Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison Study of DQN and PPO based Reinforcement Learning for Scheduling Workﬂows in the Cloud

As a new mode of resource and service provision, cloud computing provides a cost-effective deploying environment for hosting scientiﬁc applications. More and more applications modeled as work-ﬂows are being moved to the cloud. However, the traditional heuristic and metaheuristic algorithms encounter some problems that cannot be ignored in solving the scheduling problem, such as difﬁcult to get the optimal solution or consuming too much time. In this work, we design the corresponding workﬂow scheduling model based on Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithm with makespan minimized, and explore the performance difference of DQN and PPO algorithms in workﬂow scheduling through different experiments. Firstly, a cloud workﬂow scheduling environment is developed based on the cloud workﬂow task model. Secondly, according to the DQN and PPO algorithm, the agent models are designed. Then we train the resulting workﬂow scheduling models. During the training process, the agent (actor) learns and accumulates experience constantly to optimize the scheduling results. Finally, experiments are conducted and results show that both PPO algorithm and DQN algorithm show excellent characteristics in solving the cloud workﬂow scheduling problem, which also reﬂects the advantages of deep reinforcement learning in sequential decision optimization.
Recent Results of Machine Learning Inspired Wireless Communications

—It is expected that in the future, main core technologies will be based on machine learning (ML) which is a subsection of artiﬁcial intelligence (AI). So called intelligence will be embedded into Many futuristic applications like Internet-of-things (IoT), mobile communications, autonomous vehicles by resorting to the AI or ML. Moreover, it is a recent trend to deploy ML technology to 5G wireless communications to address challenges in physical layer design, decision making, network management and resource management. Thus, in this survey, the recent advances in ML and related methods in relation to wireless communications are reviewed, which are classiﬁed as: ML in wireless system applications, ML for optimizing wireless communication systems and also application of ML in 5G. Moreover, application of ML in 5G explores the utilization of ML in 5G resource management and 5G transmission technologies. Finally, future directions of ML in wireless communication is discussed.
A Network Security Situation Awareness Model Based on Artificial Immune

Network security situation awareness is a new technology for network security monitoring and early warning, and is a hot spot in the area of network security. In this paper, the artificial immune technology is applied to the study of network security situation awareness, and a model is presented and implemented. The paper adopts the artificial immune-based intrution detection technology to real-timely monitor network attacks, and introduces vaccination mechanism to effectively improve the real-time network attack detection abilities of network hosts. In addition,the paper puts forward the concept of network group according to idiotypic immune network theory, and establishes a real-time and quantitive network risk assessment sub-model based on antibody concentration. The changes of antibody concentration are not only related to intrusion intensity, but also to interactions with antibodies in the same network group. To predict future network security situation trends,the paper adopts time-series prediction mechanism based on cloud models. Theoretical analysis and experimental results show that the model is effective to network security situation awareness with advantages of real-time and high accuracy.
Solving Newton’s equations of motion with large timesteps using recurrent neural networks based operators

Classical molecular dynamics simulations are based on solving Newton’s equations of motion. Using a small timestep, numerical integrators such as Verlet generate trajectories of particles as solutions to Newton’s equations. We introduce operators derived using recurrent neural networks that accurately solve Newton’s equations utilizing sequences of past trajectory data, and produce energy-conserving dynamics of particles using timesteps up to 4000 times larger compared to the Verlet timestep. We demonstrate significant speedup in many example problems including 3D systems of up to 16 particles.
Cross-modal Music Emotion Recognition Using Composite Loss-based Embeddings

—Most music emotion recognition approaches use one-way classiﬁcation or regression that estimates a general emotional category from a distribution of music samples, but without considering emotional variations (e.g., happiness can be further categorised into much, moderate or little happiness). We propose a cross-modal music emotion recognition approach that associates music samples with emotions in a common embedding space by considering both general emotional categories and ﬁne-grained discrimination within each category. Since the association of music samples with emotions is uncertain due to subjective human perceptions, we compute composite loss-based embeddings obtained to maximise two statistical characteristics, one being the correlation between music samples and emotions based on canonical correlation analysis, and the other being a probabilistic similarity between a music sample and an emotion with KL-divergence. The experiments on two benchmark datasets demonstrate the effectiveness of our cross-modal approach, the composite loss and learned acoustic features. In addition, detailed analysis shows that our approach can accomplish robust cross-modal music emotion recognition that not only identiﬁes music samples matching with a speciﬁc emotion but also detects emotions expressed in a certain music sample.
Prototypical many-body signatures in transport properties of semiconductors

