Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial birth-death processes with multiple changes and applications to batch service networks and c

Reversible spatial birth-death processes are studied with simultaneous jumps of multi-components. A relationship is established between (i) a product-form solution, (ii) a partial symmetry condition on the jump rates and (iii) a solution of a deterministic concentration equation. Applications studied are reversible networks of queues with batch services and blocking and clustering processes such as those found in polymerization chemistry. As illustrated by examples, known results are hereby unified and extended. An expectation interpretation of the transition rates is included.
On the use of supra model information from multiple classifiers for robust speaker identification

In this paper, we propose a text-independent speaker identiﬁcation (SI) scheme under uncertainty. In this scheme, extraction of supra model information about probability distributions in the feature space is proposed. Supra modeling is a model clustering technique which groups the speaker models into model sets where the speakers in these sets have similar properties. The scheme uses the Dempster-Shafer (D-S) theory of evidence to combine the model sets of two classiﬁers which are thought to provide complementary information about the speaker identity. A dependencyanalysisof classiﬁers to be combined is presented and it is shown to be effective in avoiding wrong decisions. Experimental results of the classiﬁer combination system is given at the end of the paper.
Probabilistic models for concurrent chatting activity recognition

Recognition of chatting activities in social interactions is useful for constructing human social networks. However, the existence of multiple people involved in multiple dialogues presents special challenges. To model the conversational dynamics of concurrent chatting behaviors, this article advocates Factorial Conditional Random Fields (FCRFs) as a model to accommodate co-temporal relationships among multiple activity states. In addition, to avoid the use of inefficient Loopy Belief Propagation (LBP) algorithm, we propose using Iterative Classification Algorithm (ICA) as the inference method for FCRFs. We designed experiments to compare our FCRFs model with two dynamic probabilistic models, Parallel Condition Random Fields (PCRFs) and Hidden Markov Models (HMMs), in learning and decoding based on auditory data. The experimental results show that FCRFs outperform PCRFs and HMMs-like models. We also discover that FCRFs using the ICA inference approach not only improves the recognition accuracy but also takes significantly less time than the LBP inference method.
An effective approach of intrinsic and extrinsic domain relevance technique for feature extraction in opinion mining

Opinion mining, also known as sentiment analysis, refers to the method of identifying and extracting subjective information from the source materials by the use of natural language processing, text analysis and computational linguistics. It has gained immense amount of importance in recent times due to the growing number of blogs, forums and other social networks which contain huge amount of opinions. Feature extraction is an important factor in opinion mining which refers to the method of extracting those properties on which the opinions are based on. There is a method for identifying features based on Intrinsic and Extrinsic Domain Relevance (IEDR) which exploits the difference in opinion feature's statistics across two corpora. The approach includes syntactic rules to process the review sentences and a well known and generalized weight equation with a numerical statistic known as Term Frequency-Inverse Document Frequency to calculate domain relevance which often fails to identify many of the legitimate features. So in this paper, we propose an effective approach of this IEDR technique for the purpose of feature extraction. Our proposed approach includes a handful of extended syntactic rules to process review sentences. It also includes optimization in calculation of domain relevance with the modification of weight equation. To verify our proposed approach, we have applied it on two real-world review corpora along with the existing IEDR approach. Our proposed approach exhibits a remarkable improvement in performance for finding opinion features outperforming the currently existing IEDR method.
Compressive parameter estimation with multiple measurement vectors via structured low-rank covariance estimation

In this paper, we study the problem of frequency estimation from partial observations of multiple measurement vectors under a sparsity constraint. We develop a two-step approach which first estimates a low-rank Hermitian Toeplitz covariance matrix from the partially observed sample covariance matrix via convex optimization, then recovers the set of frequencies via conventional spectrum estimation methods such as MUSIC. Our method doesn't assume discretization of the underlying frequencies, therefore overcomes the basis mismatch problem in conventional compressed sensing [1], and can possibly recover a higher number of frequencies than the number of samples per measurement vector. Numerical examples are provided to validate the performance of the proposed algorithm, with comparisons against several existing approaches.
The Impact of Process Faults on Specific Parameters of a 1.9GHz CMOS Mixer

