Miracle-dz/UniFAR-FTUs huggingface dataset:
Local linear discriminant analysis with composite kernel for face recognition

This paper presents a method for nonlinear discriminant analysis utilizing a composite kernel which is derived from a combination of local linear models with interpolation. The underlying idea is to decompose a complex nonlinear problem into a set of simpler local linear problems. Combining with the theory of nonlinear classification based on kernels, the local linear models with interpolation can be formulated as a composite kernel based discriminant analysis form. In face recognition, linear discriminant analysis (LDA) has been widely adopted owing to its efficiency, but it fails to solve nonlinear problems. Conventional kernel based approaches such as generalized discriminant analysis (GDA) has been successfully applied to extend LDA to nonlinear pattern recognition tasks. However, selecting an appropriate kernel function is usually difficult. Utilizing an implicit kernel mapping may face potential over-training problems for some complex and noised tasks. Our proposed method gives an alternative solution for nonlinear discriminant analysis while the conventional linear and nonlinear approaches are difficult to achieve a satisfactory results. Experiments on both synthetic data and face data set show the effectiveness of the proposed methods.
High-Efficiency Differential-Chaos-Shift-Keying Scheme for Chaos-Based Noncoherent Communication

In this brief, a new noncoherent chaos-based communication scheme, named high-efficiency differential chaos shift keying (DCSK) (HE-DCSK), is proposed. By recycling each reference sample in DCSK, 2 bits of data can be carried in one data-modulated sample sequence, which offers our scheme double bandwidth efficiency and nonrepeated transmitted signal less prone to interception in comparison to DCSK. The bit-error performance of the proposed scheme is studied analytically based on Gaussian approximation for discrete-time implementations. Simulations in additive-white-Gaussian-noise channel are performed and compared with DCSK and correlation delay shift keying (CDSK) schemes. Results show that the bit-error rate performance of HE-DCSK can always outperform CDSK and be even better than DCSK with typical spreading factors and at reasonable Eb/No levels.
Restricted coverage in wireless networks

For wireless networks, coverage with different restrictions that can capture the practical requirements have received great research interests. We will study several restricted coverage problems. The first problem is about K-coverage, i.e., how to deploy wireless nodes such that each target is covered by at least K wireless nodes. We study the problem restricted to linear-K-coverage where there is a line, all targets lie in one side of this line and all wireless nodes lie in the other side. Assume each wireless node is associated with a weight, the objective is to select a minimum weighted subset of nodes such that each target is K-covered. We propose a 3-approximation for this problem by exploring geometric properties. The second problem is called K-road-coverage. Given a road map in a two-dimensional area which contains a set of paths and a set of wireless nodes, the locations of nodes can either be arbitrary or fixed, the objective is to select a minimum number of wireless nodes such that each path can be K-covered. We will reduce the problem to K-coverage and apply the algorithmic results for K-coverage to solve it. Another line of this work is to investigate a well-motivated problem called strongly dominating set, which is intrinsically related to coverage. Given a wireless networking system represented by a digraph G = (V, E⃗). Each wireless node u has a covering disk centering at u with its radius equal to the transmission range of u. We then draw a directed edge uv⃗ in G if u's corresponding covering disk contains v. A subset U ⊆ V of wireless nodes is a strongly dominating set if every wireless node in V \ U has both an in-neighbor in U and an out-neighbor in U. The objective is to find a minimum size strongly dominating set. Our method can achieve an approximation factor of (2 + ε).
Clustering via nonparametric density estimation: an application to microarray data

Cluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statisti- cal problems due to dimensionality issues, being the number of variables much higher than the number of observations. Here, we present a novel approach to clustering of microarray data via nonparametric density estimation, based on the following steps: (i) selection of relevant variables; (ii) dimensionality reduction; (iii) clustering of observations in the reduced space. Applications on simulated and real data show promising results in comparison with those produced by two standard approaches, k-means and Mclust. In the simulation studies, our nonparametric approach shows performances comparable to those of models based on normality assumption, even in Gaussian settings. On the other hand, in two benchmarking real datasets, it outperforms the existing parametric approaches.
Quadratic Optimization to Identify Highly Heritable Quantitative Traits from Complex Disease Features

