Miracle-dz/UniFAR-FTUs huggingface dataset:
User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks

The Metaverse is emerging as maturing technologies are empowering the different facets. Virtual Reality (VR) technologies serve as the backbone of the virtual universe within the Metaverse to offer a highly immersive user experience. As mobility is emphasized in the Metaverse context, VR devices reduce their weights at the sacrifice of local computation abilities. In this paper, for a system consisting of a Metaverse server and multiple VR users, we consider two cases of (i) the server generating frames and transmitting them to users, and (ii) users generating frames locally and thus consuming device energy. Moreover, in our multi-user VR scenario for the Metaverse, users have different characteristics and demands for Frames Per Second (FPS). Then the channel access arrangement (including the decisions on frame generation location), and transmission powers for the downlink communications from the server to the users are jointly optimized to improve the utilities of users. This joint optimization is addressed by deep reinforcement learning (DRL) with heterogeneous actions. Our proposed user-centric DRL algorithm is called User-centric Critic with Heterogenous Actors (UCHA). Extensive experiments demonstrate that our UCHA algorithm leads to remarkable results under various requirements and constraints.
Maverick: An App-independent and Platform-agnostic Approach to Enforce Policies in IoT Systems at Runtime

Many solutions have been proposed to curb unexpected behavior of automation apps installed on programmable IoT platforms by enforcing safety policies at runtime. However, all prior work addresses a weaker version of the actual problem due to a simpler, unrealistic threat model. These solutions are not general enough as they are heavily dependent on the installed apps and catered to specific IoT platforms. Here, we address a stronger version of the problem via a realistic threat model, where (i) undesired cyber actions can come from not only automation platform backends (e.g., SmartThings) but also close-sourced third-party services (e.g., IFTTT), and (ii) physical actions (e.g., user interactions) on devices can move the IoT system to an undesirable state. We propose a runtime mechanism, dubbed Maverick, which employs an app-independent, platform-agnostic mediator to enforce policies against all undesired cyber actions and applies corrective-actions to bring the IoT system back to a safe state from an unsafe state transition. Maverick is equipped with a policy language capable of expressing rich temporal invariants and an automated toolchain that includes a policy synthesizer and a policy analyzer for user assistance. We implemented Maverick in a prototype and showed its efficacy in both physical and virtual testbeds, incurring minimal overhead.
Protease-independent action of tissue plasminogen activator in brain plasticity and neurological recovery after ischemic stroke

Significance Therapies that boost brain plasticity and repair processes without deleterious side effects have the potential to transform the clinical management of stroke. Here we report that tissue plasminogen activator (tPA), known as a clot buster for clinical treatment of ischemic stroke, exerts additional neurorestorative effects in a murine model of stroke by stimulating axonal regeneration and facilitating functional recovery. Importantly, functional restoration was independent of tPA’s proteolytic activity—a mutant, protease-inactive form of tPA, tPA-S478A, was as effective as regular tPA. Results of our preclinical studies support the view that tPA-S478A has the therapeutic potential to enhance brain regeneration and functional restoration in stroke victims. Unlike regular tPA, tPA-S478A does not bear the risk of inducing intracerebral hemorrhage. Emerging evidence suggests that tissue plasminogen activator (tPA), currently the only FDA-approved medication for ischemic stroke, exerts important biological actions on the CNS besides its well-known thrombolytic effect. In this study, we investigated the role of tPA on primary neurons in culture and on brain recovery and plasticity after ischemic stroke in mice. Treatment with recombinant tPA stimulated axonal growth in culture, an effect independent of its protease activity and achieved through epidermal growth factor receptor (EGFR) signaling. After permanent focal cerebral ischemia, tPA knockout mice developed more severe sensorimotor and cognitive deficits and greater axonal and myelin injury than wild-type mice, suggesting that endogenously expressed tPA promotes long-term neurological recovery after stroke. In tPA knockout mice, intranasal administration of recombinant tPA protein 6 hours poststroke and 7 more times at 2 d intervals mitigated white matter injury, improved axonal conduction, and enhanced neurological recovery. Consistent with the proaxonal growth effects observed in vitro, exogenous tPA delivery increased poststroke axonal sprouting of corticobulbar and corticospinal tracts, which might have contributed to restoration of neurological functions. Notably, recombinant mutant tPA-S478A lacking protease activity (but retaining the EGF-like domain) was as effective as wild-type tPA in rescuing neurological functions in tPA knockout stroke mice. These findings demonstrate that tPA improves long-term functional outcomes in a clinically relevant stroke model, likely by promoting brain plasticity through EGFR signaling. Therefore, treatment with the protease-dead recombinant tPA-S478A holds particular promise as a neurorestorative therapy, as the risk for triggering intracranial hemorrhage is eliminated and tPA-S478A can be delivered intranasally hours after stroke.
High-pressure deuterium annealing for improving the reliability characteristics of silicon–oxide–nitride–oxide–silicon nonvolatile memory devices