This paper presents the impact of process faults - such as parameter variations and process variations - on specific parameters of a mixer - like linearity (1 dB CP, IIP3), noise figure, voltage conversion gain, voltage output signal. A review of mathematical types that shows the relations between specifications and parameters of the mixer is the tool to explain the behavior of the circuit under the presence of process shifts and parasitic elements. A differential Gilbert-Cell active mixer with degeneration resistors has been designed in a 90nm CMOS UMC technology to validate the theoretical conclusions and investigate the possibility of restoring the circuit prescriptions to the desired values, varying one or more parameters.
Connectivity, Coverage and Placement in Wireless Sensor Networks

Wireless communication between sensors allows the formation of flexible sensor networks, which can be deployed rapidly over wide or inaccessible areas. However, the need to gather data from all sensors in the network imposes constraints on the distances between sensors. This survey describes the state of the art in techniques for determining the minimum density and optimal locations of relay nodes and ordinary sensors to ensure connectivity, subject to various degrees of uncertainty in the locations of the nodes.
Adult Stem Cell Therapies for Wound Healing: Biomaterials and Computational Models

The increased incidence of diabetes and tumors, associated with global demographic issues (aging and life styles), has pointed out the importance to develop new strategies for the effective management of skin wounds. Individuals affected by these diseases are in fact highly exposed to the risk of delayed healing of the injured tissue that typically leads to a pathological inflammatory state and consequently to chronic wounds. Therapies based on stem cells (SCs) have been proposed for the treatment of these wounds, thanks to the ability of SCs to self-renew and specifically differentiate in response to the target bimolecular environment. Here, we discuss how advanced biomedical devices can be developed by combining SCs with properly engineered biomaterials and computational models. Examples include composite skin substitutes and bioactive dressings with controlled porosity and surface topography for controlling the infiltration and differentiation of the cells. In this scenario, mathematical frameworks for the simulation of cell population growth can provide support for the design of bioconstructs, reducing the need of expensive, time-consuming, and ethically controversial animal experimentation.
Dust and Biological Aerosols from the Sahara and Asia Influence Precipitation in the Western U.S.

Action at a Distance Snowfall in the Sierra Nevada provides a large fraction of the water that California receives as precipitation. Knowing what factors influence the amount of snow that falls is thus critical for projecting how water availability may change in the future. Aerosols have an important effect on cloud processes and precipitation. Creamean et al. (p. 1572, published online 28 February) found that dust and biological aerosols originating from as far away as the Sahara facilitate ice nuclei formation and ice-induced precipitation in the Sierra Nevada and show how dust and biological articles from places as distant as Africa and Asia can influence precipitation over the western United States. Dust and biological aerosols from the Sahara and Asia can act as ice nuclei for precipitation in California’s Sierra Nevada. Winter storms in California’s Sierra Nevada increase seasonal snowpack and provide critical water resources and hydropower for the state. Thus, the mechanisms influencing precipitation in this region have been the subject of research for decades. Previous studies suggest Asian dust enhances cloud ice and precipitation, whereas few studies consider biological aerosols as an important global source of ice nuclei (IN). Here, we show that dust and biological aerosols transported from as far as the Sahara were present in glaciated high-altitude clouds coincident with elevated IN concentrations and ice-induced precipitation. This study presents the first direct cloud and precipitation measurements showing that Saharan and Asian dust and biological aerosols probably serve as IN and play an important role in orographic precipitation processes over the western United States.
Compact Data Structures for Temporal Graphs

