Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling processing of real-time data streams on heterogeneous multi-GPU systems

Processing vast numbers of data streams is a common problem in modern computer systems and is known as the "online big data problem. " Adding hard real-time constraints to the processing makes the scheduling problem a very challenging task that this paper aims to address. In such an environment, each data stream is manipulated by a (different) application and each datum (data packet) needs to be processed within a known deadline from the time it was generated. This work assumes a central compute engine which consists of a set of CPUs and a set of GPUs. The system receives a configuration of multiple incoming streams and executes a scheduler on the CPU side. The scheduler decides where each data stream will be manipulated (on the CPUs or on one of the GPUs), and the order of execution, in a way that guarantees that no deadlines will be missed. Our scheduler finds such schedules even for workloads that require high utilization of the entire system (CPUs and GPUs). This paper focuses on an environment where all CPUs share a main memory, and are controlled by a single operating system (and a scheduler). The system uses a set of discrete graphic cards, each with its own private main memory. Different memory regions do not share information, and coherency is maintained by the use of explicit memory-copy operations. The paper presents a new algorithm for distributing data and scheduling applications that achieves high utilization of the entire system (CPUs and GPUs), while producing schedules that meet hard real-time constraints. We evaluate our new proposed algorithm by using the AES-CBC encryption kernel on thousands of streams with realistic distribution of rates and deadlines. The paper shows that on a system with a CPU and two GPU cards, our current framework allows up to 87% more data to be processed per time unit than a similar single-GPU system.
Decentralized Convergence to Nash Equilibria in Constrained Deterministic Mean Field Control

This paper considers decentralized control and optimization methodologies for large populations of systems, consisting of several agents with different individual behaviors, constraints and interests, and influenced by the aggregate behavior of the overall population. For such large-scale systems, the theory of aggregative and mean field games has been established and successfully applied in various scientific disciplines. While the existing literature addresses the case of unconstrained agents, we formulate deterministic mean field control problems in the presence of heterogeneous convex constraints for the individual agents, for instance arising from agents with linear dynamics subject to convex state and control constraints. We propose several model-free feedback iterations to compute in a decentralized fashion a mean field Nash equilibrium in the limit of infinite population size. We apply our methods to the constrained linear quadratic deterministic mean field control problem.
Color image super-resolution reconstruction based on IMED

In this paper, a novel color image super-resolution reconstruction algorithm based on IMage Euclidean Distance (IMED) and manifold learning is proposed. Firstly, the wavelet decomposition is used to extract features from training images. Then IMED is adopted for the computation of distances between pairs of small patches. Finally, the color high resolution image is reconstructed in YCbCr space in the framework of locally linear embedding. Experimental results demonstrate that the proposed method has better performance in comparison with the manifold learning method that is not combined with IMED.
Analysis of Different Positions of Fiber-Reinforced Composite Retainers versus Multistrand Wire Retainers Using the Finite Element Method

Background. The aim of this study was to evaluate root displacement of the lower incisors fixed with FRC in different positions versus FSW retainers using the finite element method. Materials and Methods. 3D finite element models were designed for a mandibular anterior segment: Model 1: flexible spiral wire bonded to the lingual teeth surfaces, Model 2: FRC bonded to the upper third of lingual teeth surfaces, and Model 3: FRC bonded to the middle third. FE analysis was performed for three models and then tooth displacements were evaluated. Results. In contrast to lateral incisors and canines, the FSW retainer caused the central teeth to move more than the teeth bonded with FRC in both loadings. Comparison between Models 2 and 3 (in vertical loading) showed that FRC retainers that bonded at the upper third of lingual teeth surfaces made central and canine teeth move less than FRC retainers bonded at the middle third; however, for lateral teeth it was the opposite. Conclusion. FRC retainers bonded at the upper third of lingual teeth surfaces make central and canine teeth move less than FRC retainers bonded at the middle third in vertical loading; however, for lateral teeth it was the opposite.
Learning for accurate classification of real-time traffic

Accurate network traffic classification is an important task. We intend to develop an intelligent classification system by learning the types of service inside a network flow using machine learning techniques. Previous work used Bayesian methods for traffic classification. In this paper we propose a further plan to identify a fine-grained traffic classification scheme through combining a series of techniques.
Effects of High-Frequency Cranial Electrostimulation on the Rest-Activity Rhythm and Salivary Cortisol in Alzheimer’s Disease

