Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Multimodal Movie Scene Segmentation Using Mixture of Acoustic Experts

Scenes are the most basic semantic units of a movie that are important as pre-processing for various multimedia computing technology. Previous scene segmentation studies have introduced constraints and alignment mechanisms to cluster low-level frames and shots based on the visual features and temporal properties. Recent researchers have extended by using multimodal semantic representations with the acoustic representations blindly extracted by a universal pretrained model. They tend to ignore the semantic meaning of audio and complex interaction between the audio and visual representations for scene segmentation. In this work, we introduce a mixture-of-audio-experts (MOAE) framework to integrate acoustic experts and multimodal experts for scene segmentation. The acoustic expert is learned to model different acoustic semantics, including speaker, environmental sounds, and other events. The MOAE optimizes the weights delicately among various multimodal experts and achieves a state-of-the-art 61.89% F1-score for scene segmentation. We visualize the expert weights in our framework to illustrate the complementary properties among diverse experts, leading to improvements for segmentation results.
Last-mile Matters: Mitigating the Tail Latency of Virtualized Networks with Multipath Data Plane

Virtualized network has become the cornerstone of today's large-scale cloud data centers. In particular, the data plane of virtualized network, consisting of virtual switch, virtual router and other software network functionalities, performs all network packets processing of virtual machines (VMs). However, current virtualized data plane solutions incur drastic performance interference with co-resident VMs, and thus suffer from unpredictable network performance, especially in terms of tail latency. In this work, we show that the performance issue stems from the fact that CPU plays a dual role of both communication and computation in virtualized networks. A number of virtual network components and their complex packets processing create an undue burden on the hosts' CPUs and in turn cause the mutual performance interference among VMs and networks. To address this issue, we present a multipath data plane solution, where the traffic of VMs can be adaptively and seamlessly offloaded to the adjacent hosts. At the core of this design is to optimize the VM traffic allocation among multiple paths. We formulate the VM multipath traffic allocation problem with coupled variables of computing and network resources, which were only considered as mutually independent in prior researches. Then we present a distributed algorithm to efficiently solve the large-scale, interdependent global optimization problem, with convergence and optimality guarantees. Through extensive simulations and real-world testbed experiments, we show that our solution delivers consistent performance improvement (up to $6.7\times$ improvement in aggregate throughput and $21.4\times$ reduction in tail latency, respectively) in the dynamic cloud system.
Improved YOLOv5-based Method for Recognition of Insulator String Pins

Aiming at the problem that the substation insulator replacement robot cannot accurately identify insulator pins in the complex outdoor light environment, an improved YOLOv5-based insulator pin identification method YOLOv5-CBP is proposed. For the substation insulator pin recognition task, the backbone of the network is replaced by the lightweight EfficientNet-lite0; the Neck of the network is optimized and a more efficient feature fusion module BiFPN combined with an attention mechanism is introduced. The bounding box regression loss function was changed to improve the accuracy of inspection frame detection. Finally, the improved algorithm is compared with the original YOLOv5 algorithm on a self-built dataset for experiments. The results show that the insulator pin recognition accuracy is improved by 8.3%, the size of the algorithm model is reduced to 54% of the original, and the number of parameters is reduced by 49.4%. The improved algorithm model more lightweight.
Standalone Power Management System for Flexible Piezo Electric Nano Generators (PENG) Based on the Co-Polymer P(VDF:TrFE)

This paper presents the design and manufacturing of a PENG based on flexible PVDF. The power management system (PMS) used to convert its ac voltage into a dc one, providing MPPT is presented. The system is tested to cover two use cases, namely energy storage in a battery and in a standalone configuration. The PMS is able to produce adjustable output voltages ranging from 0.8V to 1.5V. With a regulated 1.5V the PMS is capable of producing up to 900nJ of energy from bending single deformation of the harvester. The PMS can harvest as low as 1. 4μW from the PENG. A low power MPPT system, consisting of a single comparator is shown. In addition, insights about selecting optimal input capacitance of the PMS under different use cases is given in this paper. The MPPT technique improved the harvested output power by up to 44%.
High Dimensional Trellis Coded Modulation