Summary form only given. In this paper we propose three compact data structures to answer queries on temporal graphs. We define a temporal graph as a graph whose edges appear or disappear along time. Possible queries are related to adjacency along time, for example, to get the neighbors of a node at a given time point or interval. A naive representation consists of a time-ordered sequence of graphs, each of them valid at a particular time instant. The main issue of this representation is the unnecessary use of space if many nodes and their connections remain unchanged during a long period of time. The work in this paper proposes to store only what changes at each time instant. The ttk2-tree is conceptually a dynamic k2-tree in which each leaf and internal node contains a change list of time instants when its bit value has changed. All the change lists are stored consecutively in a dynamic sequence. During query processing, the change lists are used to expand only valid regions in the dynamic k2-tree. It supports updates of the current or past states of the graph. The ltg-index is a set of snapshots and logs of changes between consecutive snapshots. The structure keeps a log for each node, storing the edge and the time where a change has been produced. To retrieve direct neighbors of a node, the previous snapshot is queried, and then the log is traversed adding or removing edges to the result. The differential k2-tree stores snapshots of some time instants in k2-trees. For the other time instants, a k2-tree is also built, but these are differential (they store the edges that differ from the last snapshot). To perform a query it accesses the k2-tree of the given time and the previous full snapshot. The edges that appear in exactly one of these two k2-trees will be the final results. We test our proposals using synthetic and real datasets. Our results show that the ltg-index obtains the smallest space in general. We also measure times for direct and reverse neighbor queries in a time instant or a time interval. For all these queries, the times of our best proposal range from tens of μs to several ms, depending on the size of the dataset and the number of results returned. The ltg-index is the fastest for direct queries (almost as fast as accessing a snapshot), but it is 5-20 times slower in reverse queries. The differential k2-tree is very fast in time instant queries, but slower in time interval queries. The ttk2-tree obtains similar times for direct and reverse queries and different time intervals, being the fastest in some reverse interval queries. It has also the advantage of being dynamic.
Data integrity and intrusion detection in Wireless Sensor Networks

Wireless sensor networks are emerging as cost effective solutions to critical applications such as remote monitoring of strategic installations covering large geographical areas, pollution control and disaster recovery. Their ability of self-organization, built-in redundancy and operational capabilities without needing human intervention make Wireless Sensor Networks suitable for such remote monitoring applications. But the unattended operation of WSNs and limited processing capabilities of the individual nodes make them vulnerable to a variety of security attacks, especially disruption of information flow or data corruption. For wired or wireless ad-hoc networks, there exist several automated systems for maintaining their functional integrity. Unfortunately, these strategies are not directly applicable to WSNs since ad-hoc networks are not as resource constrained as WSNs.This paper tries to develop distributed algorithms for detecting anomalous activity in information flow patterns in large-scale sensor networks. These algorithms will be used as building blocks of an integrated system for intrusion detection, attack isolation and fully automated response for assuring WSN survivability and information flow continuity.
From the Cover : Accelerated telomere shortening in response to life stress

www.pnas. org/cgi/content/full/101/49/17312#otherarticles This article has been cited by other articles: E-mail Alerts . click here at the top right corner of the article or Receive free email alerts when new articles cite this article sign up in the box Rights & Permissions www.pnas. org/misc/rightperm.shtml To reproduce this article in part (figures, tables) or in entirety, see: Reprints www.pnas. org/misc/reprints.shtml To order reprints, see:
Ankle dorsiflexor muscle performance in healthy young men and women: reliability of eccentric peak torque and work measurements.

