Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Efficient Deflection-based On-chip Networks: Topology, Routing, Flow Control

As the number of cores scales to tens and hundreds, the energy consumption of routers across various types of on-chip networks in chip muiltiprocessors (CMPs) increases significantly. A major source of this energy consumption comes from the input buffers inside Network-on-Chip (NoC) routers, which are traditionally designed to maximize performance. To mitigate this high energy cost, many works propose bufferless router designs that utilize deflection routing to resolve port contention. While this approach is able to maintain high performance relative to its buffered counterparts at low network traffic, the bufferless router design suffers performance degradation under high network load. In order to maintain high performance and energy efficiency under both low and high network loads, this chapter discusses critical drawbacks of traditional bufferless designs and describes recent research works focusing on two major modifications to improve the overall performance of the traditional bufferless network-on-chip design. The first modification is a minimally-buffered design that introduces limited buffering inside critical parts of the on-chip network in order to reduce the number of deflections. The second modification is a hierarchical bufferless interconnect design that aims to further improve performance by limiting the number of hops each packet needs to travel while in the network. In both approaches, we discuss design tradeoffs and provide evaluation results based on common CMP configurations with various network topologies to show the effectiveness of each proposal.
Active Learning Meets Optimized Item Selection

Designing recommendation systems with limited or no available training data remains a challenge. To that end, a new combinatorial optimization problem is formulated to generate optimized item selection for experimentation with the goal to shorten the time for collecting randomized training data. We first present an overview of the optimized item selection problem and a multi-level optimization framework to solve it. The approach integrates techniques from discrete optimization, unsupervised clustering, and latent text embeddings. We then discuss how to incorporate optimized item selection with active learning as part of randomized exploration in an ongoing fashion.
Learning Swarm Interaction Dynamics From Density Evolution

In this article, we consider the problem of understanding the coordinated movements of biological or artificial swarms. In this regard, we propose a learning scheme to estimate the coordination laws of the interacting agents from observations of the swarm's density over time. We describe the dynamics of the swarm based on pairwise interactions according to a Cucker–Smale flocking model, and express the swarm's density evolution as the solution to a system of mean-field hydrodynamic equations. We propose a new family of parametric functions to model the pairwise interactions, which allows for the mean-field macroscopic system of integro-differential equations to be efficiently solved as an augmented system of partial differential equations. Finally, we incorporate the augmented system in an iterative optimization scheme to learn the dynamics of the interacting agents from observations of the swarm's density evolution over time. The results of this work can offer an alternative approach to study how animal flocks coordinate, create new control schemes for large networked systems, and serve as a central part of defense mechanisms against adversarial drone attacks.
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks

Many state-of-the-art ML models have outperformed humans in various tasks such as image classification. With such outstanding performance, ML models are widely used today. However, the existence of adversarial attacks and data poisoning attacks really questions the robustness of ML models. For instance, Engstrom et al. demonstrated that state-of-the-art image classifiers could be easily fooled by a small rotation on an arbitrary image. As ML systems are being increasingly integrated into safety and security-sensitive applications, adversarial attacks and data poisoning attacks pose a considerable threat. This chapter focuses on the two broad and important areas of ML security: adversarial attacks and data poisoning attacks.
Experiments in the penetration of cuboid intruders near walls into granular matter

When an object penetrates into granular matter near a boundary, it experiences a horizontal repulsion due to the intruder-grain-wall interaction. Here we show experimentally that a square cuboid intruder, released from rest with no initial velocity, neara vertical wall to its left, goes through three distinct kinds of motion: it first tilts clockwise, then 'slides' away from the wall,and finally tilts counterclockwise. This dynamic highly favors both repulsion and penetration of the cuboid intruder as compared to that observed from its release farther away from the wall
Learning Proximal Operator Methods for Massive Connectivity in IoT Networks

Grant-free random access has the potential to sup-port massive connectivity in Internet of Things (IoT) networks, where joint activity detection and channel estimation (JADCE) is a key issue that needs to be tackled. The existing methods for JADCE usually suffer from one of the following limitations: high computational complexity, ineffective in inducing sparsity, and incapable of handling complex matrix estimation. To mitigate all the aforementioned limitations, we in this paper develop an effective unfolding neural network framework built upon the proximal operator method to tackle the JADCE problem in IoT networks, where the base station is equipped with multiple antennas. Specifically, the JADCE problem is formulated as a group-sparse-matrix estimation problem, which is regularized by non-convex minimax concave penalty (MCP). This problem can be iteratively solved by using the proximal operator method, based on which we develop a unfolding neural network structure by parameterizing the algorithmic iterations. By further exploiting the coupling structure among the training parameters as well as the analytical computation, we develop two additional unfolding structures to reduce the training complexity. We prove that the proposed algorithm achieves a linear convergence rate. Results show that our proposed three unfolding structures not only achieve a faster convergence rate but also obtain a higher estimation accuracy than the baseline methods.
Modeling Live Video Streaming: Real-Time Classification, QoE Inference, and Field Evaluation

