Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Feasibility of Data Loss Insurance for Personal Cloud Storage

Personal data are important assets that people nowadays entrust with cloud storage services for the convenience of easy, ubiquitous access. To attract/retain customers, cloud storage companies aggressively replicate and georeplicate data. Such replication may be overcautious for the majority of data objects and contributes to the relatively high price of cloud storage. Yet cloud storage companies are reluctant to provide costumers with any guarantee against permanent data loss. In this paper, we discuss the viability for cloud storage service to provide optional data insurance. We examine major risks associated with cloud storage data loss and derive a crude model for premium calculation. The estimated premium level (per unit declared value) in most scenarios is found significantly smaller than that accepted in mature businesses like shipping. Therefore, optional insurance can potentially provide cloud storage services with more flexibility and cost-effectiveness in resource management, and customers with both peace of mind and lowered cost.
Traffic grooming in unidirectional WDM ring networks using design theory

We address the problem of traffic grooming in WDM rings with all-to-all uniform unitary traffic. We want to minimize the total number of SONET add-drop multiplexers (ADMs) required. We show that this problem corresponds to a partition of the edges of the complete graph into subgraphs, where each subgraph has at most C edges (where C is the grooming ratio) and where the total number of vertices has to be minimized. Using tools of graph and design theory, we optimally solve the problem for practical values and infinite congruence classes of values for a given C, and thus improve and unify all the preceding results. We disprove a conjecture of [A.L. Chiu and E.H. Modiano, 2000] saying that the minimum number of ADMs cannot be achieved with the minimum number of wavelengths and also another conjecture of [J.Q. Hu, 2002].
G-CRLB analysis of time delay based localization for decode-and-forward cooperative relay networks in multipath environments

In this study, we investigate the fundamental limits for time delay based location estimation in decode-andforward (DF) relaying strategy based cooperative relay networks considering multipath environments. The limits are presented by obtaining Cramer-Rao Lower Bounds (CRLBs) expressions. Simulations are performed to verify the CRLB expressions and show the effects of the node topology of the network, number of relay nodes, and number of multipath components (MPCs) on the location estimation accuracy of source node. According to the results, the placement of the relay nodes is very important to lower the location estimation error. Furthermore, using more relay nodes decreases the location estimation error, dramatically. Finally, processing more MPCs at the destination node is crucial to improve the estimation accuracy in the cooperative relay networks.
Analyzing uplink capacity of partially overlapping channel based WLANs using a hyper-receiver

Spectrum allocation within the fixed unlicensed band affects performance of wireless networks. Fundamental limitations of spectral efficiency on capacity of wireless local area networks (WLANs) hence needs to be studied. Recently, it was shown that the performance can be increased when both non-overlapping and partially overlapping channels are used. Unlike previous studies, this is the first known attempt on analysis of information theoretic capacity for partially-overlapping channels, as characterized by IEEE 802.11b-type systems using a hyper-receiver. Using Marenko-Pastur law distribution, capacity is approximated for such systems. We implement this by proposing a novel channel model for our analysis. The results conform to Monte Carlo simulations, with an approximate mean error of less than 5%. Fundamental tradeoffs of channel interference and power gain are discussed. We further explore the effect of access point density and find that systems based on partially overlapping channels perform well in high density deployments. We also compute capacity for a range of channel overlaps and conclude that rates close to capacity are achievable for a channel overlap of 90% and above.
Parsing collective behaviors by hierarchical model with varying structure

Collective behaviors are usually composed of several groups. Considering the interactions among groups, this paper presents a novel framework to parse collective behaviors for video surveillance applications. We first propose a latent hierarchical model (LHM) with varying structure to represent the behavior with multiple groups. Furthermore, we also propose a multi-layer-based (MLB) inference method, where a sample-based heuristic search (SHS) is introduced to infer the group affiliation. And latent SVM is adopted to learn our model. With the proposed LHM, not only are the collective behaviors detected effectively, but also the group affiliation in the collective behaviors is figured out. Experiment results demonstrate the effectiveness of the proposed framework.
A Universal Signature in Whole Genomes

Genomes are replete with duplicated sequences in the form of paralogs, transposons, pseudogenes, simple repeats, and others. To understand the origin of this phenomenon we did a systematic study of occurrence frequencies of short words in all extant complete genomes and found a common pattern of duplications in complete genomes so clear and pronounced that it allows all the genomes except one to be placed in a single class expressed by an extremely simple formula. Our analysis including extensive computer simulation in growth of DNA sequences shows that the formation of the class may be attributed to a universal genome growth mechanism in which maximally stochastic segmental duplication is the major mode of growth.
Turbo-like Decoding Algorithm for Structured LDPC codes