Abstract : This work develops a high dimensional circular trellis-coded modulation with permuted state structure (HDCTCM) for power-limited spread spectrum channels. High dimensional simplex signal constellation is systematically developed to achieve the optimal energy efficiency and maximize the minimum distance among error events for any size trellis. By systematically analyzing the error events, sets of state transitions that should be assigned simplex are identified. Butterfly structure of circular trellis coding is successfully related to those state transitions and perfectly aligned into a multidimensional matrix. An algebraic representation of a simplex in a high dimensional space is introduced, and a corresponding signal constellation and symbol assignment procedure are built. A circular trellis code has an unknown starting state; conventional algorithms for calculating the minimum distance of a trellis code always assume a known starting state. This work develops a computational algorithm to calculate minimum distance of circular trellis codes. The coding gain of HDCTCM is evaluated after the minimum distance is obtained. Tight upper and lower bonds on the minimum distance of HDCTCM codes are derived. Furthermore, a method to build codes that have the bounds of the minimum distance is developed. Whereas, in most coding schemes, the optimal distance codes can only be obtained through exhaustive search.
Noncoherent Sparse Subarrays for DOA Estimation Based on Low-Rank and Sparse Recovery

This paper investigates the problem of noncoher-ent direction-of-arrival (DOA) estimation using different sparse subarrays. In particular, we present a Multiple Measurements Vector (MMV) model for noncoherent DOA estimation based on a low-rank and sparse recovery optimization problem. Moreover, we develop two different practical strategies to obtain sparse arrays and subarrays: i) the subarrays are generated from a main sparse array geometry (Type-I sparse array), and ii) the sparse subarrays are directly designed and grouped together to generate the whole sparse array (Type-II sparse array). Numerical results demonstrate that the proposed MMV model can benefit from multiple data records and that Type-II sparse noncoherent arrays are superior in performance for DOA estimation.
Configurable Universal QC-LDPC Encoder Architecture Design

Quasi-cyclic low-density parity-check (QC-LDPC) codes are widely used owning to its good performance and easy hardware implementation. However, most classical LDPC encoder designs do not support changing the LDPC code types or parameters according to different communication services and channel conditions. In this paper, we propose an encoder architecture with configurable code types, input information block lengths, coding rates and degrees of parallelism, which can realize “hot-switching” of configuration parameters. A new scheme that makes full use of the characteristics of the generation matrix is used in the core of parallel coding. The implementation on field programmable gate array (FPGA) shows that the throughput of the encoder can reach 1.6 Gbps under the condition of 7/8 coding rate (8176, 7154) and the degree of parallelism set to 8.
Predicting GPU Performance and System Parameter Configuration Using Machine Learning

GPUs are widely used in accelerating computation-intensive applications. Performance models are important for designing high-performance and cost-efficient GPUs. In this work, we developed machine learning models that can accurately predict GPU system performance. Our model can identify important features that can provide insights to designers on the most important hardware system parameters when executing their applications. We also developed a model for predicting minimum system configuration parameters based on performance. Our model can provide system configuration recommendations for users to meet their performance requirements.
Look-Ahead Reinforcement Learning for Load Balancing Network Traffic

Given the growth in complexity and scale of computer networks and considering that the leading cause of failures is human error, there is an increasing interest in minimizing the role of humans in network management. In this context, we propose a two-step, machine learning approach for automatically balancing network flows. Firstly, we rely on identifying flows that can more heavily impact the network, i.e., elephant flows. Sec-ondly, we use reinforcement learning to determine the best action to be performed in the network, given its state. The intuition for this two-step approach is to amortize the computational costs of reinforcement learning and apply it only to flows that can cause a high impact on network performance. Our main contributions are (i) problem modeling as a function of states and actions to balance network traffic and (ii) an architecture that more judiciously uses reinforcement learning on flows of interest for load balancing.
Wavelet decomposition of event related potentials: toward the definition of biologically natural components

Transient time-voltage neural waveforms recorded at the scalp are elicited by discrete sensory or perceptual events and thought to be composed of fundamental waveform components. A new definition is proposed of ERP components as 'wavepackets', which are oscillating waveforms that extend throughout the ERP. The wavelet transform is used to extract orthogonal wavepacket components. Each component is restricted to its own frequency band, and may strongly influence the ERPs over local time regions. Unilateral visual field stimulation data from human subjects show that wavepacket components reveal visual processing events earlier than direct amplitude measurements on the ERPs. Wavepackets provide new insights into ERP morphology and its relationship to the timecourse of neural processes. <<ETX>>
Reduced Kernel Dictionary Learning

