Miracle-dz/UniFAR-FTUs huggingface dataset:
Radix-4 and radix-8 booth encoded interleaved modular multipliers over general Fp

This paper presents radix-4 and radix-8 Booth encoded modular multipliers over general Fp based on inter-leaved multiplication algorithm. An existing bit serial interleaved multiplication algorithm is modified using radix-4, radix-8 and Booth recoding techniques. The modified radix-4 and radix-8 versions of interleaved multiplication result in 50% and 75% reduction in required number of clock cycles for one modular multiplication over the corresponding bit serial interleaved multipliers, while maintaining a competitive critical path delay. The proposed architectures are implemented in Verilog HDL and synthesized by targeting virtex-6 FPGA platform. Due to an efficient utilization of optimized addition chains available in FPGAs and exploiting the parallelism among operations, the proposed radix-4 and radix-8 multipliers compute one 256 × 256 bit modular multiplication in 1. 49μs and 0. 93μs respectively, which are 35% and 94% improvement over the corresponding bit serial version. Further, this work also presents a thorough comparison on basis of area, throughput, and area × time per bit value. Which shows that these designs are efficiently optimized for area × time per bit value with a high throughput rate. Thus, these designs are suitable to construct most of the elliptic curve and pairing based cryptographic processors.
Insights into characteristics, sources, and evolution of submicron aerosols during harvest seasons in the Yangtze River delta region, China

Abstract. Atmospheric submicron particulate matter (PM1) is one of the most significant pollution components in China. Despite its current popularity in the studies of aerosol chemistry, the characteristics, sources and evolution of atmospheric PM1 species are still poorly understood in China, particularly for the two harvest seasons, namely, the summer wheat harvest and autumn rice harvest. An Aerodyne Aerosol Chemical Speciation Monitor (ACSM) was deployed for online monitoring of PM1 components during summer and autumn harvest seasons in urban Nanjing, in the Yangtze River delta (YRD) region of China. PM1 components were shown to be dominated by organic aerosol (OA, 39 and 41%) and nitrate (23 and 20%) during the harvest seasons (the summer and autumn harvest). Positive matrix factorization (PMF) analysis of the ACSM OA mass spectra resolved four OA factors: hydrocarbon-like mixed with cooking-related OA (HOA + COA), fresh biomass-burning OA (BBOA), oxidized biomass-burning-influenced OA (OOA-BB), and highly oxidized OA (OOA); in particular the oxidized BBOA contributes ~80% of the total BBOA loadings. Both fresh and oxidized BBOA exhibited apparent diurnal cycles with peak concentration at night, when the high ambient relative humidity and low temperature facilitated the partitioning of semi-volatile organic species into the particle phase. The fresh BBOA concentrations for the harvests are estimated as BBOA = 15.1 × (m/z 60–0.26% × OA), where m/z (mass-to-charge ratio) 60 is a marker for levoglucosan-like species. The (BBOA + OOA-BB)/ΔCO, (ΔCO is the CO minus background CO), decreases as a function of f44 (fraction of m/z 44 in OA signal), which might indicate that BBOA was oxidized to less volatile OOA, e.g., more aged and low volatility OOA (LV-OOA) during the aging process. Analysis of air mass back trajectories indicates that the high BB pollutant concentrations are linked to the air masses from the western (summer harvest) and southern (autumn harvest) areas.
On the Limits of Automatic Processor Specialisation by Mapping Dataflow Sections on Ad-hoc Functional Units

Many available systems-on-chip embedded processors can be specialised for a given application-domain by adding ad-hoc functional units. These functional units can be used to map clusters of elementary arithmetic or logic operations (sections of the dataflow graph); more complex hardware addons could attack the control flow (e.g., map loops onto sequential functional units). For automatic processor specialisation, the two strategies imply different methods and have a different complexity. Before incurring in the complexities of control flow, this paper attacks the question that appears so far unanswered: how much can one improve the performance of an embedded processor on specific algorithms by automatically mapping only dataflow sections of code on special functional units? The basic scope for performance improvement is assessed and broken out in different sources: hardware parallelism; avoided quantisation of instructions in an integral number of cycles; and simplification of the logic due to constants. The scope for speedup is increased through additional manual optimisations (amenable to automation): bit-width analysis and arithmetic optimisation. Finally, ILP techniques such as loop unrolling and predication are used to increase the size of the basic blocks and give more scope for the sources of improvement. The results show that significant improvements in speed (up to 6 times) can be targeted without necessarily mapping the control flow onto hardware.
Fingerprint Recognition using Orthogonal Polynomial Based Transform