The aims of this study were: (i) to assess the test-retest intrarater reliability of eccentric ankle dorsiflexor muscle performance in young healthy men and women using the Biodex dynamometer; and (ii) to examine different statistical indices for the interpretation of reliability. Thirty men and women (age 22.5 +/- 2.5 years, mean +/- S.D.) performed three maximal eccentric contractions at 30 degrees/second and 90 degrees/second, with 7-10 days between test sessions. Reliability was evaluated with three intraclass correlation coefficients (ICC1,1, ICC2,1 and ICC3,1), and was excellent for peak torque (ICC 0.90-0.96) and good to excellent for work (ICC 0.69-0.83), with no discernible differences among the three ICCs. Method errors, assessed by the standard error of the measurement (S.E.M.) and S.E. M. %, were low. The Bland & Altman graphs and analyses indicated no significant systematic bias in the data. In conclusion, measurements of eccentric ankle dorsiflexor muscle performance in young healthy individuals using the Biodex are highly reliable.
Performance evaluation of the LH*lh scalable, distributed data structure for a cluster of workstations

Scalable, Distributed Data Structures (SDDSs) can provide fast access to large volumes of data. They allow the data structure to grow or shrink without suffering from a penalty with respect to the space utilization or the average access time. In this paper, we present a performance study of one particular SDDS, called LH*LH, which has been implemented for a cluster of workstations. Our experimental results demonstrate that our LH*LH implementation is truly scalable and yields access-times that are of an order of magnitude smaller than a typical disk access. Furthermore, we also show that parallel access to the LH*LH data structure can speed up client applications quite significantly .
Learning to Pronounce First Words in Three Languages: An Investigation of Caregiver and Infant Behavior Using a Computational Model of an Infant

Words are made up of speech sounds. Almost all accounts of child speech development assume that children learn the pronunciation of first language (L1) speech sounds by imitation, most claiming that the child performs some kind of auditory matching to the elements of ambient speech. However, there is evidence to support an alternative account and we investigate the non-imitative child behavior and well-attested caregiver behavior that this account posits using Elija, a computational model of an infant. Through unsupervised active learning, Elija began by discovering motor patterns, which produced sounds. In separate interaction experiments, native speakers of English, French and German then played the role of his caregiver. In their first interactions with Elija, they were allowed to respond to his sounds if they felt this was natural. We analyzed the interactions through phonemic transcriptions of the caregivers' utterances and found that they interpreted his output within the framework of their native languages. Their form of response was almost always a reformulation of Elija's utterance into well-formed sounds of L1. Elija retained those motor patterns to which a caregiver responded and formed associations between his motor pattern and the response it provoked. Thus in a second phase of interaction, he was able to parse input utterances in terms of the caregiver responses he had heard previously, and respond using his associated motor patterns. This capacity enabled the caregivers to teach Elija to pronounce some simple words in their native languages, by his serial imitation of the words' component speech sounds. Overall, our results demonstrate that the natural responses and behaviors of human subjects to infant-like vocalizations can take a computational model from a biologically plausible initial state through to word pronunciation. This provides support for an alternative to current auditory matching hypotheses for how children learn to pronounce.
High-Dialysate-Glucose-Induced Oxidative Stress and Mitochondrial-Mediated Apoptosis in Human Peritoneal Mesothelial Cells

Human peritoneal mesothelial cells (HPMCs) are a critical component of the peritoneal membrane and play a pivotal role in dialysis adequacy. Loss of HPMCs can contribute to complications in peritoneal dialysis. Compelling evidence has shown that high-dialysate glucose is a key factor causing functional changes and cell death in HPMCs. We investigated the mechanism of HPMC apoptosis induced by high-dialysate glucose, particularly the role of mitochondria in the maintenance of HPMCs. HPMCs were incubated at glucose concentrations of 5 mM, 84 mM, 138 mM, and 236 mM. Additionally, N-acetylcysteine (NAC) was used as an antioxidant to clarify the mechanism of high-dialysate-glucose-induced apoptosis. Exposing HPMCs to high-dialysate glucose resulted in substantial apoptosis with cytochrome c release, followed by caspase activation and poly(ADP-ribose) polymerase cleavage. High-dialysate glucose induced excessive reactive oxygen species production and lipid peroxidation as well as oxidative damage to DNA. Mitochondrial fragmentation, multiple mitochondrial DNA deletions, and dissipation of the mitochondrial membrane potential were also observed. The mitochondrial dysfunction and cell death were suppressed using NAC. These results indicated that mitochondrial dysfunction is one of the main causes of high-dialysate-glucose-induced HPMC apoptosis.
Geographical Differences Associated with Single-Nucleotide Polymorphisms (SNPs) in Nine Gene Targets among Resistant Clinical Isolates of Mycobacterium tuberculosis

