state-of-the-art social recommendation models by robustly removing 10-40% of original relations. We release the source code at https://github.com/tsinghua-fib-lab/Graph-Denoising-SocialRec.
Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators

Nonlinear control systems with partial information to the decision maker are prevalent in a variety of applications. As a step toward studying such nonlinear systems, this work explores reinforcement learning methods for finding the optimal policy in the nearly linear-quadratic regulator systems. In particular, we consider a dynamic system that combines linear and nonlinear components, and is governed by a policy with the same structure. Assuming that the nonlinear component comprises kernels with small Lipschitz coefficients, we characterize the optimization landscape of the cost function. Although the cost function is nonconvex in general, we establish the local strong convexity and smoothness in the vicinity of the global optimizer. Additionally, we propose an initialization mechanism to leverage these properties. Building on the developments, we design a policy gradient algorithm that is guaranteed to converge to the globally optimal policy with a linear rate.
A comparative evaluation of turbulence models for simulation of unsteady cavitating flows

Cavitation is a complex multiphase phenomenon characterised by vapour bubbles forming due to a sudden pressure drop and is often accompanied by increased hull vibrations, increased radiated noise and decrease in propeller and impeller performance. Although the Reynolds-Averaged Navier-Stokes (RANS) method coupled with a cavitation model is still considered a practical tool to predict cavitating flows owing to its computational efficiency, it is unable to predict the unsteadiness of vapor shedding and over-predicts the eddy viscosity. To improve the prediction, an empirical eddy viscosity correction,[Reboud et al. 1998] was proposed to consider the compressibility effects produced by cavitation. Additionally, a new type of models termed as hybrid RANS-Large Eddy Simulation (LES) models have also been recently introduced in the community, having the ability to behave as a RANS or a LES model in different regions of the flow in order to combine the computational cost efficiency of RANS with the accuracy of LES modelling. However, there exists a lack of a comprehensive review of various such turbulence models like the k-{\omega} Shear Stress Transport Model (SST), k-{\omega} SST Scale-adaptive Simulation (SAS), k-{\omega} SST Detached Eddy Simulation (DES), k-{\omega} SST Delayed DES (DDES), Filter-Based Method (FBM) and Partially-Averaged Navier Stokes Method (PANS) to predict cavitating flows. In this work, we conduct such a review to compare their ability to predict cloud cavitating flows by comparing them with x-ray experimental data in a venturi. It is shown that with mesh refinement, standard models do show the vapor unsteadiness as seen in the experiment similar to that seen when using the Reboud correction. However, on local comparison of turbulence quantities, it is observed both forms of models have huge discrepancies with experimental data that does not improve downstream.
A singular perturbation model of fluid dynamics in the vestibular semicircular canal and ampulla

A matched asymptotic analysis is presented that describes the mechanical response of the vestibular semicircular canals to rotation of the head and includes the fluid–structure interaction which takes place within the enlarged ampullary region of the duct. New theoretical results detail the velocity field in a fluid boundary layer surrounding the cupula. The governing equations were linearized for small perturbations in fluid displacement from the prescribed motion of the head and reduced asymptotically by exploiting the slender geometry of the duct. The results include the pressure drop around the three-dimensional endolymphatic duct and through the transitional boundary layers within the ampulla. Results implicitly include the deflected shape of the cupular partition and provide an expression for the dynamic boundary condition acting on the two surfaces of the cupula. In this sense, the analysis reduces the three-dimensional fluid dynamics of the endolymph to a relatively simple boundary condition acting on the surfaces of the cupula. For illustrative purposes we present specific results modelling the cupula as a simple viscoelastic membrane. New results show that the multi-dimensional fluid dynamics within the enlarged ampulla has a significant influence on the pointwise deflection of the cupula near the crista. The spatially averaged displacement of the cupula is shown to agree with previous macromechanical descriptions of endolymph flow and pressure that ignore the fluid–structure interaction at the cupula. As an example, the model is applied to the geometry of the horizontal semicircular canal of the toadfish, Opsanus tau, and results for the deflection of the cupula are compared to individual semicircular canal afferent responses previously reported by Boyle & Highstein (1990). The cupular-shear-angle gain, defined by the angular slope of the cupula at the crista divided by the angular velocity of the head, is relatively constant at frequencies from 0.01 Hz up to 1 Hz. Over this same range, the phase of the cupular shear angle aligns with the angular velocity of the head. Near 10 Hz, the shear-angle gain increases slightly and the phase shows a lead of as much a 30°. Results are sensitive to the cupular stiffness and viscosity. Comparing results to the afferent responses represented within the VIIIth nerve provides additional theoretical evidence that the macromechanical displacement of the cupula accounts for the behaviour of only a subset of afferent fibres.
GaAsSb: a novel material for near infrared photodetectors on GaAs substrates