Biometric features such as face, iris, voice, retina, handwriting and fingerprint plays an important role in security for authentication. Among biometrics, fingerprint systems have been one of most widely researched and deployed because of their easy access and low price of fingerprint sensors. Nowadays, for personal identification automatic fingerprint recognition is preferred due to their reliability and uniqueness features. Traditional minutia based and pattern based techniques face difficulty in extraction of minutia points and patterns in fingerprints. This paper proposed an efficient feature extraction technique for fingerprint recognition based on Orthogonal Polynomial based Transform. Statistical analysis on the fingerprint image is done and a candidate region suitable for feature extraction is selected. This region is subjected to the Orthogonal Polynomial Transform and transform coefficients obtained forms the feature vector. These feature vectors act as representatives for a fingerprint image and recognition is done using Support Vector Machine. Index Terms — Biometrics, fingerprint recognition, Orthogonal Polynomial Transform, Support Vector Machine.
NMF-based blind source separation using a linear predictive coding error clustering criterion

Non-negative matrix factorization (NMF) based sound source separation involves two phases: First, the signal spectrum is decomposed into components which, in a second step, are clustered in order to obtain estimates of the source signal spectra. The major challenge with this approach is the accuracy of the clustering algorithm in the second step, especially as most previously used clustering algorithms are focusing on the frequency part of NMF only and, hence, are missing the information of the time activation matrix. In this paper, we propose a novel clustering criterion which combines the frequency and time activation part of NMF. It is based on the linear predictive coding compression error and we show that it allows a good clustering of the NMF components while at the same time can be efficiently computed. Our new clustering criterion shows an overall improved performance compared with the current state-of-the-art clustering algorithms as we experiment on the TRIOS dataset.
Evidence of the Impacts of Urban Sprawl on Social Capital

Sprawl opponents have blamed sprawl for weakening linkages among the residents and social capital but there is a lack of empirical evidence to support their argument. This study examines the relationship between the county sprawl index and social-capital factors from the 2000 Social Capital Community Benchmark Survey data. Using 3-level hierarchical models, this study shows that for the US, urban sprawl may support some types of social capital while negatively impacting the others. Furthermore, changing urban form via population density and street accessibility at the county level may not be ideal to improve social capital.
Genetic Inhibition of CaMKII in Dorsal Striatal Medium Spiny Neurons Reduces Functional Excitatory Synapses and Enhances Intrinsic Excitability

Ca2+/calmodulin-dependent protein kinase II (CaMKII) is abundant in striatal medium spiny neurons (MSNs). CaMKII is dynamically regulated by changes in dopamine signaling, as occurs in Parkinson's disease as well as addiction. Although CaMKII has been extensively studied in the hippocampus where it regulates excitatory synaptic transmission, relatively little is known about how it modulates neuronal function in the striatum. Therefore, we examined the impact of selectively overexpressing an EGFP-fused CaMKII inhibitory peptide (EAC3I) in striatal medium spiny neurons (MSNs) using a novel transgenic mouse model. EAC3I-expressing cells exhibited markedly decreased excitatory transmission, indicated by a decrease in the frequency of spontaneous excitatory postsynaptic currents (sEPSCs). This decrease was not accompanied by changes in the probability of release, levels of glutamate at the synapse, or changes in dendritic spine density. CaMKII regulation of the AMPA receptor subunit GluA1 is a major means by which the kinase regulates neuronal function in the hippocampus. We found that the decrease in striatal excitatory transmission seen in the EAC3I mice is mimicked by deletion of GluA1. Further, while CaMKII inhibition decreased excitatory transmission onto MSNs, it increased their intrinsic excitability. These data suggest that CaMKII plays a critical role in setting the excitability rheostat of striatal MSNs by coordinating excitatory synaptic drive and the resulting depolarization response.
An acoustic profile of consonant reduction

Vowel reduction has been studied for years. It is a universal phenomenon that reduces the distinction of vowels in informal speech and unstressed syllables. How consonants behave in situations where vowels are reduced is much less well known. The authors compare durational and spectral data (for both intervocalic consonants and vowels) segmented from read speech with otherwise identical segments from spontaneous speech. On a global level, it shows that consonants reduce like vowels when the speaking style becomes informal. On a more detailed level there are differences related to the type of the consonant.
Face Recognition Based on Multi-classifierWeighted Optimization and Sparse Representation

Facial recognition (FR) is a challenging area of research due to difficulties with robust FR when the number of training samples is very small. The state-of-the-art sparse representation-based classification (SRC) shows very excellent FR performance. However, the recognition rate of SRC will drop dramatically when the number of training samples per class is very limited. To solve these issues, we propose a weighted multi-classifier optimization and sparse representation based (WMSRC) method for FR, which efficiently combines the local and global characteristics of face images. A face image is firstly divided into continuous but non-overlapped blocks by multi-resolution based blocking and each block is sparsely represented over the corresponding set of blocks of all training samples. The multi-scale SRC classifiers are then established and associated with different weights based on sub-block dictionary learning. According to the multiple voting results of the classifiers, the weights of multi-classifiers are optimized by a least-squares optimization equation with 2 l -norm regularization. Finally, the classification results of all the blocks are combined by a weighted fusion criterion. Our experiments show that the WMSRC algorithm outperforms many existing block-based sparse representation classification algorithms, especially for FR when the available training samples per subject are very limited.
Innovative sparse representation algorithms for robust face recognition