In this paper we present new algorithms for training reduced-size nonlinear representations in the Kernel Dictionary Learning (KDL) problem. Standard KDL has the drawback of a large size of the kernel matrix when the data set is large. There are several ways of reducing the kernel size, notably Nyström sampling. We propose here a method more in the spirit of dictionary learning, where the kernel vectors are obtained with a trained sparse representation of the input signals. Moreover, we optimize directly the kernel vectors in the KDL process, using gradient descent steps. We show with three data sets that our algorithms are able to provide better representations, despite using a small number of kernel vectors, and also decrease the execution time with respect to KDL.
ON THE RATE OF CONVERGENCE FOR MARUYAMA'S APPROXIMATE SOLUTIONS OF STOCHASTIC DIFFERENTIAL EQUATIONS

with $X(0$] $\equiv X_{0}$ , where $\sigma(t, x$ ] $:=\{\sigma_{\ell}^{j}(t, x], 1\leqq i\leqq r, 1\leqq j\leqq d\}$ is a Borel measurable function $(t, x$ ] $\in[0,1]\times R^{a}\rightarrow R^{d}\otimes R^{r}$ and $b(t, x$ ] $:=\{b^{j}(t, x], 1\leqq j\leqq d\}$ is a Borel measurable function $(t, x$ ] $\in[0,1]\times R^{a}\rightarrow R^{a}$ and $B(t$] is a r-dimensional standard Brownian motion ( $r\geqq 1$ ]. SuPpose that $\sigma(\cdot, $] and $ b(\cdot, \cdot$ ] are Lipschitz continuous and bounded, namely, there exist positive constants $L_{1}$ and $L_{2}$ independent of $x,$ $y,$ $s$ and $t$ such that
Research on Generative Adversarial Networks and Their Applications in Image Generation

This paper first introduces the basic principles, model structures, and advantages and disadvantages of generative adversarial networks. Then we give a detailed introduction to three application areas of generative adversarial networks in image generation: medical imaging, 3D reconstruction, and image fusion. Finally, the development trend of generative adversarial networks and their applications in the field of image generation prospects.
Robust Acoustic Contrast Control with Positive Semidefinite Constraint Using Iterative POTDC Algorithm

Personal sound zone systems require to know the acoustic transfer functions (ATFs) between the loudspeakers and matching microphones, which in general have to be measured in advance. As the measurement is affected by such factors as sensors’ position mismatches and environmental changes, deviation of the measured ATF-s from the true ones is unavoidable and such perturbation may lead to significant performance degradation in sound zone production. In this work, a method is developed for robust acoustic contrast control, which simultaneously considers the norm-bounded uncertainties and a positive semidefinite constraint to ensure the optimization problem to have a solution. Since the problem is non-convex, the polynomial time difference-of-convex functions (POTDC) algorithm is adopted to identify the optimal solution. Simulations are performed to validate the effectiveness of the proposed method and it demonstrates a performance superior to other existing robust control methods.
Demand Side Management Pricing Method Based on LSTM and A3C in Cloud Environment

This electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. DSM can effectively reduce the peak load and fill the valley, and improve the stability and operation efficiency of power system. With the development of the power Internet of things, the control structure at the edge of cloud management is becoming more and more popular. For the protection of users' privacy, users' power consumption information can only be used locally after collection and cannot be uploaded further, which makes it difficult for load aggregators to make pricing decisions. This paper proposes a demand side management method based on asynchronous advanced actor critical algorithm (A3C) reinforcement learning algorithm and long short term memory (LSTM) network in cloud edge environment, and solves the problem of lack of foresight in demand side management decision-making through reinforcement learning process; Through the structure of distributed learning and centralized decision-making, the local utilization of user information is realized, and the confusion in actual implementation is reduced; The virtual environment based on LSTM network accelerates the learning process and reduces the cost of algorithm implementation. Through the example analysis, it can be seen that the decision-making method described in this paper can effectively speed up the learning process while ensuring users' privacy, and price decision-making can more accurately grasp the characteristics of users' response behavior and ensure the economy of decision-making.
Journal Pre-proof Polyvalent Guide RNAs for CRISPR Antivirals

This a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. effector Cas13 recognizes and degrades RNA molecules that are complementary to its guide RNA (gRNA) and possesses potential as an antiviral biotechnology because it can degrade viral mRNA and RNA genomes. Because multiplexed targeting is a critical strategy to improve viral suppression, we developed a strategy to design of gRNAs where individual gRNAs have maximized activity at multiple viral targets, simultaneously, by exploiting the molecular biophysics of promiscuous target recognition by Cas13. These “polyvalent” gRNA sequences (“pgRNAs”) provide superior antiviral elimination across tissue/organ scales in a higher organism ( Nicotiana benthamiana ) compared to 18 conventionally-designed gRNAs—reducing detectable viral RNA by >30- fold, despite lacking perfect 19 complementarity with either of their targets and, when multiplexed, reducing viral RNA by >99.5%. Pairs 20 of pgRNA-targetable sequences are abundant in the genomes of RNA viruses, and this work highlights 21 the need for specific approaches to the challenges of targeting viruses in eukaryotes using CRISPR.
Active Disturbance Rejection Decoupling Control of Grid-Connected PWM Converter