Objective: In a previous study, low-frequency (0.5 Hz) cranial electrostimulation (CES) neither improved the rest-activity rhythm nor reduced the level of salivary cortisol in patients with probable Alzheimer’s disease (AD). To investigate whether the frequency of CES was responsible for these negative findings, we set out to examine the effects of high-frequency CES on the rest-activity rhythm and salivary cortisol of patients with probable AD. We hypothesized that a decreased level of cortisol would parallel a positive effect of high-frequency CES on nocturnal restlessness in AD patients. Methods: Twenty AD patients were randomly assigned to an experimental group (n = 10) and a control group (n = 10). The experimental group was treated with high-frequency CES, the control group received sham stimulation, for 30 min a day, during 6 weeks. The rest-activity rhythm was assessed by actigraphy. Level of cortisol was measured by means of salivette tubes. Results: The rest-activity rhythm and the level of salivary cortisol did not react positively to high-frequency CES. In contrast, both groups showed an increase in the level of cortisol after the 6-week treatment period. Conclusions: High-frequency CES appeared to be ineffective in AD patients.
High resolution SAR imagery for surface deformation assessment

The geodetic information of the terrain can be measured using remote sensing techniques such as photogrammetry, airborne laser scanner (ALS) and interferometric synthetic aperture radar (InSAR). They are considered to be more cost-effective and complementary to conventional ground-based surveying methods. Differential InSAR (DInSAR) can be further used to monitor the dynamic change of the terrain by eliminating the topography from the interferometric phase. The aim of this study is to examine the importance of the resolution of the digital elevation models (DEMs) and the SAR images towards subsidence mapping. The multisensor data which have the various wavelengths, repeat-cycles and imaging resolutions are tested in the following sections. Two DEMs, including a SRTM 90m resolution DEM, and the SAR data acquired by ERS-1/2, JERS-1, Radarsat-1 and ENVISAT were tested and compared. The results indicated the SRTM works well to remove the topographic phase fringes for the moderate terrain. But some topographic residue phases would still occur at steep terrain or in the results of the pairs with larger perpendicular baseline. The results also showed that the coherence of the Radarsat-1 data at finer resolution is conserved better than the ERS-1/2 interferometric pairs. The scheduled SAR satellites in the coming years are also discussed in this paper.
An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation

The neural network joint model of translation or NNJM (Devlin et al., 2014) combines source and target context to produce a powerful translation feature. However, its softmax layer necessitates a sum over the entire output vocabulary, which results in very slow maximum likelihood (MLE) training. This has led some groups to train using Noise Contrastive Estimation (NCE), which side-steps this sum. We carry out the first direct comparison of MLE and NCE training objectives for the NNJM, showing that NCE is significantly outperformed by MLE on large-scale ArabicEnglish and Chinese-English translation tasks. We also show that this drop can be avoided by using a recently proposed translation noise distribution.
Lessons Learned from Building a Terabyte Digital Video Library

The Informedia Project at Carnegie Mellon University has created a terabyte digital video library in which automatically derived descriptors for the video are used for indexing, segmenting and accessing the library contents. Begun in 1994, the project presented numerous challenges for library creation and deployment, valuable information covered in this article. The authors, developers of the project at Carnegie Mellon University, addressed these challenges by: automatically extracting information from digitized video; creating interfaces that allowed users to search for and retrieve videos based on extracted information; and validating the system through user testbeds. Through speech, image, and natural language processing, the Informedia Project has demonstrated that previously inaccessible data can be derived automatically and used to describe and index video segments.
A BRAUER ALGEBRA THEORETIC PROOF OF LITTLEWOOD'S RESTRICTION RULES

Let U be a complex vector space endowed with an orthogonal or symplectic form, and let G be the subgroup of GL(U) of all the symmetries of this form (resp. O(U) or Sp(U)); if M is an irreducible GL(U){module, the Littlewood's restriction rules describes the G{module M GL(U) G. In this paper we give a new representation-theoretic proof of this formula: realizing M in a tensor power U f and using Schur's duality we reduce to the problem of describing the restriction to an irreducible S f {module of an irreducible module for the centralizer algebra of the action of G on U f ; the latter is a quotient of the Brauer algebra, and we know the kernel of the natural epimorphism, whence we deduce the Littlewood's restriction rule. "Non potrai dir che quest' e cosa dura: usando la dualitt a di Brauer dimostrazione dar, novella e pura" N. Barbecue, "Scholia"
For Some Mothers More than Others: How Children Matter for Labour Market Outcomes When Both Fertility and Female Employment are Low