In this paper, we propose two innovative and computationally efficient al- gorithms for robust face recognition, which extend the previous Sparse Representation- based Classication (SRC) algorithm proposed by Wright et al. (2009). The two new algorithms, which are designed for both batch and online modes, operate on matrix rep- resentation of images, as opposed to vector representation in SRC, to achieve efficiency whilst maintaining the recognition performance. Werst show that, by introducing a matrix representation of images, the size of the l1-norm problem in SRC is reduced from O ( whN ) to O ( rN ) , where r ≪ wh and thus higher computational efficiency can be ob- tained. We then show that the computational efficiency can be even enhanced with an online setting where the training images arrive incrementally by exploiting the interlacing property of eigenvalues in the inner product matrix. Finally, we demonstrate the supe- rior computational efficiency and robust performance of the proposed algorithms in both batch and online modes, as compared with the original SRC algorithm through numerous experimental studies.
Competition and Quality Choice in Hospital Markets *

While hospital quality is typically measured using clinical outcomes, patients are likely to also value non-clinical attributes such as hospital amenities. As a result, using clinical outcome measures to evaluate whether competition policy effectively encourages higher hospital quality will not provide a complete picture if hospitals compete along non-clinical dimensions. We examine the causal impact of competition on both a hospital’s level of demand-shifting quality and clinical quality. We find that demand-shifting quality is increased for all patients when competing for managed care patients while the impact of competition for Medicare patients is mixed. Clinical quality also improves with competition for managed care patients but declines with competition for Medicare patients. Competition appears to affect clinical quality through the selective networking practices of managed care while direct competition for patients largely affects other non-clinical quality attributes.
Naval Postgraduate School Monterey, California Graph Reduction for Emulated Network Experimentation

Network researchers and operators often turn to emulation and simulation for testing and experimentation. Obtaining topologies that reflect the graph characteristics of the Internet, while of small enough order to emulate or simulate on commodity hardware, however, is a difficult undertaking. In this work, we reexamine a previous study devoted to generating Internet-like topologies by reducing Autonomous System-level Internet instances to a more manageable scale. In addition to replicating the original experiment using Routeviews data from 2001, we extend the prior work’s methodology to more current data and to another data set compiled by the Center for Applied Internet Data Analysis. Finally, we introduce a new Internet graph reduction method, and examine its performance on both data sets.
The Spike-Triggered Average of the Integrate-and-Fire Cell Driven by Gaussian White Noise

We compute the exact spike-triggered average (STA) of the voltage for the nonleaky integrate-and-fire (IF) cell in continuous time, driven by gaussian white noise. The computation is based on techniques from the theory of renewal processes and continuous-time hidden Markov processes (e.g., the backward and forward Fokker-Planck partial differential equations associated with first-passage time densities). From the STA voltage, it is straightforward to derive the STA input current. The theory also gives an explicit asymptotic approximation for the STA of the leaky IF cell, valid in the low-noise regime 0. We consider both the STA and the conditional average voltage given an observed spike doublet event, that is, two spikes separated by some fixed period of silence. In each case, we find that the STA as a function of time-preceding-spike, , has a square root singularity as approaches zero from below and scales linearly with the scale of injected noise current. We close by briefly examining the discrete-time case, where similar phenomena are observed.
Partitioning Strategies for Load Balancing Subgraph-centric Distributed Graph Processing

Distributed graph processing platforms have helped emerging application domains use commodity clusters and Clouds to analyze large graphs. Vertex-centric programming models like Google Pregel, and their subgraph-centric variants, specify data-parallel application logic for a single vertex or component that execute iteratively. The locality and balancing of components within partitions affects the performance of such platforms. We propose three partitioning strategies for a subgraph-centric model, and analyze their impact on CPU utilization, communication, iterations, and makespan. We analyze these using Breadth First Search and PageRank algorithms on powerlaw and spatio-planar graphs. They are validated on a commodity cluster using our GoFFish subgraph-centric platform, and compared against Apache Giraph vertex-centric platform. Our experiments show upto 8 times improvement in utilization resulting to upto 5 times improvement of overall makespan for flat and hierarchical partitioning over the default strategy due to improved machine utilization. Further, these also exhibit better horizontal scalability relative to Giraph.
Using cross-entropy for satisfiability

