Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitigating the Curse of Dimensionality for Exact kNN Retrieval

Efficient data indexing and exact k-nearest-neighbor (kNN) retrieval are still challenging tasks in high-dimensional spaces. This work highlights the difficulties of indexing in high-dimensional and tightly-clustered dataspaces by exploring several important tunable parameters for optimizing kNN query performance using the iDistance and iDStar algorithms. We experiment on real and synthetic datasets of varying size, cluster density, and dimensionality, and compare performance primarily through filter-and-refine efficiency and execution time. Results show great variability over parameter values and provide new insights and justifications in support of prior best-use practices. Local segmentation with iDStar consistently outperforms iDistance in any clustered space below 256 dimensions, setting a new benchmark for efficient and exact kNN retrieval in high-dimensional spaces. We propose several directions of future work to further increase performance in high-dimensional real-world settings.
Evaporation and growth of crystals: Propagation of step-density compression waves at vicinal surfaces

We studied the step dynamics during crystal sublimation and growth in the limit of fast surface diffusion and slow kinetics of atom attachment-detachment at the steps. For this limit we formulate a model free of the quasi-static approximation in the calculation of the adatom concentration on the terraces at the crystal surface. Such a model provides a relatively simple way to study the linear stability of a step train in a presence of step-step repulsion and an absence of destabilizing factors (as Schwoebel effect, surface electromigration etc.). The central result is that a critical velocity of the steps in the train exists which separates the stability and instability regimes. When the step velocity exceeds its critical value the plot of these trajectories manifests clear space and time periodicity (step density compression waves propagate on the vicinal surface). This ordered motion of the steps is preceded by a relatively short transition period of disordered step dynamics.
Tracking smartphones using low-power sensor nodes

Location information about mobile devices can reveal movement patterns of people wearing them. We present a cross-technology approach to track Wi-Fi enabled smartphones using a ZigBee sensor network. Implementing such a system on resource constrained low-power sensor nodes requires means to efficiently process signal strength measurements. We propose a new method to recognize Wi-Fi probe requests on devices that are unable to decode Wi-Fi packets. Initial experiments on a testbed show that it is possible to localize unmodified phones within a building using signal strength measurements.
Portable high performance programming via architecture-cognizant divide-and-conquer algorithms

Portability and high performance are two goals that in themselves have been hard to achieve, but the combination has been especially elusive. For programs to achieve high performance on a given computer they must make effective use of its instruction set architecture, and take advantage of the memory hierarchy capacity, associativity, bandwidth, and latency. The result is that each computer must execute a different instruction sequence in order to get the best performance. Without hand-tuned codes this level of performance is difficult to obtain since modern compilers don't have the ability to generate code that can adequately exploit these aspects of the architecture. This thesis presents a new framework, architecture-cognizant divide-and-conquer, which bridges the gap between portability and high performance. The conflict between performance and portability is handled by a mechanism that selects from a set of semantically equivalent variants to create a variant policy. The single metric we use to guide this selection is execution time. We show that the entire space of the variant policies need not be exhaustively searched to find the optimal variant policy, and we give a set of heuristics that greatly reduce the search space, often from exponential to polynomial. Along with this we implemented a prototype system, Napoleon, which runs experiments on the target architecture using the heuristics we've developed. The effectiveness of architecture-cognizance is demonstrated through the implementation on various computers of four algorithms representative of scientific kernels: Matrix Multiply, Mergesort, Point Jacobi, and Fast Fourier Transform.
Expression of the Kv3.1 Potassium Channel in the Avian Auditory Brainstem

The Shaw-like potassium channel Kv3.1, a delayed rectifier with a high threshold of activation, is expressed in the time coding nuclei of the bird auditory brainstem. In both barn owls and chickens, Kv3.1 mRNA was expressed in the cochlear nucleus magnocellularis (NM) and the nucleus laminaris (NL). Western blot analysis showed that an antibody raised against the synthetic peptide sequence of rat Kv3.1 (rKv3.1) specifically recognized the same 92 kDa protein bands in both rat and chicken synaptosomal preparations. Immunohistochemical analyses using this anti-rKv3.1 antibody revealed a prominent gradient in Kv3.1 immunoreactivity along the tonotopic axis of the barn owl NM and NL and a less prominent gradient in the chicken. The precise localization of the Kv3.1 immunoproduct was resolved by electron microscopy. In both the owl and the chicken, Kv3.1 was targeted postsynaptically in NM and NL. The major difference in localization of Kv3.1 protein between the two birds was the expression of Kv3.1 in the NM axons and terminals in the region of the barn owl NL. This location of Kv3.1 channels supports its postulated function in reducing the width of action potentials as they invade the presynaptic terminal. The presynaptic localization may be a specialization for enabling neurons in owl NM to transmit high-frequency temporal information with little jitter.
A multi-channel transmission scheme in cognitive radio networks