ABSTRACT Alternative diagnostic methods, such as sequence-based techniques, are necessary for increasing the proportion of tuberculosis cases tested for drug resistance. Despite the abundance of data on drug resistance, isolates can display phenotypic resistance but lack any distinguishable markers. Furthermore, because resistance-conferring mutations develop under antibiotic pressure, different drug regimens could favor unique single-nucleotide polymorphisms (SNPs) in different geographical regions. A total of 407 isolates were collected from four geographical regions with a high prevalence of drug-resistant tuberculosis (India, Moldova, the Philippines, and South Africa). The “hot spot” or promoter sequences of nine genes (rpoB, gyrA, gyrB, katG, inhA promoter, ahpC promoter, eis promoter, rrs, and tlyA) associated with resistance to four types of antibiotics (rifampin, isoniazid, fluoroquinolones, and aminoglycosides) were analyzed for markers. Four genes contributed largely to resistance (rpoB, gyrA, rrs, and katG), two genes contributed moderately to resistance (the eis and inhA promoters), and three genes contributed little or no resistance (gyrB, tlyA, and the ahpC promoter) in clinical isolates. Several geographical differences were found, including a double mutation in rpoB found in 37.1% of isolates from South Africa, the C→T mutation at position −12 of the eis promoter found exclusively in 60.6% of isolates from Moldova, and the G→A mutation at position −46 of the ahpC promoter found only in India. These differences in polymorphism frequencies emphasize the uniqueness of isolates found in different geographical regions. The inclusion of several genes provided a moderate increase in sensitivity, and elimination of the examination of other genes might increase efficiency.
HTML 5 in Mobile Devices -- Drivers and Restraints

Application stores have played a crucial role in the proliferation of applications for smart phones and other mobile devices. However, web-based mobile applications are challenging the application store model by allowing developers to directly reach the end users. These web-based applications are enhanced by the HTML5 standard, which provides additional capabilities for the use of developers and brings the performance of mobile web applications closer to that of native applications. In this paper, we analyze the potential of HTML5 and identify drivers and restraints that affect the future of the technology.
Novel Motility Mutants ofSynechocystis Strain PCC 6803 Generated by In Vitro Transposon Mutagenesis

ABSTRACT We screened for transposon-generated mutants ofSynechocystis sp. strain PCC 6803 that exhibited aberrant phototactic movement. Of the 300 mutants generated, about 50 have been partially characterized; several contained transposons in genes encoding chemotaxis-related proteins, while others mapped to novel genes. These novel genes and their possible roles in motility are discussed.
The ismorphism conjecture fails relative to a random oracle

Berman and Hartmanis [BH77] conjectured that there is a polynomial-time computable isomorphism between any two languages m-complete (“Karp” complete) for NP. Joseph and Young [JY85] discovered a structurally defined class of NP-complete sets and conjectured that certain of these sets (the <italic>K<supscrpt>k</supscrpt><subscrpt>ƒ</subscrpt></italic>'s) are not isomorphic to the standard NP-complete sets for some one-way functions ƒ. These two conjectures cannot both be correct. We introduce a new family of strong one-way functions, the <italic>scrambling</italic> functions. If ƒ is a scrambling function, then <italic>K<supscrpt>k</supscrpt><subscrpt>fnof;</subscrpt></italic> is not isomorphic to the standard NP-complete sets, as Joseph and Young conjectured, and the Berman-Hartmanis conjecture fails. As evidence for the existence of scrambling functions, we show that much more powerful one-way functions--the <italic>annihilating</italic> functions--exist relative to a random oracle.