With the increasing penetration of renewable energy integrated into the power system and increasing demand for flexible power conversion, pulse width modulated (PWM) converter is being extensively used in many areas. Either working in the rectification of inversion mode, the PWM converter is usually connected to the grid. Appropriate control strategies are essential in guaranteeing the performance of the grid-connected PWM converters for various applications. There exist some adverse disturbances such as parameter variations, source and load changes, measurement disturbance that can affect the control performance in the grid-connected PWM converter system. The active disturbance rejection control (ADRC) is a promising candidate controller to address the issues caused by disturbances. This paper investigates the ADRC for decoupling control of the grid-connected PWM converter. When the converter works as a rectifier, the main control objective is the DC voltage and reactive power (or power factor). When the converter works as an inverter, the main control objective is the AC current and power factor. Based on modeling analysis, different disturbances in the system can be lumped as the total disturbance and be estimated by an extended state observer (ESO) and then be compensated by the dynamic feedback in an appropriately designed controller. The simulation results demonstrate the feasibility of analysis and effectiveness of the proposed controller in dealing with disturbances.
Joint Channel Estimation and Hybrid Beamforming via Deep-Unfolding

In this work, we propose an end-to-end deep-unfolding neural network (NN) based joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the sum rate in massive multiple-input multiple-output (MIMO) systems. Specifically, the recursive least-squares (RLS) and stochastic successive convex approximation (SSCA) algorithms are unfolded for channel estimation and hybrid beamforming, respectively. We consider a mixed-timescale scheme, where analog beamforming matrices are designed based on the channel state information (CSI) statistics once in each frame, while the digital beamforming matrices are designed at each time slot based on the equivalent CSI matrices. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms.
Dynamic Anchor Point Selection in Software Defined Distributed Mobility Management

Distributed mobility management (DMM) solution is proposed to address the downsides of centralized mobility management protocols. The standard DMM is proposed for flat architectures and always selects the anchor point from access layer. Numerical analysis is used in this paper to show that dynamic anchor point selection can improve the performance of standard DMM in terms of packet signalling and delivery cost. In next step, an SDN-based DMM solution that we refer to as SD-DMM is presented to provide dynamic anchor point selection for hierarchical mobile network architecture. In SD-DMM, the anchor point is dynamically selected for each mobile node by a virtual function implemented as an application on top of the SDN controller which has a global view of the network. The main advantages of SD-DMM is to decrease packet delivery cost.
Statistical Analysis of Randomness in Training of Small-Scale Neural Networks for Speech Enhancement

For the training of neural networks, randomness due to the initialization is a widely acknowledged factor for training success. In speech enhancement, randomness is also introduced via the creation of the training dataset due to the synthetic mixing of speech and noise, and other data augmentation techniques. This aspect of randomness is often neglected in literature. In this contribution, we quantify the effect of randomness in the generation of the training dataset in comparison to the randomness introduced by the network initialization and find that for small networks and very small datasets, the assembly of the training dataset has a significant influence on the network’s performance.
An Analysis of Using Fuzzy Annotations in CRNN-Based Joint Beat and Downbeat Tracking

This paper addresses joint beat and downbeat tracking by using a Convolutional Recurrent Neural Network (CRNN) trained on Mel-spectral and chroma features of musical audio signals. Since beats and downbeats occur sparsely, we extend the original beat/downbeat annotation ranges to create fuzzy annotations for a better-balanced training. We compare three fuzzy annotations generated by convolving the original annotations with triangular, Gaussian, and rectangular windows. In comparison to the baseline model trained with the original annotations, our model of using fuzzy annotations improves the beat and downbeat tracking performance on pieces with tempi slower than 80 bpm. We analyzed the activation functions from the CRNN outputs and found that the improvements are mainly due to enlarged activation functions and phase error correction. The results showed that the proposed models provide promising results on test datasets with various music styles.
Gaussian Processes for Topology Inference of Directed Graphs

In machine learning applications, the data are often high-dimensional and intrinsically related. It is often of interest finding the underlying structure and the causal relationships of the data and representing the findings with directed graphs. In this paper, we study multivariate time series, where each series is associated with a node of a graph, and where the objective is estimating the topology of the graph that reflects how the nodes of the graph affect each other, if at all. We propose a novel Bayesian method which allows for nonlinear and multiple lag relationships among the time series. The method is based on Gaussian processes, and it treats the entries of the adjacency matrix as hyperparameters. The method employs an automatic relevance determination (ARD) kernel and allows for learning of the mapping function from selected past data to current data. The resulting adjacency matrix provides the intrinsic structure and answers questions related to causality. Numerical tests show that the proposed method has comparable or better performance than state-of-the-art methods.