Low utilization efficiency of spectrum, which is caused by fixed spectrum allocation scheme, is the bottleneck of wireless communication systems. Cognitive Radio (CR) is proposed to utilize the idle spectrum in an intelligent way while not interfering with primary users. For application of CR, the objective is the optimization of spectrum utilization. In order to achieve this, we propose a multi-channel transmission scheme in CR Networks which is based on Layered Coding scheme. For ensuring QoS of secondary users, we assign the data with different priority to spectrum with different stability. The expectation-utility theory is used to evaluate the performance of the proposed scheme according to supposed spectrum occupation states. Compared with existing data transmission schemes, the proposed scheme not only brings better satisfaction of users but also improves the spectrum utilization.
Conventional Space-Vector Modulation Techniques Versus the Single-Phase Modulator for Multilevel Converters

Space-vector modulation (SVM) is a well-suited technique to be applied to multilevel converters and is an important research focus in the last 25 years. Recently, a single-phase multilevel modulator has been introduced, showing its conceptual simplicity and its very low computational cost. In this paper, some of the most conventional multilevel SVM techniques have been chosen to compare their results with those obtained with single-phase multilevel modulators. The obtained results demonstrate that the single-phase multilevel modulators applied to each phase are equivalent with the chosen well-known multilevel SVM techniques. In this way, single-phase multilevel modulators can be applied to a converter with any number of levels and phases, avoiding the use of conceptually and mathematically complex SVM strategies. Analytical calculations and experimental results are shown, validating the proposed concepts.
Computerized Adaptive Student Help: Experiments in Low-Cost Artificially Intelligent Automation of Student-Tutor Interaction

: - The lessons attained from research in artificially intelligent learning systems demonstrate the complexity in their attempts to learn either broad categories of knowledge or explicit tasks and skills that require extensive use of resources. This paper describes an investigation into some energy effective and low-cost approaches to developing lightweight artificially intelligent learning systems that are specific to a knowledge domain, and yet, are easily portable and expandable to incorporate larger domains of knowledge. A prototype system was built to test the feasibility of such an approach. The resulting observations provide evidence that it is indeed feasible to create a low-cost system that is highly energy effective for both end-users and trainers with the use of relatively simple processing algorithms. Furthermore, the investigation has yielded several promising opportunities for improvements to the current prototype system.
Malware Detection Method by Catching Their Random Behavior in Multiple Executions

Modern malware often changes their runtime behaviors in each execution to tolerate against malware analyses and detections. For example, when a malware copies itself on a file system, it can randomly determine its file name for avoiding the detections. Another example is that when a malware tries to connect its command and control server, it randomly chooses a domain name from a hard-coded domain name list to avoid being blocked by a static blacklist of malicious domain names. We assume that such random behaviors are unnecessary for benign software. Therefore the behaviors can be clues to distinguish malware from benign software. In this paper, we propose a novel malware detection method based on investigating the behavioral difference in multiple executions of suspicious software. Our proposed method conducts dynamic analysis on an executable file multiple times in the same sandbox environment so as to obtain plural lists of API call sequence, and then compares the lists to find the difference between the multiple executions. In the experiments with 5,697 malware samples and 819 benign software samples, the proposed method could detect about 67% malware samples and the false positive rate is about 1%. Moreover, the proposed method could detect 117 malware samples out of 273 malware samples which could not be detected by the antivirus software. Therefore we confirmed the possibility the proposed method may be able to improve the accuracy of malware detection utilizing in combination with other existing methods.
RcaDF : Towards a Relational Content Analysis Standard

Increasing numbers of high quality tools and data sets are becoming available for the study of media content, providing an opportunity for more rapid progress in Communication Science if these tools and data sets could be effectively combined and shared. In order to do so, it would be beneficial to have a shared Data format that is flexible enough to allow for changing research methods, expressive enough to deal with sophisticated content, yet has a clear semantic interpretation within and outside of the discipline. This paper proposes RcaDF, an expressive and flexible standard for Relational Content Analysis (Rca) based on the Resource Description Framework (RDF). Translations of existing data in NET, DyNETML, WorldView, and TABARI formats will be given, showing how data from these sources can be effectively combined, visualized, and analyzed. This standard will help researchers to share and combine data within and between institutions, to develop more general analysis and extraction tools, and to increase the usage of existing external repositories and tools.
A Study of Sort Algorithms for Multiprocessor Database Machines