Social media, professional sports, and video games are driving rapid growth in live video streaming, on platforms such as Twitch and YouTube Live. Live streaming experience is very susceptible to short-time-scale network congestion since client playback buffers are often no more than a few seconds. Unfortunately, identifying such streams and measuring their QoE for network management is challenging, since content providers largely use the same delivery infrastructure for live and video-on-demand (VoD) streaming, and packet inspection techniques (including SNI/DNS query monitoring) cannot always distinguish between the two. In this paper, we design, build, and deploy ReCLive: a machine learning method for live video detection and QoE measurement based on network-level behavioral characteristics. Our contributions are four-fold: (1) We analyze about 23,000 video streams from Twitch and YouTube, and identify key features in their traffic profile that differentiate live and on-demand streaming. We release our traffic traces as open data to the public; (2) We develop an LSTM-based binary classifier model that distinguishes live from on-demand streams in real-time with over 95% accuracy across providers; (3) We develop a method that estimates QoE metrics of live streaming flows in terms of resolution and buffer stall events with overall accuracies of 93% and 90%, respectively; and (4) Finally, we prototype our solution, train it in the lab, and deploy it in a live ISP network serving more than 7,000 subscribers. Our method provides ISPs with fine-grained visibility into live video streams, enabling them to measure and improve user experience.
On the optimal $L^q$-regularity for viscous Hamilton-Jacobi equations with sub-quadratic growth in the gradient

This paper studies a maximal L-regularity property for nonlinear elliptic equations of second order with a zero-th order term and gradient nonlinearities having superlinear and subquadratic growth, complemented with Dirichlet boundary conditions. The approach is based on the combination of linear elliptic regularity theory and interpolation inequalities, so that the analysis of the maximal regularity estimates boils down to determine lower order integral bounds. The latter are achieved via a L duality method, which exploits the regularity properties of solutions to stationary Fokker-Planck equations. For the latter problems, we discuss both global and local estimates. Our main novelties for the regularity properties of this class of nonlinear elliptic boundary-value problems are the treatment of equations with a zero-th order term together with the analysis of the end-point summability threshold q = d(γ − 1)/γ, d being the dimension of the ambient space and γ > 1 the growth of the first-order term in the gradient variable.
Optimal Participation of Heterogeneous, RES-based Virtual Power Plants in Energy Markets

In this work, the optimal participation of heterogeneous, Renewable Energy Source (RES)-based Virtual Power Plant (VPP) in Day-Ahead Market (DAM) and Intra-Day Market (IDM) is studied. For this purpose, a detailed model of the RES-based VPP and of the market operation is needed. The VPP includes both dispatchable and non-dispatchable RESs and flexible demand assets. This paper presents an improved, linear solar thermal plant model to consider its non-linear efficiency curve. A novel demand model with two flexibility levels that are associated with the different market sessions is also proposed. The market operation allows for updates of energy offers and this is used by the VPP to submit DAM auctions and to participate subsequently in IDM to correct for deviations. Finally, the optimal participation of the VPP in energy markets is assessed under different weather conditions.
A comparison study of CNN denoisers on PRNU extraction

Performance of the sensor-based camera identification (SCI) method heavily relies on the denoising filter in estimating Photo-Response Non-Uniformity (PRNU). Given various attempts on enhancing the quality of the extracted PRNU, it still suffers from unsatisfactory performance in low-resolution images and high computational demand. Leveraging the similarity of PRNU estimation and image denoising, we take advantage of the latest achievements of Convolutional Neural Network (CNN)-based denoisers for PRNU extraction. In this paper, a comparative evaluation of such CNN denoisers on SCI performance is carried out on the public"Dresden Image Database". Our findings are two-fold. From one aspect, both the PRNU extraction and image denoising separate noise from the image content. Hence, SCI can benefit from the recent CNN denoisers if carefully trained. From another aspect, the goals and the scenarios of PRNU extraction and image denoising are different since one optimizes the quality of noise and the other optimizes the image quality. A carefully tailored training is needed when CNN denoisers are used for PRNU estimation. Alternative strategies of training data preparation and loss function design are analyzed theoretically and evaluated experimentally. We point out that feeding the CNNs with image-PRNU pairs and training them with correlation-based loss function result in the best PRNU estimation performance. To facilitate further studies of SCI, we also propose a minimum-loss camera fingerprint quantization scheme using which we save the fingerprints as image files in PNG format. Furthermore, we make the quantized fingerprints of the cameras from the"Dresden Image Database"publicly available.
Unpaired topological triply degenerate point for spin-tensor-momentum-coupled ultracold atoms