Identifying genetic variation underlying a complex disease is important. Many complex diseases have heterogeneous phenotypes and are products of a variety of genetic and environmental factors acting in concert. Deriving highly heritable quantitative traits of a complex disease can improve the identification of genetic risk of the disease. The most sophisticated methods so far perform unsupervised cluster analysis on phenotypic features; and then a quantitative trait is derived based on each resultant cluster. Heritability is estimated to assess the validity of the derived quantitative traits. However, none of these methods explicitly maximize the heritability of the derived traits. We propose a quadratic optimization approach that directly utilizes heritability as an objective during the derivation of quantitative traits of a disease. This method maximizes an objective function that is formulated by decomposing the traditional maximum likelihood method for estimating heritability of a quantitative trait. We demonstrate the effectiveness of the proposed method in finding a trait with high heritability on synthetic data. We also apply our algorithm to identify highly heritable traits of complex human behavior disorders, such as opioid use disorder and cocaine use disorders. Our approach outperforms standard cluster analysis methods to identify highly heritable traits of opioid use and cocaine use.
Clinical signs, MRI features, and outcomes of two cats with thiamine deficiency secondary to diet change

Two cats were presented with vestibular signs and seizures. Both cats were diagnosed with thiamine deficiency. The transverse and dorsal T2-weighted magnetic resonance (MR) images revealed the presence of bilateral hyperintense lesions at specific nuclei of the midbrain, cerebellum, and brainstem. After thiamine supplementation, the clinical signs gradually improved. Repeated MR images taken 3 weeks after thiamine supplementation had started showed that the lesions were nearly resolved. This case report describes the clinical and MR findings associated with thiamine deficiency in two cats.
The Dynamics of Multi-Agent Reinforcement Learning

Infinite-horizon multi-agent control processes with non-determinism and partial state knowledge have particularly interesting properties with respect to adaptive control, such as the non-existence of Nash Equilibria (NE) or non-strict NE which are nonetheless points of convergence. The identification of reinforcement learning (RL) algorithms that are robust, accurate and efficient when applied to these general multi-agent domains is an open, challenging problem. This paper uses learning pressure fields as a means for evaluating RL algorithms in the context of multi-agent processes. Specifically, we show how to model partially observable infinite-horizon stochastic processes (single-agent) and games (multi-agent) within the Finite Analytic Stochastic Process framework. Taking long term average expected returns as utility measures, we show the existence of learning pressure fields: vector fields --similar to the dynamics of evolutionary game theory, which indicate medium and long term learning behaviours of agents independently seeking to maximise this utility. We show empirically that these learning pressure fields are followed closely by policy-gradient RL algorithms.
Effects of conjugated linoleic acid on the performance of laying hens, lipid composition of egg yolk, egg flavor, and serum components

Objective This experiment investigated the effects of dietary supplementation with conjugated linoleic acid (CLA) on the serum components, laying hen productivity, lipid composition of egg yolk, egg flavor and egg quality. Methods Healthy 28-week-old Hy-Line white laying hens (n = 480) were divided randomly into 4 groups, 6 replicates/group, 20 birds/replicate. The 30-day experimental diets included 0% (control), 0.4%, 0.8%, and 1.6% CLA. Some serum indices of the birds, and egg production, quality, fatty acid composition, egg quality were measured. Results The dietary supplementation with 0.4%, 0.8%, and 1.6% CLA did not significantly affect the laying rate and feed intake, as well as calcium ion and phosphorus ion concentration in serum (p>0.05). However, the CLA had significantly increased the strength of eggshell, decreased the odor, flavor, and taste of egg yolk, deepened the color of egg yolk, increased saturated fatty acids and polyunsaturated fatty acids, and reduced the monounsaturated fatty acids (p<0.05). On the other hand, the dietary supplementation with 1.6% CLA had significant effects on feed/gain, and improved serum hormones. Dietary supplementation with 0.4% and 0.8% CLA can significantly enhance the activity of alkaline phosphates. Conclusion CLA has no effect on production performance, but does enhance the lipid content of the egg yolk and the strength of the eggshell.
Power Reduction in VLIW Processor with Compiler Driven Bypass Network

With the increase in issue width, bypass control of a processor become more complex. Also, in a processor, operands are read both from register file as well as from bypass. For a multi-port register file, read/write energy is much more than that of single port register file. Both redundant register read/write and bypass control area can be reduced with compiler hints for register bypass. In this work we suggest an innovative way to represent compiler bypass hints that serve both these motivations. Further, bypass hints are used in effective design of multi-stage bypass network. Experiments on media-bench benchmarks show that by using our approach: (i) register file energy savings can be as much as 60%; (ii) and synthesis of VLIW core saves 2-4% of the core area
Architecture Exploration of Real-Time Systems Based on Multi-objective Optimization

This article deals with real-time embedded system design and verification. Real-time embedded systems are frequently designed according to multi-tasking architectures that have timing constraints to meet. The design of real-time embedded systems expressed as a set of tasks raises a major challenge since designers have to decide how functions of the system must be assigned to tasks. Assigning each function to a different task will result in a high number of tasks, and then in higher preemption overhead. In contrast, mapping many functions on a limited number of tasks leads to a less flexible design which is more expensive to change when the functions of the system evolve. This article presents a method based on an optimization technique to investigate the assignment of functions to tasks. We propose a multi-objective evolution strategy formulation which both minimizes the number of preemptions and maximizes task laxities. Our method allows designers to explore the search space of all possible function to task assignments and to find good tradeoffs between the two optimization objectives among schedulable solutions. After explaining our mapping approach, we present a set of experiments which demonstrates its effectiveness for different system sizes.
Learning to segment videos in HEVC compressed domain