We have studied the molecular beam epitaxy (MBE) growth of GaAsSb on GaAs substrates. The optical properties and composition of GaAsSb layer strongly depend on the growth temperature, the Ga growth rate, and the As and Sb fluxes and their ratios. We also report on two GaAsSb-GaAs photodiode structures operating at 1.3 /spl mu/m. The peak quantum efficiency was 54% for the GaAsSb resonant-cavity-enhanced (RCE) p-i-n photodiode and 36% for the RCE GaAsSb avalanche photodiode (APD) with separate absorption, charge, and multiplication regions (SACM). At 90% of the breakdown, the dark current of the SACM APD was 5 nA. The GaAsSb SACM APD also exhibited very low multiplication noise and k/sub eff/ was approximately 0.1, which is the lowest ever reported for APDs operating at 1.3 /spl mu/m.
Physics-Informed Optical Kernel Regression Using Complex-valued Neural Fields

Lithography is fundamental to integrated circuit fabrication, necessitating large computation overhead. The advancement of machine learning (ML)-based lithography models alleviates the trade-offs between manufacturing process expense and capability. However, all previous methods regard the lithography system as an image-to-image black box mapping, utilizing network parameters to learn by rote mappings from massive mask-to-aerial or mask-to-resist image pairs, resulting in poor generalization capability. In this paper, we propose a new ML-based paradigm disassembling the rigorous lithographic model into non-parametric mask operations and learned optical kernels containing determinant source, pupil, and lithography information. By optimizing complex-valued neural fields to perform optical kernel regression from coordinates, our method can accurately restore lithography system using a small-scale training dataset with fewer parameters, demonstrating superior generalization capability as well. Experiments show that our framework can use 31% of parameters while achieving 69× smaller mean squared error with 1.3× higher throughput than the state-of-the-art.
Reservation of Virtualized Resources with Optimistic Online Learning

The virtualization of wireless networks enables new services to access network resources made available by the Network Operator (NO) through a Network Slicing market. The different service providers (SPs) have the opportunity to lease the network resources from the NO to constitute slices that address the demand of their specific network service. The goal of any SP is to maximize its service utility and minimize costs from leasing resources while facing uncertainties of the prices of the resources and the users' demand. In this paper, we propose a solution that allows the SP to decide its online reservation policy, which aims to maximize its service utility and minimize its cost of reservation simultaneously. We design the Optimistic Online Learning for Reservation (OOLR) solution, a decision algorithm built upon the Follow-the-Regularized Leader (FTRL), that incorporates key predictions to assist the decision-making process. Our solution achieves a $\mathcal{O}(\sqrt{T})$ regret bound where $T$ represents the horizon. We integrate a prediction model into the OOLR solution and we demonstrate through numerical results the efficacy of the combined models' solution against the FTRL baseline.
Learning to Place Unseen Objects Stably Using a Large-Scale Simulation

Object placement is a fundamental task for robots, yet it remains challenging for partially observed objects. Existing methods for object placement have limitations, such as the requirement for a complete 3D model of the object or the inability to handle complex shapes and novel objects that restrict the applicability of robots in the real world. Herein, we focus on addressing the Unseen Object Placement (UOP) problem. We tackled the UOP problem using two methods: (1) UOP-Sim, a large-scale dataset to accommodate various shapes and novel objects, and (2) UOP-Net, a point cloud segmentation-based approach that directly detects the most stable plane from partial point clouds. Our UOP approach enables robots to place objects stably, even when the object's shape and properties are not fully known, thus providing a promising solution for object placement in various environments. We verify our approach through simulation and real-world robot experiments, demonstrating state-of-the-art performance for placing single-view and partial objects.