We devise a methodology for charge, heat, and entropy transport driven by carriers with finite lifetimes. Combining numerical simulations with analytical expressions for low temperatures, we establish a comprehensive and thermodynamically consistent phenomenology for transport properties in semiconductors. We demonstrate that the scattering rate (inverse lifetime) is a relevant energy scale: It causes the emergence of several characteristic features in each transport observable. The theory is capable to reproduce -- with only a minimal input electronic structure -- the full temperature profiles measured in correlated narrow-gap semiconductors. In particular, we account for the previously elusive low-$T$ saturation of the resistivity and the Hall coefficient, as well as the (linear) vanishing of the Seebeck and Nernst coefficient in systems, such as FeSb$_2$, FeAs$_2$, RuSb$_2$ and FeGa$_3$.
Sherman: A Write-Optimized Distributed B+Tree Index on Disaggregated Memory

Memory disaggregation architecture physically separates CPU and memory into independent components, which are connected via high-speed RDMA networks, greatly improving resource utilization of databases. However, such an architecture poses unique challenges to data indexing due to limited RDMA semantics and near-zero computation power at memory-side. Existing indexes supporting disaggregated memory either suffer from low write performance, or require hardware modification. This paper presents Sherman, a write-optimized distributed B+Tree index on disaggregated memory that delivers high performance with commodity RDMA NICs. Sherman combines RDMA hardware features and RDMA-friendly software techniques to boost index write performance from three angles. First, to reduce round trips, Sherman coalesces dependent RDMA commands by leveraging in-order delivery property of RDMA. Second, to accelerate concurrent accesses, Sherman introduces a hierarchical lock that exploits on-chip memory of RDMA NICs. Finally, to mitigate write amplification, Sherman tailors the data structure layout of B+Tree with a two-level version mechanism. Our evaluation shows that, Sherman is one order of magnitude faster in terms of both throughput and 99th percentile latency on typical write-intensive workloads, compared with state-of-the-art designs.
Co-training Transformer with Videos and Images Improves Action Recognition

In learning action recognition, models are typically pre-trained on object recognition with images, such as ImageNet, and later fine-tuned on target action recognition with videos. This approach has achieved good empirical performance especially with recent transformer-based video architectures. While recently many works aim to design more advanced transformer architectures for action recognition, less effort has been made on how to train video transformers. In this work, we explore several training paradigms and present two findings. First, video transformers benefit from joint training on diverse video datasets and label spaces (e.g., Kinetics is appearance-focused while SomethingSomething is motion-focused). Second, by further co-training with images (as single-frame videos), the video transformers learn even better video representations. We term this approach as Co-training Videos and Images for Action Recognition (CoVeR). In particular, when pretrained on ImageNet-21K based on the TimeSFormer architecture, CoVeR improves Kinetics-400 Top-1 Accuracy by 2.4%, Kinetics-600 by 2.3%, and SomethingSomething-v2 by 2.3%. When pretrained on larger-scale image datasets following previous state-of-the-art, CoVeR achieves best results on Kinetics-400 (87.2%), Kinetics-600 (87.9%), Kinetics-700 (79.8%), SomethingSomething-v2 (70.9%), and Moments-in-Time (46.1%), with a simple spatio-temporal video transformer.
Modeling Tidal Current Profiles by Means of Empirical Orthogonal Functions

The method of empirical orthogonal functions (EOFs) is used to model vertical velocity profiles of the current. The whole current field is decomposed into time series of along and cross-slope velocity components. These time series are then filtered keeping only the frequency bands corresponding to the most significant peaks of the current power density spectra, which in most cases correspond to the main semidiurnal and long period tidal components. New time series are originated containing only filtered current. For each one of these filtered time-series, EOFs and the respective principal components are then derived. The derivation of empirical orthogonal functions make possible the separation of the local flow variability into a few modes of variance. In a general way, the along-slope flow may be described mainly as barotropic, although the baroclinic contribution tends to reach some significance in the flow crossing the shelf slope.
Uncertainties in quantum measurements: a quantum tomography

The observables associated with a quantum system S form a non-commutative algebra AS . It is assumed that a density matrix ρ can be determined from the expectation values of observables. But AS admits inner automorphisms a↦uau−1,a,u∈AS , u*u=uu*=1 , so that its individual elements can be identified only up to unitary transformations. So since Tr ρ(uau*) = Tr(u*ρu)a, only the spectrum of ρ, or its characteristic polynomial, can be determined in quantum mechanics. In local quantum field theory, ρ cannot be determined at all, as we shall explain. However, abelian algebras do not have inner automorphisms, so the measurement apparatus can determine mean values of observables in abelian algebras AM⊂AS (M for measurement, S for system). We study the uncertainties in extending ρ|AM to ρ|AS (the determination of which means measurement of AS ) and devise a protocol to determine ρ|AS≡ρ by determining ρ|AM for different choices of AM . The problem we formulate and study is a generalization of the Kadison–Singer theorem. We give an example where the system S is a particle on a circle and the experiment measures the abelian algebra of a magnetic field B coupled to S. The measurement of B gives information about the state ρ of the system S due to operator mixing. Associated uncertainty principles for von Neumann entropy are discussed in the appendix, adapting the earlier work by Białynicki-Birula and Mycielski (1975 Commun. Math. Phys. 44 129) to the present case.
Pressure Sensor with New Electrical Circuit Utilizing Bipolar Junction Transistor