High efficiency video coding (HEVC), as an up-to-date video coding standard, is becoming widely used, due to its preeminent performance. We argue in this paper that the new HEVC encoder can be utilized to provide efficient features for video segmentation. Thus, we propose a novel method, which learns to segment videos in HEVC compressed domain. Specifically, three features are extracted from HEVC bitstreams. Then, these features are integrated for video segmentation in compressed domain, through the logistic regression which is learnt from training data. This way, the computational complexity on video decoding can be saved. Experimental results show that our method is significantly superior to other state-of-the-art methods in terms of F-measure.
Guardian of the HAN: Thwarting Mobile Attacks on Smart-Home Devices Using OS-level Situation Awareness

A new development of smart-home systems is to use mobile apps to control IoT devices across a Home Area Network (HAN). Those systems tend to rely on the Wi-Fi router to authenticate other devices; as verified in our study, IoT vendors tend to trust all devices connected to the HAN. This treatment exposes them to the attack from malicious apps, particularly those running on authorized phones, which the router does not have information to control, as confirmed in our measurement study. Mitigating this threat cannot solely rely on IoT manufacturers, which may need to change the hardware on the devices to support encryption, increasing the cost of the device, or software developers who we need to trust to implement security correctly. In this work, we present a new technique to control the communication between the IoT devices and their apps in a unified, backward-compatible way. Our approach, called Hanguard, does not require any changes to the IoT devices themselves, the IoT apps or the OS of the participating phones. Hanguard achieves a fine-grained, per-app protection through bridging the OS-level situation awareness and the router-level per-flow control: each phone runs a non-system userspace Monitor app to identify the party that attempts to access the protected IoT device and inform the router through a control plane of its access decision; the router enforces the decision on the data plane after verifying whether the phone should be allowed to talk to the device. Hanguard uses a role-based access control (RBAC) schema which leverages type enforcement (TE) and multi-category security (MCS) primitives to define highly flexible access control rules. We implemented our design over both Android and iOS (>95% of mobile OS market share) and a popular router. Our study shows that Hanguard is both efficient and effective in practice.
National environmental performance: an empirical analysis of policy results and determinants

Efforts to identify the determinants of environmental policy success at the national level have largely been anecdotal and case study based. This article seeks to identify empirically the factors that drive environmental performance as measured by levels of urban particulates and sulfur dioxide and energy use per unit of GDP. Although the data are imperfect and causal linkages cannot be definitively established, the statistical analysis presented suggests that environmental results vary not only with income levels as suggested by the environmental Kuznets Curve literature but also with both the sophistication of a nation's regulatory regime and, perhaps more notably, its broader economic and social context. Thus, at every level of development, countries face policy choices that determine environmental quality in important ways. Strong environmental performance appears to be positively correlated with competitiveness, putting into question the presumed trade-off between economic progress and environmental gains. Although preliminary, these results provide evidence that environmental decision making can be made more data driven and analytically rigorous.
Evolution of the a-Crystallin / Small Heat-Shock Protein Family 1

The common characteristic of the a-crystallin/small heat-shock protein family is the presence of a conserved homologous sequence of 90- 100 residues. Apart from the vertebrate lens proteins-aA- and aB-crystallin-and the ubiquitous group of 15-30-kDa heat-shock proteins, this family also includes two mycobacterial surface antigens and a major egg antigen of Schistosorna mansoni. Multiple small heatshock proteins are especially present in higher plants, where they can be distinguished in at least two classes of cytoplasmic proteins and a chloroplast-located class. The a-crystallins have recently been found in many tissues outside the lens, and uBcrystallin, in particular, behaves in many respects like a small heat-shock protein. The homologous sequences constitute the C-terminal halves of the proteins and probably represent a structural domain with a more variable C-terminal extension. These domains must be responsible for the common structural and functional properties of this protein family. Analysis of the phylogenetic tree and comparison of the biological properties of the various proteins in this family suggest the following scenario for its evolution: The primordial role of the small heat-shock protein family must have been to cope with the destabilizing effects of stressful conditions on cellular integrity. The u-crystallin-like domain appears to be very stable, which makes it suitable both as a surface antigen in parasitic organisms and as a longliving lens protein in vertebrates. It has recently been demonstrated that, like the other heat-shock proteins, the u-crystallins and small heat-shock proteins function as molecular chaperones, preventing undesired protein-protein interactions and assisting in refolding of denatured proteins. Many of the small heat-shock proteins are differentially expressed during normal development, and there is good evidence that they are involved in cytomorphological reorganizations and in degenerative diseases. In conjunction with the stabilizing, thermoprotective role of a-crystallins and small heat-shock proteins, they may also be involved in signal transduction. The reversible phosphorylation of these proteins appears to be important in this respect.
Integrating Importance, Non-Redundancy and Coherence in Graph-Based Extractive Summarization