This paper presents and analyzes algorithms for parallel execution of sort operations in a general multiprocessor architecture. We consider both internal and external sorting algorithms. For the latter, we study the performance of sorting algorithms that are derived from sorting algorithms that only do comparison and exchange by replacing each comparison-exchange with a B-way merge. In particular, we propose a new algorithm called the modified block bitonic sort. We then present the results of analyzing the performance of these different parallel external sorting algorithms. We show that the modified block bitonic sort is the fastest of the algorithms over a wide range of values of interest to us.
NLRP3 Inflammasome Is a Target for Development of Broad-Spectrum Anti-Infective Drugs

ABSTRACT We describe the molecular mode of action and pharmacodynamics of a new molecular entity (NME) that induces the NLRP3 inflammasome-mediated innate immune response. This innate response reduces the pathogen load in an experimentally induced methicillin-resistant Staphylococcos aureus infection, enhances survival in an experimentally induced Gram-negative bacteremia, and overrides the escape mechanism of an obligate intracellular pathogen, viz. Chlamydia pneumoniae. Furthermore, the NME is more effective than standard-of-care antibiotic therapy in a clinically established multifactorial bacterial infection. Analysis of transcriptional regulation of inflammasome signaling genes and innate/adaptive immune genes revealed consistent and significant host changes responsible for the improved outcomes in these infections. These studies pave the way for the development of first-in-class drugs that enhance inflammasome-mediated pathogen clearance and identify the NLRP3 inflammasome as a drug target to address the global problem of emerging new infectious diseases and the reemergence of old diseases in an antibiotic-resistant form.
Viral Respiratory Tract Infections in Adult Patients Attending Outpatient and Emergency Departments, Taiwan, 2012–2013

AbstractViral etiologies of respiratory tract infections (RTIs) have been less studied in adult than in pediatric populations. Furthermore, the ability of PCR/electrospray ionization mass spectrometry (PCR/ESI-MS) to detect enteroviruses and rhinoviruses in respiratory samples has not been well evaluated. We sought to use PCR/ESI-MS to comprehensively investigate the viral epidemiology of adult RTIs, including testing for rhinoviruses and enteroviruses.Nasopharyngeal or throat swabs from 267 adults with acute RTIs (212 upper RTIs and 55 lower RTIs) who visited a local clinic or the outpatient or emergency departments of a medical center in Taiwan between October 2012 and June 2013 were tested for respiratory viruses by both virus isolation and PCR/ESI-MS. Throat swabs from 15 patients with bacterial infections and 27 individuals without active infections were included as control samples.Respiratory viruses were found in 23.6%, 47.2%, and 47.9% of the 267 cases by virus isolation, PCR/ESI-MS, and both methods, respectively. When both methods were used, the influenza A virus (24.3%) and rhinoviruses (9.4%) were the most frequently identified viruses, whereas human coronaviruses, human metapneumovirus (hMPV), enteroviruses, adenoviruses, respiratory syncytial virus, and parainfluenza viruses were identified in small proportions of cases (<5% of cases for each type of virus). Coinfection was observed in 4.1% of cases. In the control group, only 1 (2.4%) sample tested positive for a respiratory virus by PCR/ESI-MS. Patients who were undergoing steroid treatment, had an active malignancy, or suffered from chronic obstructive pulmonary disease (COPD) were at risk for rhinovirus, hMPV, or parainfluenza infections, respectively. Overall, immunocompromised patients, patients with COPD, and patients receiving dialysis were at risk for noninfluenza respiratory virus infection. Rhinoviruses (12.7%), influenza A virus (10.9%), and parainfluenza viruses (7.3%) were the most common viruses involved in the 55 cases of lower RTIs. The factors of parainfluenza infection, old age, and immunosuppression were independently associated with lower RTIs.In conclusion, PCR/ESI-MS improved the diagnostic yield for viral RTIs. Non-influenza respiratory virus infections were associated with patients with comorbidities and with lower RTIs. Additional studies that delineate the clinical need for including non-influenza respiratory viruses in the diagnostic work-up in these populations are warranted.
Discriminating the Effects of Prenatal Alcohol Exposure From Other Behavioral and Learning Disorders

Fetal alcohol syndrome and fetal alcohol spectrum disorders are underdiagnosed in general treatment settings. Among the factors involved in identifying the effects of prenatal alcohol exposure are (1) the evidence for prenatal alcohol exposure; (2) the effects of the postnatal, caregiving environment; (3) comorbidities; and (4) differential diagnosis, which includes identifying the neurodevelopmental effects of alcohol and discriminating these effects from those characterizing other conditions. This article reviews findings on the neurodevelopmental effects of prenatal alcohol exposure, including learning and memory, motor and sensory/motor effects, visual/spatial skills, and executive functioning and effortful control. Encouraging clinicians to discriminate the effects of prenatal alcohol exposure from other conditions may require more education and training but ultimately will improve outcomes for affected children.
Characterization of Desulfitobacterium chlororespirans sp. nov., which grows by coupling the oxidation of lactate to the reductive dechlorination of 3-chloro-4-hydroxybenzoate

