Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Context Analysis and Search Quality

Applications of machine learning techniques to eDiscovery, information governance, and other related forms of classification and categorization continue to improve in performance and efficiency, but traditional search-based methods of exploration and classification—searches for keywords, key phrases, proximity searches, regular expression searches, and their Boolean combinations—have not disappeared. Part of their persistence lies in their suitability for specialized tasks such as identification of privileged or hot documents. Even in the presence of machine learning techniques to relevance filtering and information governance classification, search-based methods often play a role—as in the initial identification of seed sets used for initial training. In fact, it’s hardly surprising to see search-based methods used to support machine learning techniques and technologically assisted review. In this paper, we look at a way in which statistical techniques can be used to increase search quality, focusing on increasing precision using automated and semi-automated tools to analyze the contexts immediately surrounding search-term matches. Standardly, search quality is assessed at the document level, depending on two properties: whether a document is relevant to the goal of the search and whether the document is returned by the search. Precision—the ratio of relevant documents returned to all documents returned—is a measure of correctness. If we know (or can estimate) the number of relevant documents in the search universe, we can define the corresponding measure of completeness known as recall as the ratio of relevant documents returned to the known or estimated total of relevant documents in the search universe. Büttcher et al. describe a Boolean query designed to address TREC topic 426—a topic created for TREC 1999 involving ‘the use of dogs worldwide for law enforcement purposes’ [1, p. 25]. The proposed query [1, p. 67] takes the form:
RLC Coupling-Aware Simulation and On-Chip Bus Encoding for Delay Reduction

This paper shows that the worst case switching pattern that incurs the longest bus delay while considering the RLC effect is quite different from that while considering the RC effect alone. It implies that the existing encoding schemes based on the RC model may not improve or possibly worsen the delay when the inductance effects become dominant. A bus-invert method is also proposed to reduce the on-chip bus delay based on the RLC model. Simulation results show that the proposed encoding scheme significantly reduces the worst case coupling delay of the inductance-dominated buses
Significance of vowel epenthesis in Telugu text-to-speech synthesis

Unit selection synthesis inventories have coverage issues, which lead to missing syllable or diphone units. In the conventional back-off strategy of substituting the missing unit with approximate unit(s), the rules for approximate matching are hard to derive. In this paper we propose a back-off strategy for Telugu TTS systems emulating native speaker intuition. It uses reduced vowel insertion in complex consonant clusters to replace missing units. The inserted vowel identity is determined using a rule-set adapted from L2 (second language) acquisition research in Telugu, reducing the effort required in preparing the rule-set. Subjective evaluations show that the proposed back-off method performs better than the conventional methods.
Insertion of a Specific Fungal 3′-phosphoadenosine-5′-phosphatase Motif into a Plant Homologue Improves Halotolerance and Drought Tolerance of Plants

Soil salinity and drought are among the most serious agricultural and environmental problems of today. Therefore, investigations of plant resistance to abiotic stress have received a lot of attention in recent years. In this study, we identified the complete coding sequence of a 3′-phosphoadenosine-5′-phosphatase protein, ApHal2, from the halotolerant yeast Aureobasidium pullulans. Expression of the ApHAL2 gene in a Saccharomyces cerevisiae hal2 mutant complemented the mutant auxotrophy for methionine, and rescued the growth of the hal2 mutant in media with high NaCl concentrations. A 21-amino-acids-long region of the ApHal2 enzyme was inserted into the Arabidopsis thaliana homologue of Hal2, the SAL1 phosphatase. The inserted sequence included the META motif, which has previously been implicated in increased sodium tolerance of the Hal2 homologue from a related fungal species. Transgenic Arabidopsis plants overexpressing this modified SAL1 (mSAL1) showed improved halotolerance and drought tolerance. In a medium with an elevated salt concentration, mSAL1-expressing plants were twice as likely to have roots in a higher length category in comparison with the wild-type Arabidopsis and with plants overexpressing the native SAL1, and had 5% to 10% larger leaf surface area under moderate and severe salt stress, respectively. Similarly, after moderate drought exposure, the mSAL1-expressing plants showed 14% increased dry weight after revitalisation, with no increase in dry weight of the wild-type plants. With severe drought, plants overexpressing native SAL1 had the worst rehydration success, consistent with the recently proposed role of SAL1 in severe drought. This was not observed for plants expressing mSAL1. Therefore, the presence of this fungal META motif sequence is beneficial under conditions of increased salinity and moderate drought, and shows no drawbacks for plant survival under severe drought. This demonstrates that adaptations of extremotolerant fungi should be considered as a valuable resource for improving stress-tolerance in plant breeding in the future.
Identification and Compensation of Piezoelectric Hysteresis Without Modeling Hysteresis Inverse