We propose a graph-based method for extractive single-document summarization which considers importance, non-redundancy and local coherence simultaneously. We represent input documents by means of a bipartite graph consisting of sentence and entity nodes. We rank sentences on the basis of importance by applying a graph-based ranking algorithm to this graph and ensure nonredundancy and local coherence of the summary by means of an optimization step. Our graph based method is applied to scientific articles from the journal PLOS Medicine. We use human judgements to evaluate the coherence of our summaries. We compare ROUGE scores and human judgements for coherence of different systems on scientific articles. Our method performs considerably better than other systems on this data. Also, our graph-based summarization technique achieves state-of-the-art results on DUC 2002 data. Incorporating our local coherence measure always achieves the best results.
Measurementof Serum Lithiumby Atomic

Lithium concentrations in the serum of patients undergoing therapy with lithium salts were measured by atomic absorption spectroscopy. Serum was diluted 10-fold with water. Physiological amounts of sodium and potassium were included in the blank and standard solutions because these cations enhance the lithium signal by 2%. Calcium, magnesium, bicarbonate, sulfate, and phosphate at the concentrations found in serum were without effect. Protein clogging of the triple-slot Boling burner was not observed. The relative standard deviation of within-run variability was 0.6% (n = 20 and = 0.88 mEq/liter), and of daily precision, tested over a month, it was 2.5% (n = 30, = 0.91 mEq/Iiter). Recovery of 1.00 mEq of lithium added per liter of serum ranged from 97 to 103%, averaging 99.8%.
World-Size Global Markets Lead to Economic Instability

Economic and cultural globalization is one of the most important processes humankind has been undergoing lately. This process is assumed to be leading the world into a wealthy society with a better life. However, the current trend of globalization is not unprecedented in human history, and has had some severe consequences in the past. By applying a quantitative analysis through a microscopic representation we show that globalization, besides being unfair (with respect to wealth distribution), is also unstable and potentially dangerous as one event may lead to a collapse of the system. It is proposed that the optimal solution in controlling the unwanted aspects and enhancing the advantageous ones lies in limiting competition to large subregions, rather than making it worldwide.
Spectral Analysis and Reconstruction for Periodic Nonuniformly Sampled Signals in Fractional Fourier Domain

Nonuniform sampling occurs in many applications due to imperfect timebase or random events. Periodic nonuniform sampling is a special type of nonuniform sampling. The properties and applications of periodic nonuniform sampling signals in traditional Fourier domain have been extensively studied, but so far none of the research papers covering the spectral analysis and reconstruction of periodic nonuniformly sampled signals in fractional Fourier domain have been published. This correspondence is to explore the spectral properties of periodic nonuniformly sampled signals associated with the fractional Fourier transform. First, based on the uniform fractional Fourier transform kernel, the general spectral representation of periodic nonuniformly sampled signals has been derived. Second, detailed analysis of periodic nonuniformly sampled chirp signals in the fractional Fourier domain have been performed. The results can be used to estimate the chirp rate and the sampling offsets. Finally, a more simple relationship between the discrete fractional spectrum of periodic nonuniformly sampled signals and the continuous spectrum has been obtained. Based on this relationship, the original continuous spectrum can be reconstructed from periodic nonuniformly sampled signals in fractional Fourier domain. In addition, the simulations are carried out to verify the correctness of the results.
How to upgrade wireless networks: Small cells or massive MIMO?

Radio network deployment and coverage optimization are critical to next-generation wireless networks. In this paper, the problem of optimally deciding on whether to install additional small cells or to upgrade current macrocell base stations (BSs) with massive antenna arrays is studied. This integrated deployment problem is cast as a general integer optimization model by using the facility location framework. The capacity limits of both the radio access link and the backhaul link are considered. The problem is shown to be an extension of the modular capacitated location problem (MCLP) which is known to be NP-hard. To solve the problem, a novel deployment algorithm that uses Lagrangian relaxation and tabu local search is proposed. The developed tabu search is shown to have a two-level structure and to be able to search the solution space thoroughly. Simulation results show how the proposed, optimal approach to upgrading an existing wireless network infrastructure can make use of a combination of both small cells and BSs with massive antennas. The results also show that the proposed algorithm can find the optimal solution effectively while having a computational time that is up to 30% lower than that of conventional algorithms.