This paper presents a high-speed "turbo-like" decoding algorithm for certain structured LDPC codes such as those adopted in IEEE 802.16e and in the draft 802.11n standards. It is shown that after a key modification, such LDPC codes may be processed as generalized repeat accumulate codes, codes which are known to support "turbo-like" decoding. A GRA-like encoder of structured LDPC codes is derived, which in turn leads to the decoding algorithm. It is also shown that the "structured" properties result in an inherent parallelism, leading to an efficient high speed decoder implementation
Potential predictability and extended range prediction of Indian summer monsoon breaks

Extended range prediction (two to three weeks in advance) of Indian summer monsoon active (rainy) and break (dry) phases are of great importance for agricultural planning and water management. Using daily rainfall and circulation data for 23 years, a fundamental property of the monsoon intraseasonal oscillations (ISO's) is discovered and shown that the potential predictability limit (∼20 days) of monsoon breaks is significantly higher than that for active conditions (∼10 days). An empirical model for prediction of monsoon ISO's is then constructed and feasibility of useful prediction of monsoon breaks up to 18 days in advance is demonstrated.
Self-Silencing Attitudes and Behaviors in Adolescent Couples: Links With Relationship Satisfaction

Copyright 2010 by Psi Chi, The International Honor Society in Psychology (Vol. 15, No. 2/ISSN 1089-4136) According to a developmental perspective, people are motivated to form close relationships with romantic partners to fulfill social and attachment needs that parents and friends previously met (Collins & Sroufe, 1999). Earlier relationships with peers and parents provided the experience by which people learned about and developed expectations about relationships. They also learned behaviors and ways of interacting that they will use in later relationships (Furman & Wehner, 1997). Researchers of romantic relationships hypothesize that romantic relationships during adolescence form the foundation for romantic experiences across the lifetime (e.g., Collins, 2003; Furman & Wehner, 1994). For this reason, understanding behaviors in healthy and maladaptive romantic relationships in adolescence may have important implications for prevention and intervention initiatives with both adolescent and adult romantic couples. In this study, we examined the psychological and behavioral construct of self-silencing as an important correlate of global relationship functioning in adolescent couples.
Scheduling Timed Modules for Correct Resource Sharing

Real-time embedded systems typically include concurrent tasks of different priorities with time-dependent operations accessing common resources. In this context, unsynchronized parallel executions may lead to hazard situations caused by e.g., race conditions. To be able to detect such faulty system behaviors before implementation, we introduce a unified model of resource constrained, scheduled real-time system descriptions, in Alur's and Henzinger's rigorous framework of timed reactive modules. We take a component-based design perspective and construct the realtime system model, by refinement, as a composition of realtime periodic preemptible tasks with encoded functionality, and a fixed-priority scheduler, all modeled as timed modules. For the model, we express the notions of race condition and redundant locking, formally, as invariance properties that can be verified by model-checking.
A Detection Method of WCDMA Signal Based on the Quadratic Fourth-Order Moment Chip at Lower SNR

Wideband code division multiple access signal is non-periodic signal, which always floods in the background noise, bringing difficulties to the signal's reconnaissance. In order to detecting this signal, the detection method based on the quadratic fourth-order moment chip of WCDMA is proposed, and the amount of calculation is reduced by using fast calculation method about fourth-order moment chip. Experimental results show that the detection performance of the method is better than that of the double frequency method, spectrum-reprocessing method. When the false alarm probability is 1%, the detection probability of presented methods reaches above 90% at SNR -21dB.
Defining dynamic Bayesian networks for probabilistic situation assessment

In surveillance systems, the situation awareness of decision makers is often a crucial point in making appropriate decisions. For supporting the situation assessment process, modules performing an automatic interpretation of the observed environment can be used. However, there is still a need for an optimal solution for the definition of such modules. In this article we describe how situations of interest can be modeled in a human-understandable way and how their existence can be inferred from sensor observations by the use of dynamic Bayesian networks. A crucial point of modeling such networks is the definition of the parameters, namely the conditional probabilities. We present a method for an automatic definition of the parameters that can be easily used by a human operator when designing a new network. By using this approach, we define two example networks that are able to recognize situations of interest in the VIRAT dataset. Finally, the two networks are applied to the VIRAT dataset and we present an evaluation of the performance of the automatic situation assessment.
Abandoned object detection with logical reasoning