This paper proposes a novel approach to SAT solving by using the cross-entropy method for optimization. It introduces an extension of the Boolean satisfiability setting to a multi-valued framework, where a probability space is induced over the set of all possible assignments. For a given formula, a cross-entropy-based algorithm (implemented in a tool named CROiSSANT) is used to find a satisfying assignment by applying an iterative procedure that optimizes an objective function correlated with the likelihood of satisfaction. We investigate a hybrid approach by employing cross-entropy as a preprocessing step to SAT solving. First CROiSSANT is run to identify the areas of the search space that are more likely to contain a satisfying assignment; this information is then given to a DPLL-based SAT solver as a partial or a complete assignment that is used to suggest variables assignments in the search. We tested our approach on a set of benchmarks, in different configurations of tunable parameters of the cross-entropy algorithm; as experimental results show, it represents a sound basis for the development of a cross-entropy-based SAT solver.
Correlation between change in pulmonary function and suppression of reactive nitrogen species production following steroid treatment in COPD

Background: Reactive nitrogen species (RNS) have a number of inflammatory actions and the production of these molecules has been reported to be increased in the airways of patients with chronic obstructive pulmonary disease (COPD), which suggests that they may be involved in the inflammatory and obstructive process in COPD. Methods: The relationship between the reduction in RNS and the improvement in pulmonary function was studied in 18 patients with COPD following steroid treatment (800 μg beclomethasone dipropionate inhalation for 4 weeks). Twelve patients were treated with inhaled steroids and the others received placebo treatment. Forced expiratory volume in 1 second (FEV1) and airway responsiveness to histamine were measured before and after treatment. Induced sputum cells were stained with anti-nitrotyrosine antibody, a footprint of RNS, and RNS formation was assessed by measuring nitrotyrosine immunoreactivity. The immunoreactivity of inducible nitric oxide synthase (iNOS) in induced sputum and exhaled NO levels were also measured. Results: Treatment with steroids resulted in a significant reduction in both nitrotyrosine and iNOS immunoreactivity in sputum cells compared with pretreatment levels (both p<0.01). The reduction rates in both parameters were significantly related (p<0.05). The reduction in nitrotyrosine and iNOS immunoreactivity was correlated with the improvement in FEV1 (p<0.05) and airway responsiveness to histamine (p<0.01). None of the parameters was significantly changed by placebo administration. Conclusions: These results suggest that RNS may be involved in the reversible component of inflammation in COPD that is suppressed by steroids. Further studies using specific inhibitors for RNS are needed to clarify their effects on the long term progression of COPD.
Multilocus Sequence Typing of Klebsiella pneumoniae Nosocomial Isolates

ABSTRACT A multilocus sequence typing (MLST) scheme was developed for Klebsiella pneumoniae. Sequences of seven housekeeping genes were obtained for 67 K. pneumoniae strains, including 19 ceftazidime- and ciprofloxacin-resistant isolates. Forty distinct allelic profiles were identified. MLST data were validated against ribotyping and showed high (96%) discriminatory power. The MLST approach provides unambiguous data useful for the epidemiology of K. pneumoniae isolates.
LCC-WSD: System Description for English Coarse Grained All Words Task at SemEval 2007

This document describes the Word Sense Disambiguation system used by Language Computer Corporation at English Coarse Grained All Word Task at SemEval 2007. The system is based on two supervised machine learning algorithms: Maximum Entropy and Support Vector Machines. These algorithms were trained on a corpus created from Sem-Cor, Senseval 2 and 3 all words and lexical sample corpora and Open Mind Word Expert 1.0 corpus. We used topical, syntactic and semantic features. Some semantic features were created using WordNet glosses with semantic relations tagged manually and automatically as part of eXtended WordNet project. We also tried to create more training instances from the disambiguated WordNet glosses found in XWN project (XWN, 2003). For words for which we could not build a sense classifier, we used First Sense in WordNet as a back-off strategy in order to have coverage of 100%. The precision and recall of the overall system is 81.446% placing it in the top 5 systems.
An Investigation into Real Estate Investment and Economic Growth in China: A Dynamic Panel Data Approach

Using a dynamic panel data approach to analyze national-level and province-level data in China from 2000 to 2012, this paper studies how real estate investment affects Chinese economic growth. We find that real estate investment has significantly positive contemporaneous effects on economic growth on both national and regional levels. Surprisingly, we also find that real estate investment has negative lagged effects on economic growth. Such negative lagged effects differ among the three regions we investigated: the eastern region shows the most significant effects from real estate investment; while the middle region shows the least. Further examinations of the four types of real estate investment ( i.e. housing investment, office building investment, investment for commercial and business purposes, and other investment) show that housing investment exhibits the most influence on the economy in China. Additionally, we find that the four types of real estate investment exhibit significantly negative lagged effects on Chinese economic growth and there are regional differences in the repressive effects of the four types of real estate investments on economic growth.