High sensitivity MEMS pressure sensor chip for different ranges (1 to 60 kPa) utilizing the novel electrical circuit of piezosensitive differential amplifier with negative feedback loop (PDA-NFL) is developed. Pressure sensor chip PDA-NFL utilizes two bipolar-junction transistors (BJT) with vertical n-p-n type structure (V-NPN) and eight piezoresistors (p–type). Both theoretical model of sensor response to pressure and temperature and experimental data are presented. Data confirms the applicability of theoretical model. Introduction of the amplifier allows for decreasing chip size while keeping the same sensitivity as a chip with classic Wheatstone bridge circuit. Keywords—pressure sensor, bipolar junction transistor, differential amplifier, high sensitivity, temperature compensation.
Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic Algorithm

In recent years, people from all over the world are suffering from one of the most severe diseases in history, known as Coronavirus disease 2019, COVID-19 for short. When the virus reaches the lungs, it has a higher probability to cause lung pneumonia and sepsis. X-ray image is a powerful tool in identifying the typical features of the infection for COVID-19 patients. The radiologists and pathologists observe that ground-glass opacity appears in the chest X-ray for infected patient \cite{cozzi2021ground}, and it could be used as one of the criteria during the diagnosis process. In the past few years, deep learning has proven to be one of the most powerful methods in the field of image classification. Due to significant differences in Chest X-Ray between normal and infected people \cite{rousan2020chest}, deep models could be used to identify the presence of the disease given a patient's Chest X-Ray. Many deep models are complex, and it evolves with lots of input parameters. Designers sometimes struggle with the tuning process for deep models, especially when they build up the model from scratch. Genetic Algorithm, inspired by the biological evolution process, plays a key role in solving such complex problems. In this paper, I proposed a genetic-based approach to optimize the Convolutional Neural Network(CNN) for the Chest X-Ray classification task.
Robust Output Regulation Approach for Attitude Control and Momentum Management of the Space Station

An novel methodology to solve the attitude control and momentum management problem of the space station with model uncertainties is developed. By the use of the robust output regulation theory, the attitude control and momentum management integrating model and the internal model of disturbances are combined to design a robust coupling controller such that the plant outputs track desired reference signals and the model uncertainties can be tolerated. It is proved that the designed controller guarantees stability of the closed-loop system and suppresses the disturbance well. Simulation results are provided to demonstrate the feasibility of the proposed method.
Conversational Search with Mixed-Initiative - Asking Good Clarification Questions backed-up by Passage Retrieval

We deal with the scenario of conversational search, where user queries are under-specified or ambiguous. This calls for a mixed-initiative setup. User-asks (queries) and system-answers, as well as system-asks (clarification questions) and user response, in order to clarify her information needs. We focus on the task of selecting the next clarification question, given conversation context. Our method leverages passage retrieval from background content to fine-tune two deep-learning models for ranking candidate clarification questions. We evaluated our method on two different use-cases. The first is an open domain conversational search in a large web collection. The second is a task-oriented customer-support setup.We show that our method performs well on both use-cases.
A systematic reliability investigation of the dielectric charging process in electrostatically actuated MEMS based on Kelvin probe force microscopy

This paper presents a comprehensive investigation for the dielectric charging problem in electrostatically actuated microelectromechanical system (MEMS) devices. The approach is based on Kelvin probe force microscopy (KPFM) and targets, in this specific paper, thin PECVD silicon nitride films for electrostatic capacitive RF MEMS switches. KPFM has been employed in order to mimic the potential induced at the dielectric surface due to charge injection through asperities. The effect of dielectric thickness has been investigated through depositing SiNx films with different thicknesses. Then, in order to simulate the different scenarios of dielectric charging in real MEMS switches, SiNx films have been deposited over thermally grown oxide, evaporated gold and electroplated gold layers. Also, the effect of the deposition conditions has been investigated through depositing dielectric films using low and high frequency PECVD methods. The investigation reveals that thin dielectric films have larger relaxation times compared to thick ones when the same injection bias is applied, independently of the substrate nature. For the same SiNx film thickness, the decay time constant is found to be smaller in dielectric films deposited over metallic layers compared to the ones deposited over silicon substrates. Finally, the material stoichiometry is found to affect the surface potential distribution as well as the relaxation time constant.
Real-Time Neural Voice Camouflage