Abandoned object detection is an essential requirement in many video surveillance contexts. We introduce an abandoned object detection tool based on a set of possible events and on a set of rules to act upon those events. This implementation is simple and reusable unlike existing techniques. It is implemented using a simple logical reasoning upon textual data, in contrast to image centric processing. Objects foreign to a usual environment are extracted using background subtraction. Results of blob detection and tagging process are passed to an abandoned object detector in a textual format. The abandoned object detector, which is an acyclic graph of asynchronously interconnected lightweight processing modules, evaluates the variations of speeds and inter-blob distances. By configuring several parameters according to the context, it generates an alert upon encountering such a scenario. We provide results of this implementation by applying it on PETS 2006 dataset.
The Empirics of International Monetary Transmission: Identification and the Impossible Trinity

The transmission of monetary policy across borders is central to many open economy models. Research has tried to evaluate the "impossible trinity" through estimating international interest rate linkages under alternative exchange rate regimes using realized base country interest rates. Such interest rates include anticipated and endogenous elements, which need not propagate internationally. We compare international interest rate responses under pegged and non-pegged regimes to identified, unanticipated, and exogenous U.S. interest rate changes and realized U.S. interest rate changes. We find important differences in estimated transmission from the two sets of measures-identified interest rate changes demonstrate a greater concordance with the impossible trinity than realized rate changes. Copyright (c) 2010 The Ohio State University.
Unsupervised Negation Focus Identification with Word-Topic Graph Model

Due to the commonality in natural language, negation focus plays a critical role in deep understanding of context. However, existing studies for negation focus identification major on supervised learning which is timeconsuming and expensive due to manual preparation of annotated corpus. To address this problem, we propose an unsupervised word-topic graph model to represent and measure the focus candidates from both lexical and topic perspectives. Moreover, we propose a document-sensitive biased PageRank algorithm to optimize the ranking scores of focus candidates. Evaluation on the *SEM 2012 shared task corpus shows that our proposed method outperforms the state of the art on negation focus identification.
Performance evaluation of multi-path routing in reservation-based wireless networks

Reservation-based wireless TDMA networks can provide deterministic quality-of-service guarantees to multi-hop data flows. The challenge is to route the flows and schedule appropriate transmission slots in a resource-efficient way. In this paper, we study the fundamental question whether multi-path routing, i.e. concurrent use of multiple paths for transmission of data belonging to the same flow, may yield better solutions to the joint routing and slot scheduling problem than single-path routing approaches. To answer this question, we first define an accurate network model. We then devise a branch-and-bound algorithm that computes optimal multi-path and single-path solutions to the joint routing and slot scheduling problem. We apply the algorithm to a large number of scenarios in randomly generated networks and compare optimal network load and end-to-end delay metric values in order to quantify the benefit of multi-path routing over the single-path approach. Our results show that in the studied settings, it is quite rare for multi-path solutions to surpass the optimal single-path solution. Multi-path routing should therefore not be considered an option to improve performance in reservation-based TDMA networks with random topologies.
Centella asiatica (L.) Urban: From Traditional Medicine to Modern Medicine with Neuroprotective Potential

This paper covers the studies relevant to neuroprotective activity of Centella asiatica (L.) Urban, also known as “Gotu Kola. ” The plant is native to the Southeast Asia and has been used traditionally as brain tonic in ayurvedic medicine. The neuroprotective effect of C. asiatica has been searched using the key words “Centella, Centella asiatica, gotu kola, Asiatic pennywort, neuroprotection, and memory” through the electronic databases including Sciencedirect, Web of Science, Scopus, Pubmed, and Google Scholar. According to the literature survey, C. asiatica (gotu kola) has been reported to have a comprehensive neuroprotection by different modes of action such as enzyme inhibition, prevention of amyloid plaque formation in Alzheimer's disease, dopamine neurotoxicity in Parkinson's disease, and decreasing oxidative stress. Therefore, C. asiatica could be suggested to be a desired phytopharmaceutical with neuroprotective effect emerged from traditional medicine.
Circumnavigation by a mobile robot using bearing measurements

This paper investigates the problem of steering a nonholonomic mobile robot to achieve a circular motion around a target. We propose control schemes that require only bearing measurements and deal with two types of targets: point target and disk target. Circumnavigation schemes are developed to achieve efficient encirclement of the target. We show that using the proposed control schemes, the robot can circle the target from a prescribed radius without distance measurement and avoid collision with disk target as well. The validity of the proposed control schemes is supported by experiments on an e-puck robot.
On the Ranging and Scheduling of Data Traffic in OFDMA Mobile Environments

In the last years the wireless metropolitan area networks have increased their popularity and attracted the interest of the most important research groups all over the world. Several standards have been defined, in particular IEEE 802.16 has taken a relevant role in achieving a full-service broadband wireless network all over a urban and suburban area. This paper deals with the resource allocation problem by considering the resource request and data scheduling of the active stations. The considered scenario is foreseen in an urban environment where multiple stations can move around the Base Station. The proposed scheduling algorithms exploit the orthogonal frequency division multiple access scheme with adaptive modulation techniques for maximizing the network performance. The performance results have been obtained by resorting to theoretical analysis and computer simulations.
A Fast Method for Textural Analysis of DCT-Based Image