The realization of triply degenerate points (TDPs) with exotic fermionic excitations has opened a new perspective for the understanding of our nature. Here we explore the coexistence of single unpaired TDP and multiple twofold Weyl points (WPs) and propose an experimental scheme with ultracold pseudospin-1 atomic gases trapped in optical lattices. We show that the predicted single unpaired TDP emerged by the interplay of quadratic spin-vector- and spin-tensor-momentum-coupling could possess a topological nontrivial middle band. This exotic TDP with mirror symmetry breaking is essential diﬀerent from the recently observed TDPs that must appear in pairs due to the Nielsen-Ninomiya theorem and host the topological trivial middle band. Strikingly, the topologically protected Fermi-arc states directly connect the unpaired TDP with additional WPs, in contrast to the conventional Fermi-arc states that connect the same degeneracies of band degenerate points with opposite chirality. Furthermore, the diﬀerent types of TDPs with unique linking structure of Fermi arcs can be readily distinguished by measuring spin texture along high-symmetry lines of the system. Our scheme provides a platform for emerging new fermions with exotic physical phenomena and versatile device applications.
End-to-end Adaptive Distributed Training on PaddlePaddle

Distributed training has become a pervasive and effective approach for training a large neural network (NN) model with processing massive data. However, it is very challenging to satisfy requirements from various NN models, diverse computing resources, and their dynamic changes during a training job. In this study, we design our distributed training framework in a systematic end-to-end view to provide the built-in adaptive ability for different scenarios, especially for industrial applications and production environments, by fully considering resource allocation, model partition, task placement, and distributed execution. Based on the unified distributed graph and the unified cluster object, our adaptive framework is equipped with a global cost model and a global planner, which can enable arbitrary parallelism, resource-aware placement, multi-mode execution, fault-tolerant, and elastic distributed training. The experiments demonstrate that our framework can satisfy various requirements from the diversity of applications and the heterogeneity of resources with highly competitive performance. The ERNIE language model with 260 billion parameters is efficiently trained on thousands of AI processors with 91.7% weak scalability. The throughput of the model from the recommender system by employing the heterogeneous pipeline asynchronous execution can be increased up to 2.1 times and 3.3 times that of the GPU-only and CPU-only training respectively. Moreover, the fault-tolerant and elastic distributed training have been successfully applied to the online industrial applications, which give a reduction of 34.49% in the number of failed long-term training jobs and an increase of 33.91% for the global scheduling efficiency in the production environment.
Real-time Registration and Reconstruction with Cylindrical LiDAR Images

Spinning LiDAR data are prevalent for 3D perception tasks, yet its cylindrical image form is less studied. Conventional approaches regard scans as point clouds, and they either rely on expensive Euclidean 3D nearest neighbor search for data association or depend on projected range images for further processing. We revisit the LiDAR scan formation and present a cylindrical range image representation for data from raw scans, equipped with an efficient calibrated spherical projective model. With our formulation, we 1) collect a large dataset of LiDAR data consisting of both indoor and outdoor sequences accompanied with pseudo-ground truth poses; 2) evaluate the projective and conventional registration approaches on the sequences with both synthetic and real-world transformations; 3) transfer state-of-the-art RGB-D algorithms to LiDAR that runs up to 180 Hz for registration and 150 Hz for dense reconstruction. The dataset and tools will be released.
Revisiting k-Nearest Neighbor Graph Construction on High-Dimensional Data : Experiments and Analyses

The $k$-nearest neighbor graph (KNNG) on high-dimensional data is a data structure widely used in many applications such as similarity search, dimension reduction and clustering. Due to its increasing popularity, several methods under the same framework have been proposed in the past decade. This framework contains two steps, i.e. building an initial KNNG (denoted as \texttt{INIT}) and then refining it by neighborhood propagation (denoted as \texttt{NBPG}). However, there remain several questions to be answered. First, it lacks a comprehensive experimental comparison among representative solutions in the literature. Second, some recently proposed indexing structures, e.g., SW and HNSW, have not been used or tested for building an initial KNNG. Third, the relationship between the data property and the effectiveness of \texttt{NBPG} is still not clear. To address these issues, we comprehensively compare the representative approaches on real-world high-dimensional data sets to provide practical and insightful suggestions for users. As the first attempt, we take SW and HNSW as the alternatives of \texttt{INIT} in our experiments. Moreover, we investigate the effectiveness of \texttt{NBPG} and find the strong correlation between the huness phenomenon and the performance of \texttt{NBPG}.
MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples

Recent advanced methods for fashion landmark detection are mainly driven by training convolutional neural networks on large-scale fashion datasets, which has a large number of annotated landmarks. However, such large-scale annotations are difficult and expensive to obtain in real-world applications, thus models that can generalize well from a small amount of labelled data are desired. We investigate this problem of few-shot fashion landmark detection, where only a few labelled samples are available for an unseen task. This work proposes a novel framework named MetaCloth via meta-learning, which is able to learn unseen tasks of dense fashion landmark detection with only a few annotated samples. Unlike previous meta-learning work that focus on solving “N-way K-shot” tasks, where each task predicts N number of classes by training with K annotated samples for each class (N is fixed for all seen and unseen tasks), a task in MetaCloth detects N different landmarks for different clothing categories using K samples, where N varies across tasks, because different clothing categories usually have various number of landmarks. Therefore, numbers of parameters are various for different seen and unseen tasks in MetaCloth. MetaCloth is carefully designed to dynamically generate different numbers of parameters for different tasks, and learn a generalizable feature extraction network from a few annotated samples with a set of good initialization parameters. Extensive experiments show that MetaCloth outperforms its counterparts by a large margin.
View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT - An Investigation of Potential Improvement

The positive outcome of a trauma intervention depends on an intraoperative evaluation of inserted metallic implants. Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR). The majority of these MAR methods require prior segmentation of the inserted metal objects. Therefore, typically a rather simple thresholding-based segmentation method in the reconstructed 3D volume is applied, despite some major disadvantages. With this publication, the potential of shifting the segmentation task to a learning-based, view-consistent 2D projection-based method on the downstream MAR's outcome is investigated. For segmenting the present metal, a rather simple learning-based 2D projection-wise segmentation network that is trained using real data acquired during cadaver studies, is examined. To overcome the disadvantages that come along with a 2D projection-wise segmentation, a Consistency Filter is proposed. The influence of the shifted segmentation domain is investigated by comparing the results of the standard fsMAR with a modified fsMAR version using the new segmentation masks. With a quantitative and qualitative evaluation on real cadaver data, the investigated approach showed an increased MAR performance and a high insensitivity against metal artifacts. For cases with metal outside the reconstruction's FoV or cases with vanishing metal, a significant reduction in artifacts could be shown. Thus, increases of up to roughly 3 dB w.r. t. the mean PSNR metric over all slices and up to 9 dB for single slices were achieved. The shown results reveal a beneficial influence of the shift to a 2D-based segmentation method on real data for downstream use with a MAR method, like the fsMAR. The nature of the method further suggests the same beneficial behavior for all (also recent data-driven) MAR methods, that for now comprise a 3D-volume-based segmentation step for subsequent inpainting.
Unifying the order and disorder dynamics in photoexcited VO2

Significance Structural phase transitions driven optically through the nonequilibrium population of excited electronic states have attracted great interest in many scientific disciplines as offering ultrafast control over the matter on timescales near the limit of atomic motions. However, atomic order or disorder characteristics are currently a critical issue in ultrafast photoinduced phase transitions. In this work, using an advanced real-time time-dependent density functional theory quantum simulation, we uncover the order and disorder processes strongly depending on the competition between thermal vibrations and photoinduced deterministic motion of atoms. We theoretically unify the order and disorder dynamics and provide a complete microscopic mechanism.
Development of a multistage current-controlled switched-capacitor step-down DC/DC converter with continuous input current

This paper presents the design and analysis of a low-profile multistage current-controlled switched-capacitor (SC) step down DC/DC converter. The converter not only exhibits all advantages of classical SC converters, but also features good regulation capability and continuous input current waveform, resulting in low conducted electromagnetic interference with the supply network. The concept of energy transfer is achieved by paralleling the input and output of two step-down converter cells and operating them in antiphase. The voltage conversion ratio is determined by controlling the charging trajectories of the capacitors in each cell. As it is unnecessary for the circuit to use any inductive elements, possibilities of integrated circuit fabrication and high power density are promising. By applying the state-space averaging technique, a third-order state-space model for an n-stage converter is derived. Static and small-signal dynamic behaviors of the converter are investigated. A 70 W 48 V/12 V converter is implemented. Analytical predictions are verified with experimental measurements.