We estimate the causal relationship between family size and labour market outcomes for families in low fertility and low female employment regime. Family size is instrumented using twinning and gender composition of the first two children. Among families with at least one child we identify the average causal effect of an additional child on mother’s employment to be -7.1 percentage points. However, we find no effect of additional children on female employment among families with two or more kids. Heterogeneity analysis suggests no causal effects of fertility on female employment among mothers with less than college education and older mothers (born before 1978). Furthermore, we find evidence for the interaction of family size with maternal education and age. An unintuitive feature of our finding is that we identify a positive bias of OLS estimates for highly educated mothers and for mothers born after 1977.
Missing nominal data imputation using association rule based on weighted voting method

With the rapid increase in the use of databases, missing data make up an important and unavoidable problem in data management and analysis. Because the mining of association rules can effectively establish the relationship among items in databases, therefore, discovered rules can be applied to predict the missing data. In this paper, we present a new method that uses association rules based on weighted voting to impute missing data. Three databases were used to demonstrate the performance of the proposed method. Experimental results prove that our method is feasible in some databases. Moreover, the proposed method was evaluated using five classification problems with two incomplete databases. Experimental results indicate that the accuracy of classification is increased when the proposed method is applied for missing attribute values imputation.
Query Performance Prediction Using Reference Lists

The task of query performance prediction is to estimate the effectiveness of search performed in response to a query when no relevance judgments are available. We present a novel probabilistic analysis of the performance prediction task. The analysis gives rise to a general prediction framework that uses pseudo-effective or ineffective document lists that are retrieved in response to the query. These lists serve as reference to the result list at hand, the effectiveness of which we want to predict. We show that many previously proposed prediction methods can be explained using our framework. More generally, we shed new light on existing prediction methods and establish formal common grounds to seemingly different prediction approaches. In addition, we formally demonstrate the connection between prediction using reference lists and fusion of retrieved lists, and provide empirical support to this connection. Through an extensive empirical exploration, we study various factors that affect the quality of prediction using reference lists.
Particle Swarm Optimization-Based Source Seeking

The task of locating a source based on the measurements of the signal emitted/emanating from it is called the source-seeking problem. In the past few years, there has been a lot of interest in deploying autonomous platforms for source-seeking. Some of the challenging issues with implementing autonomous source-seeking are the lack of a priori knowledge about the distribution of the emitted signal and presence of noise in both the environment and on-board sensor measurements. This paper proposes a planner for a swarm of robots engaged in seeking an electromagnetic source. The navigation strategy for the planner is based on Particle Swarm Optimization (PSO) which is a population-based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this paper. Our results are also validated on an experimental testbed. Note to Practitioners-This paper is inspired by the source seeking problem in which the signal emitted from the source is assumed to be very noisy, and the spatial distribution is assumed to be non-smooth. We focus our work specifically on electromagnetic sources. However, the strategies proposed in this paper are also applicable to other kinds of sources, for example, nuclear, radiological, chemical or biological. We develop a planner for a swarm of mobile agents that try to locate an unknown electromagnetic source. The mobile agents know their own positions and can measure the signal strength at their current location. They can share information among themselves, and plan for the next step. We propose a complete solution to ensure the effectiveness of PSO in complex environments where collisions may occur. We incorporate static and dynamic obstacle avoidance strategies in PSO to make it fully applicable to real-world scenario. We validate the proposed technique on an experimental testbed. As a part of our future work, we will extend the technique to locate multiple sources of different kinds.
Analysis of an Internal Combustion Engine Using Porous Foams for Thermal Energy Recovery

Homogeneous and complete combustion in internal combustion engines is advantageous. The use of a porous foam in the exhaust gas in an engine cylinder for heat recovery is examined here with the aim of reducing engine emissions. The internal combustion engine with a porous core regenerator is modeled using SOPHT software, which solved the differential equations for the thermal circuit in the engine. The engine thermal efficiency is observed to increase from 43% to 53% when the porous core regenerator is applied. Further, raising the compression ratio causes the peak pressure and thermal efficiency to increase, e.g., increasing the compression ratio from 13 to 15 causes the thermal efficiency and output work to increase from 53% to 55% and from 4.86 to 4.93 kJ, respectively. The regenerator can also be used as a catalytic converter for fine particles and some other emissions. The regenerator oxidizes unburned hydrocarbons. Meanwhile, heat recovered from the exhaust gases can reduce fuel consumption, further reducing pollutant emissions from the internal combustion engine.
CloneDoc: exploiting the cloud to leverage secure group collaboration mechanisms for smartphones