The multiresolution wavelet transform is an effective procedure in texture analysis. However, many images are still compressed by the methods based on the discrete cosine transform (DCT). Thus, decompression of the inverse DCT is required to yield the textural features based on the wavelet transform for the DCT-coded image. This investigation adopts the multiresolution reordered features in texture analysis. The proposed features are directly generated using the DCT coefficients of the encoded image. Comparisons between the subband-energy features extracted from the wavelet transform and the conventional DCT, using the Brodatz texture database, demonstrate that the proposed method offers the best textural pattern retrieval accuracy and yields a much higher classification rate. The proposed DCT features are expected to be very useful and efficient in retrieving and classifying texture patterns in large DCT-coded image databases.
Interactive Dynamic Simulator for Humanoid Robots with Deformable Soles

We present an interactive dynamic simulator for poly-articulated bodies. We use constraint-based methods to compute contact forces with non-discretized friction, allowing fast simulation. The user can interact with the virtual environment while sensing force feedback through a haptic device. We integrated in our simulator the possibility to design and model an external passive system that allows to absorb shocks generated by impacts. Especially we fixed in simulation a compliant sole under HRP-2 humanoid robot feet. We illustrate our talk by giving a simulation example.
Differential age-related changes in N170 responses to upright faces, inverted faces, and eyes in Japanese children

The main objectives of this study were to investigate the development of face perception in Japanese children, focusing on the changes in face processing strategies (holistic and/or configural vs. feature-based) that occur during childhood. To achieve this, we analyzed the face-related N170 component, evoked by upright face, inverted face, and eyes stimuli in 82 Japanese children aged between 8- and 13-years-old. During the experiment, the children were asked to perform a target detection task in which they were told to press a button when they saw images of faces or kettles with mustaches, glasses, and fake noses; i.e., an implicit face perception task. The N170 signals observed after the presentation of the upright face stimuli were longer in duration and/or had at least two peaks in the 8–11-year-old children, whereas those seen in the 12–13-year-old children were sharp and only had a single peak. N170 latency was significantly longer after the presentation of the eyes stimuli than after the presentation of the upright face stimuli in the 10- and 12-year-old children. In addition, significant differences in N170 latency were observed among all three stimulus types in the 13-year-old children. N170 amplitude was significantly greater after the presentation of the eyes stimuli than after the presentation of the upright face stimuli in the 8–10- and 12-year-old children. The results of the present study indicate that the upright face stimuli were processed using holistic and/or configural processing by the 13-year-old children.
Title Multirate Output Feedback Based Lq Optimal Discrete-time Sliding Mode Control.( Published Version ) Multirate-output-feedback-based Lq-optimal Discrete-time Sliding Mode Control

The traditional approach for sliding mode control design has been the design of a controller to achieve a predesigned sliding objective. However, not much research has been carried out on the design of the sliding surface. This note presents a technique for designing a sliding surface such that when confined to the surface, the closed-loop system has optimality in the linear quadratic sense. The paper also proposes a multirate-output-feedback-based controller that leads the system to the aforementioned optimal sliding mode.
Speaking mode dependent pronunciation modeling in large vocabulary conversational speech recognition

In spontaneous conversational speech there is a large amount of variability due to accents, speaking styles and speaking rates (also known as the speaking mode) [3]. Because current recognition systems usually use only a relatively small number of pronunciation variants for the words in their dictionaries, the amount of variability that can be modeled is limited. Increasing the number of variants per dictionary entry is the obvious solution. Un-fortunately, this also means increasing the confusability between the dictionary entries, and thus often leads to an actual performance decrease. In this paper we present a framework for speaking mode dependent pronunciation modeling. The probability of encountering pronunciation variants is de(cid:12)ned to be a function of the speaking style. The probability function is learned through decision trees from rule based generated pronunciation variants as observed on the Switchboard corpus. The framework is successfully applied to increase the performance of our state-of-the-art Janus Recognition Toolkit Switchboard recognizer signi(cid:12)cantly.
A 5-b 10-Gsample/s A/D converter for 10-Gb/s optical receivers

A 5-bit 10-Gsample/s A/D converter (ADC) is fabricated in a SiGe BiCMOS process to digitally compensate the signal distortion in a 10-Gb/s optical receiver. A fully differential, flash-type ADC has a wideband track-and-hold amplifier to mitigate the timing skew, followed by high-speed comparators with very small metastability error. It achieves a 4.1 effective bits at low input frequencies and 2.8 effective bits at full-scale 4.9-GHz input signal at 10-Gsample/s.