Automatic speech recognition systems have created exciting possibilities for applications, however they also enable opportunities for systematic eavesdropping. We propose a method to camouflage a person's voice over-the-air from these systems without inconveniencing the conversation between people in the room. Standard adversarial attacks are not effective in real-time streaming situations because the characteristics of the signal will have changed by the time the attack is executed. We introduce predictive attacks, which achieve real-time performance by forecasting the attack that will be the most effective in the future. Under real-time constraints, our method jams the established speech recognition system DeepSpeech 3.9x more than baselines as measured through word error rate, and 6.6x more as measured through character error rate. We furthermore demonstrate our approach is practically effective in realistic environments over physical distances.
Charting galactic accelerations II: how to 'learn' accelerations in the solar neighbourhood

Gravitational acceleration fields can be deduced from the collisionless Boltzmann equation, once the distribution function is known. This can be constructed via the method of normalizing flows from datasets of the positions and velocities of stars. Here, we consider application of this technique to the solar neighbourhood. We construct mock data from a linear superposition of multiple ‘quasi-isothermal’ distribution functions, representing stellar populations in the equilibrium Milky Way disc. We show that given a mock dataset comprising a million stars within 1 kpc of the Sun, the underlying acceleration field can be measured with excellent, sub-percent level accuracy, even in the face of realistic errors and missing line-of-sight velocities. The effects of disequilibrium can lead to bias in the inferred acceleration field. This can be diagnosed by the presence of a phase space spiral, which can be extracted simply and cleanly from the learned distribution function. We carry out a comparison with two other popular methods of finding the local acceleration field (Jeans analysis and 1D distribution function fitting). We show our method most accurately measures accelerations from a given mock dataset, particularly in the presence of disequilibria.
Verifying a Minimalist Reverse-Mode AD Library

By exploiting a number of relatively subtle programming language features, including dynamically-allocated mutable state, ﬁrst-class functions, and eﬀect handlers, reverse-mode automatic diﬀerentiation can be implemented as a library. One outstanding question, however, is: with which logical tools can one specify what this code is expected to compute and verify that it behaves as expected? We answer this question by using a modern variant of Separation Logic to specify and verify a minimalist (but concise and elegant) reverse-mode automatic diﬀerentiation library. We view this result as an advanced exercise in program veriﬁcation, with potential future applications to more realistic automatic diﬀerentiation systems.
Improving Spectral Graph Convolution for Learning Graph-level Representation

From the original theoretically well-deﬁned spectral graph convolution to the subsequent spatial bassed message-passing model, spatial locality (in vertex domain) acts as a fundamental principle of most graph neural networks (GNNs). In the spectral graph convolution, the ﬁlter is approximated by polynomials, where a k -order polynomial covers k -hop neighbors. In the message-passing, various deﬁ-nitions of neighbors used in aggregations are actually an extensive exploration of the spatial locality information. For learning node representations, the topological distance seems necessary since it characterizes the basic relations between nodes. However, for learning representations of the entire graphs, is it still necessary to hold? In this work, we show that such a principle is not necessary, it hinders most existing GNNs from efﬁciently encoding graph structures. By removing it, as well as the limitation of polynomial ﬁlters, the resulting new architecture signiﬁcantly boosts performance on learning graph representations. We also study the effects of graph spectrum on signals and interpret various existing improvements as different spectrum smoothing techniques. It serves as a spatial understanding that quantitatively measures the effects of the spectrum to input signals in comparison to the well-known spectral understanding as high/low-pass ﬁlters. More importantly, it sheds the light on developing powerful graph representation models.
Calculation of the total energy of a diatomic molecule in the first order of perturbation theory taking into account the Pauli principle and plasma oscillations of atomic electrons

CALCULATION OF THE TOTAL ENERGY OF A DIATOMIC MOLECULE IN THE FIRST ORDER OF PERTURBATION THEORY TAKING INTO ACCOUNT THE PAULI PRINCIPLE AND PLASMA OSCILLATIONS OF ATOMIC ELECTRONS Koshcheev V.P., Shtanov Yu.N. 1 Moscow Aviation Institute (National Research University), Strela Branch, Moscow oblast, Zhukovskii, Russia. 2 Industrial University of Tyumen, Surgut Branch, Surgut, Russia. e-mail: koshcheev1@yandex.ru, **e-mail: yuran1987@mail.ru