Strain Co23, an anaerobic spore-forming microorganism, was enriched and isolated from a compost soil on the basis of its ability to grow with 2,3-dichlorophenol (DCP) as its electron acceptor, ortho chlorines were removed from polysubstituted phenols but not from monohalophenols. Growth by chlororespiration was indicated by a growth yield of 3.24 g of cells per mol of reducing equivalents (as 2[H]) from lactate oxidation to acetate in the presence of 3-chloro-4-hydroxybenzoate but no growth in the absence of the halogenated electron acceptor. Other indicators of chlororespiration were the fraction of electrons from the electron donor used for dechlorination (0.67) and the H2 threshold concentration of < 1.0 ppm. Additional electron donors utilized for reductive dehalogenation were pyruvate, formate, butyrate, crotonate, and H2. Pyruvate supported homoacetogenic growth in the absence of an electron acceptor. Strain Co23 also used sulfite, thiosulfate, and sulfur as electron acceptors for growth, but it did not use sulfate, nitrate or fumarate. The temperature optimum for growth was 37 degrees C; however, the rates of dechlorination were optimum at 45 degrees C and activity persisted to temperatures as high as 55 degrees C. The 16S rRNA sequence was determined, and strain Co23 was found to be related to Desulfitobacterium dehalogenans JW/IU DC1 and Desulfitobacterium strain PCE1, with sequence similarities of 97.2 and 96.8%, respectively. The phylogenetic and physiological properties exhibited by strain Co23 place it into a new species designated Desulfitobacterium chlororespirans.
When conceptual worlds collide : The GUP and the BH entropy

Recently, there has been much attention devoted to resolving the quantum corrections to the Bekenstein–Hawking (black hole) entropy. In particular, many researchers have expressed a vested interest in fixing the coefficient of the sub-leading logarithmic term. In the current paper, we are able to make some substantial progress in this direction by utilizing the generalized uncertainty principle (GUP). Notably, the GUP reduces to the conventional Heisenberg relation in situations of weak gravity but transcends it when gravitational effects can no longer be ignored. Ultimately, we formulate the quantum-corrected entropy in terms of an expansion that is consistent with all previous findings. Moreover, we demonstrate that the logarithmic prefactor (indeed, any coefficient of the expansion) can be expressed in terms of a single parameter that should be determinable via the fundamental theory.
Automatic Sliding Window Operation Optimization for FPGA-Based

FPGA-based computing boards are frequently used as hardware accelerators for image processing algorithms based on sliding window operations (SWOs). SWOs are both computationally intensive and data intensive and benefit from hardware acceleration with FPGAs, especially for delay sensitive applications. The current design process requires that, for each specific application using SWOs with different size of window, image, etc.; a detail design must be completed before a realistic estimate of the achievable speedup can be obtained. We present an automated tool, sliding window operation optimization (SWOOP), that generates the estimate of speedup for a high performance design before detailed implementation is complete. The achievable speedup is determined by the area of the FPGA, or, more often, the memory bandwidth to the processing elements. The memory bandwidth to each processing element is a combination of bandwidth to the FPGA and the efficient use of on-chip RAM as a data cache. SWOOP uses analytic techniques to automatically determine the number of parallel processing elements to implement on the FPGA, the assignment of input and output data to on-board memory, and the organization of data in on-chip memory to most effectively keep the processing elements busy. The result is a block layout of the final design, its memory architecture, and a measure of the achievable speedup. The results, compared to manual designs, show that the estimates obtained usinq SWOOP are very accurate
Tracking moving object via unscented particle filter in sensor network

Moving object tracking is one of the typical applications in wireless sensor network (WSN). As a result, a lot of important solutions have been proposed in the last decade, toward addressing different aspects of object tracking in WSN settings. This work describes an Unscented particle filter (UPF) based Moving Object Tracking algorithm, UMOT, in WSN settings, where the sensor nodes are clustered dynamically to provide sensing and data fusion tasks. The key idea of UPF is to capture accurately the posterior mean and covariance of non-linear Gaussian variable up to the second order, through propagating a set of sample points in the state system. It has been demonstrated that UPF addresses the deficiency of using transition prior as the proposal distribution, which results in biased posterior estimation due to excluding the recent observations. We present the design and implementation of UMOT, together with comprehensive simulations, conducted to evaluate the proposed methods. The simulation results show that UMOT achieves significant improvement over existing schemes in various network settings.