The limited battery of smartphones makes it hard for users to fully exploit their devices. Recent research considers offloading mobile computation to the cloud to improve efficiency and to extend battery life. We design Clone2Clone (C2C), a distributed p2p platform for software cloud clones of smartphones. Upon C2C we implement CloneDoc, a secure real-time collaboration system for smartphone users. We measure the performance of CloneDoc on a testbed of six Android smartphones and clones hosted on the Amazon cloud service and show that C2C based protocols can significantly improve battery-life compared to protocols that do not make use of C2C.
Using Accessor Variety Features of Source Graphemes in Machine Transliteration of English to Chinese

This work describes a grapheme-based approach of English-to-Chinese (E2C) transliteration, which includes many-to-many alignment models and conditional random fields using accessor variety (AV) as an additional feature based on source graphemes. Experimental results indicate that the AV of a given English segment can generally improve effectiveness of E2C transliteration.
Infinite time horizon maximum causal entropy inverse reinforcement learning

We extend the maximum causal entropy framework for inverse reinforcement learning to the infinite time horizon discounted reward setting. To do so, we maximize discounted future contributions to causal entropy subject to a discounted feature expectation matching constraint. A parameterized class of stochastic policies that solve this problem are referred to as soft Bellman policies because they can be specified in terms of values that satisfy an equation identical to the Bellman equation but with a softmax (the log of a sum of exponentials) instead of a max. Under some assumptions, algorithms that repeatedly solve for a soft Bellman policy, evaluate the policy, and then perform a gradient update on the parameters will find the optimal soft Bellman policy. For the first step, we extend techniques from dynamic programming and reinforcement learning so that they derive soft Bellman policies. For the second step, we can use policy evaluation techniques from dynamic programming or perform Monte Carlo simulations. We compare three algorithms of this type by applying them to a problem instance involving demonstration data from a simple controlled queuing network model inspired by problems in air traffic management.
Fast Algorithms and Performance Bounds for Sum Rate Maximization in Wireless Networks

Sum rate maximization by power control is an important, challenging, and extensively studied problem in wireless networks. It is a nonconvex optimization problem and achieves a rate region that is in general nonconvex. We derive approximation ratios to the sum rate objective by studying the solutions to two related problems, sum rate maximization using an SIR approximation and max-min weighted SIR optimization. We also show that these two problems can be solved very efficiently, using much faster algorithms than the existing ones in the literature. Furthermore, using a new parameterization of the sum rate maximization problem, we obtain a characterization of the power controlled rate region and its convexity property in various asymptotic regimes. Engineering implications are discussed for IEEE 802.11 networks.
Maternal Hypercholesterolemia and Treatment During Pregnancy Influence the Long-Term Progression of Atherosclerosis in Offspring of Rabbits

Maternal hypercholesterolemia during pregnancy is associated with enhanced fatty streak formation in human fetuses and faster progression of atherosclerosis during childhood even under normocholesterolemic conditions. A causal role of maternal hypercholesterolemia in lesion formation during fetal development has previously been established in rabbits. The same experimental model is now used to establish that maternal hypercholesterolemia or ensuing pathogenic events in fetal arteries enhance atherogenesis later in life. Five groups of rabbit mothers were fed chow, cholesterol-enriched chow, or cholesterol-enriched chow plus 1000 IU vitamin E, 3% cholestyramine, or both during pregnancy. Offspring of all groups (n=136) were fed a mildly hypercholesterolemic diet for up to a year and had similar cholesterol levels. Aortic lesion sizes and lipid peroxidation products in plasma and lesions in offspring were determined at birth, 6 months, or 12 months. Lesion progression in offspring of hypercholesterolemic mothers was greater than in all other groups. At each time point, offspring of hypercholesterolemic mothers had 1.5- to 3-fold larger lesions than offspring of normocholesterolemic mothers (P <0.01), with the greatest absolute differences at 12 months. Maternal treatment reduced lesions by 19% to 53%, compared with offspring of untreated hypercholesterolemic mothers (P <0.01), with the greatest effect in the vitamin E groups. At 12 months, lesions in offspring of all vitamin E and cholestyramine-treated mothers were similar to those of normocholesterolemic mothers. Lipid peroxidation end-products in lesions and plasma showed analogous differences between groups as lesions (P <0.01). Thus, pathogenic programming in utero increases the susceptibility to atherogenic risk factors later in life and maternal intervention with cholesterol-lowering drugs or antioxidants reduce postnatal lipid peroxidation and atherosclerosis in their offspring.