We report the effects of high-pressure deuterium postmetallization annealing on the reliability characteristics of silicon–oxide–nitride–oxide–silicon nonvolatile memory devices. Compared with the control samples annealed in a conventional forming gas ambient (H2∕N2=4%∕96%), the samples annealed in a high-pressure (10 atm) pure deuterium ambient show improved endurance and retention characteristics without the degradation of program/erase (P/E) speed. In addition, the high-pressure deuterium-annealed samples show a significantly reduced charge loss rate for the electron-stored state and the hole-stored state, before and after the P/E cycles. The improved reliability of the high-pressure deuterium-annealed samples can be explained by the significantly decreased interface trap density and the large kinetic isotope effect of deuterium, which reduces the generation of the interface trap density under the stress of the P/E cycles.
A sliced-Wasserstein distance-based approach for out-of-class-distribution detection

There exist growing interests in intelligent systems for numerous medical imaging, image processing, and computer vision applications, such as face recognition, medical diagnosis, character recognition, and self-driving cars, among others. These applications usually require solving complex classification problems involving complex images with unknown data generative processes. In addition to recent successes of the current classification approaches relying on feature engineering and deep learning, several shortcomings of them, such as the lack of robustness, generalizability, and interpretability, have also been observed. These methods often require extensive training data, are computationally expensive, and are vulnerable to out-of-distribution samples, e.g., adversarial attacks. Recently, an accurate, data-efficient, computationally efficient, and robust transport-based classification approach has been proposed, which describes a generative model-based problem formulation and closed-form solution for a specific category of classification problems. However, all these approaches lack mechanisms to detect test samples outside the class distributions used during training. In real-world settings, where the collected training samples are unable to exhaust or cover all classes, the traditional classification schemes are unable to handle the unseen classes effectively, which is especially an important issue for safety-critical systems, such as self-driving and medical imaging diagnosis. In this work, we propose a method for detecting out-of-class distributions based on the distribution of sliced-Wasserstein distance from the Radon Cumulative Distribution Transform (R-CDT) subspace. We tested our method on the MNIST and two medical image datasets and reported better accuracy than the state-of-the-art methods without an out-of-class distribution detection procedure.
Stability of finite difference schemes for the hyperbolic initial boundary value problem by winding number computations

In this paper, we present a numerical strategy to check the strong stability (or GKS-stability) of one-step explicit finite difference schemes for the one-dimensional advection equation with an inflow boundary condition. The strong stability is studied using the Kreiss-Lopatinskii theory. We introduce a new tool, the intrinsic Kreiss-Lopatinskii determinant, which possesses the same regularity as the vector bundle of discrete stable solutions. By applying standard results of complex analysis to this determinant, we are able to relate the strong stability of numerical schemes to the computation of a winding number, which is robust and cheap. The study is illustrated with the O3 scheme and the fifth-order Lax-Wendroff (LW5) scheme together with a reconstruction procedure at the boundary.
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler

Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been shown to obtain strong theoretical performance guarantees for a wide class of submodular problems under various types of constraints while clearly outperforming standard greedy approximation algorithms. This paper introduces a setup for benchmarking algorithms for submodular optimization problems with the aim to provide researchers with a framework to enhance and compare the performance of new algorithms for submodular problems. The focus is on the development of iterative search algorithms such as evolutionary algorithms with the implementation provided and integrated into IOHprofiler which allows for tracking and comparing the progress and performance of iterative search algorithms. We present a range of submodular optimization problems that have been integrated into IOHprofiler and show how the setup can be used for analyzing and comparing iterative search algorithms in various settings.
Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling

In this manuscript (ms), we propose causal inference based single-branch ensemble trees for uplift modeling, namely CIET. Different from standard classification methods for predictive probability modeling, CIET aims to achieve the change in the predictive probability of outcome caused by an action or a treatment. According to our CIET, two partition criteria are specifically designed to maximize the difference in outcome distribution between the treatment and control groups. Next, a novel single-branch tree is built by taking a top-down node partition approach, and the remaining samples are censored since they are not covered by the upper node partition logic. Repeating the tree-building process on the censored data, single-branch ensemble trees with a set of inference rules are thus formed. Moreover, CIET is experimentally demonstrated to outperform previous approaches for uplift modeling in terms of both area under uplift curve (AUUC) and Qini coefficient significantly. At present, CIET has already been applied to online personal loans in a national financial holdings group in China. CIET will also be of use to analysts applying machine learning techniques to causal inference in broader business domains such as web advertising, medicine and economics.
A Lipschitz Bandits Approach for Continuous Hyperparameter Optimization

One of the most critical problems in machine learning is HyperParameter Optimization (HPO), since choice of hyperparameters has a significant impact on final model performance. Although there are many HPO algorithms, they either have no theoretical guarantees or require strong assumptions. To this end, we introduce BLiE -- a Lipschitz-bandit-based algorithm for HPO that only assumes Lipschitz continuity of the objective function. BLiE exploits the landscape of the objective function to adaptively search over the hyperparameter space. Theoretically, we show that $(i)$ BLiE finds an $\epsilon$-optimal hyperparameter with $\mathcal{O} \left( \epsilon^{-(d_z + \beta)}\right)$ total budgets, where $d_z$ and $\beta$ are problem intrinsic; $(ii)$ BLiE is highly parallelizable. Empirically, we demonstrate that BLiE outperforms the state-of-the-art HPO algorithms on benchmark tasks. We also apply BLiE to search for noise schedule of diffusion models. Comparison with the default schedule shows that BLiE schedule greatly improves the sampling speed.
Constraints on compensated isocurvature perturbations from BOSS DR12 galaxy data

We use the BOSS DR12 galaxy power spectrum to constrain compensated isocurvature perturbations (CIP), which are opposite-sign primordial baryon and dark matter perturbations that leave the total matter density unchanged. Long-wavelength CIP σ(x) enter the galaxy density contrast as δg (x) ⊃ bσσ(x), with bσ the linear CIP galaxy bias parameter. We parameterize the CIP spectra as Pσσ = A 2 Pℛℛ and Pσℛ = ξ√PσσPℛℛ , where A is the CIP amplitude and ξ is the correlation with the curvature perturbations ℛ. We find a significance of detection of Ab σ ≠ 0 of 1. 8σ for correlated CIP (ξ = 1), consistent with no detection. For uncorrelated CIP (ξ = 0), the constraints are instead more significantly shifted away from zero, although this may be due to large-scale data systematics which have a bigger impact on these type of CIP. The constraints on A depend on the assumed priors for the bσ parameter, which we estimate using separate universe simulations. Assuming bσ values representative of all halos we find σA = 145 for correlated CIP and σ |A| = 475 for uncorrelated CIP. Our strongest uncorrelated CIP constraint is for bσ representative of the 33% most concentrated halos, σ |A| = 197, which is better than the current CMB bounds |A| ≲ 360. We also discuss the impact of the local primordial non-Gaussianity parameter f NL in CIP constraints. Our results demonstrate the power of galaxy data to place tight constraints on CIP, and motivate works to understand better the impact of data systematics, as well as to determine theory priors for bσ .
Energy Efficient SDN and SDR Joint Adaptation of CPU Utilization Based on Experimental Data Analytics

In this paper we propose a hybrid softwarized architecture of Network Function Virtualization (NFV) where Software-Defined Networking (SDN) and Software-Defined Radio (SDR) components are integrated to form a cloud-based communication system. We analyze CPU utilization and power consumption in the OpenIreland testbed for different parameter settings and use case scenarios of this NFV architecture. The experiment results show different behaviour between SDN data plane switching and SDR in terms of CPU utilization and parallelization, which provides insights for processing aggregation and power savings when integrating them together in a cloud-based system. We then propose a power saving scheme with flexible CPU allocation that can reduce the overall power consumption of the system. Our results show that our proposed NFV architecture and its power saving scheme can save up to 20% power consumption compared to conventional scheme where SDN and SDR are separately deployed.
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation

Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long-lasting scalability challenge due to the neighborhood explosion problem in large-scale graphs. In this work, we propose to capture long-distance dependency in graphs by shallower models instead of deeper models, which leads to a much more efficient model, LazyGNN, for graph representation learning. Moreover, we demonstrate that LazyGNN is compatible with existing scalable approaches (such as sampling methods) for further accelerations through the development of mini-batch LazyGNN. Comprehensive experiments demonstrate its superior prediction performance and scalability on large-scale benchmarks. The implementation of LazyGNN is available at https://github.com/RXPHD/Lazy_GNN.
Support Recovery in Sparse PCA with Non-Random Missing Data

We analyze a practical algorithm for sparse PCA on incomplete and noisy data under a general non-random sampling scheme. The algorithm is based on a semidefinite relaxation of the $\ell_1$-regularized PCA problem. We provide theoretical justification that under certain conditions, we can recover the support of the sparse leading eigenvector with high probability by obtaining a unique solution. The conditions involve the spectral gap between the largest and second-largest eigenvalues of the true data matrix, the magnitude of the noise, and the structural properties of the observed entries. The concepts of algebraic connectivity and irregularity are used to describe the structural properties of the observed entries. We empirically justify our theorem with synthetic and real data analysis. We also show that our algorithm outperforms several other sparse PCA approaches especially when the observed entries have good structural properties. As a by-product of our analysis, we provide two theorems to handle a deterministic sampling scheme, which can be applied to other matrix-related problems.
Coxeter quiver representations in fusion categories and Gabriel's theorem

We introduce a notion of representation for a class of generalised quivers known as Coxeter quivers. These representations are built using fusion categories associated to $U_q(\mathfrak{s}\mathfrak{l}_2)$ at roots of unity and we show that many of the classical results on representations of quivers can be generalised to this setting. Namely, we prove a generalised Gabriel's theorem for Coxeter quivers that encompasses all Coxeter--Dynkin diagrams -- including the non-crystallographic types $H$ and $I$. Moreover, a similar relation between reflection functors and Coxeter theory is used to show that the indecomposable representations correspond bijectively to the positive roots of Coxeter root systems over fusion rings.
Effects of orientational and positional randomness of particles on photonic band gap

A recent work [PRL, 126, 208002 (2021)] has explored how thermal noise-induced randomness in a self-assembled photonic crystal affects photonic band gaps (PBGs). For the system of a two-dimensional photonic crystal composed of a self-assembled array of rods with square cross sections, it was found that its PBGs can exist over an extensive range of packing densities. Counterintuitively, at intermediate packing densities, the transverse magnetic (TM) band gap of the self-assembled system can be larger than that of its corresponding perfect system (rods arranged in a perfect square lattice and having identical orientations). Due to shape anisotropicity, the randomness in the self-assembled system contains two kinds of randomness, i.e., positional and orientational randomness of the particles. In this work, we further investigate how PBGs are influenced solely by positional or orientational randomness. We find that compared to the perfect situation, the introduction of only orientational randomness decreases the transverse electric (TE) band gap while having no obvious effects on the transverse magnetic (TM) band gap. In contrast, the introduction of only positional randomness decreases the TE band gap significantly, while it can widen or narrow the TM band gap, depending on the parameter range. We also discuss the thermal (i.e., self-assembled) system where two kinds of randomness are present. Our study contributes to a better understanding of the role orientational randomness and positional randomness play on PBGs, and may benefit the PBG engineering of photonic crystals through self-assembly approaches.
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become increasingly popular for the task of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a substantial quantity of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To develop a solution to the aforementioned problems, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. First, accurate classification results are obtained under limited fault samples. More specifically, we integrate the convolutional neural network (CNN) with attention mechanisms to provide strong intrinsic inductive biases of locality and spatial invariance, thereby strengthening the representational power under limited fault samples. In addition, we ulteriorly enhance the classification capability of the SCCAM method under limited fault samples by employing the supervised contrastive learning (SCL) loss. Second, a novel ante-hoc interpretable attention-based architecture is designed to directly obtain the root causes without expert knowledge. The convolutional block attention module (CBAM) is utilized to directly provide feature contributions behind each prediction thus achieving feature-level explanations. The proposed SCCAM method is testified on a continuous stirred tank heater (CSTH) and the Tennessee Eastman (TE) industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The effectiveness of the presented SCCAM method is evidenced by the comprehensive results that show our method outperforms the state-of-the-art methods in terms of fault classification and root cause analysis.