This paper presents a new approach for hysteresis identification and compensation of piezoelectric actuators by resorting to an intelligent hysteresis model. In particular, a least squares support vector machine (LSSVM)-based hysteresis model is developed and used for both purposes of hysteresis identification and hysteresis compensation. By this way, the hysteresis inverse is not needed in the feedforward hysteresis compensator since the hysteresis model is directly used. To establish the LSSVM model, the problem of how to select input variables to convert the multivalued mapping into a single-valued one is addressed. The effectiveness of the presented idea is validated by a series of experimental studies on a piezoactuated system. Results show that the proposed approach is superior to the Bouc–Wen-model-based one in terms of both hysteresis modeling and compensation. The reported method is more computational effective than existing model-based hysteresis compensation approaches, and it is extensible to other smart actuator systems as well.
Affine Invariant Shape Description Using the Triangular Kernel

We present in this report a novel approach for shape description based on kernel principal component analysis (KPCA). The strength of this method resides in the affine and particularly scale invariance of KPCA when using a particular kernel referred to as the triangular kernel. Beside this affine invariance, the method provides an effective way to capture the non-linearities in the shape geometry. Experiments conducted on the SQUID database show this affine invariance and the good performances for shape matching and retrieval.
A Framework for Analysis and Comparison of Dynamic Malware Analysis Tools

Malware writers have employed various obfuscation and polymorphism techniques to thwart static analysis approaches and bypassing antivirus tools. Dynamic analysis techniques, however, have essentially overcome these deceits by observing the actual behaviour of the code execution. In this regard, various methods, techniques and tools have been proposed. However, because of the diverse concepts and strategies used in the implementation of these methods and tools, security researchers and malware analysts find it difficult to select the required optimum tool to investigate the behaviour of a malware and to contain the associated risk for their study. Focusing on two dynamic analysis techniques: Function Call monitoring and Information Flow Tracking, this paper presents a comparison framework for dynamic malware analysis tools. The framework will assist the researchers and analysts to recognize the tools implementation strategy, analysis approach, system wide analysis support and its overall handling of binaries, helping them to select a suitable and effective one for their study and analysis.
Innovative Financial Services for Rural India Monsoon-Indexed Lending and Insurance for Smallholders

Agriculture & Rural Development Working Papers is an informal series produced by the Agriculture & Rural Development Department of the World Bank. These papers raise concepts and issues for discussion in the broader development community and describe ongoing research and/or implementation experiences from the Bank. The findings, interpretations, and conclusions are the author's own and should not be attributed to the World Bank, its management, its Board of Executive Directors, or the countries they represent. Some of the numbers quoted are estimates or approximations, and may be revised at a later stage. Ulrich Hess is an economist in the Agricultural and Rural Development department of the World Bank. India and Kairas Vakharia of Mahindra Shubhlabh in India for their vision and insights that ultimately motivated this paper. Many thanks to Melissa Williams who edited this volume and coordinated production and publication.
A Longitudinal Study Of Maternal Style, Young Adult Temperament And Cognition, And Program Outcome In Guide Dogs

How does maternal style, experienced over the first few weeks of life, affect later outcomes? Equally important, what is the role of an adolescent’s temperament and cognitive skills? The quest to understand which factors early in development lead to positive life outcomes is an endeavor that transcends species boundaries. In this dissertation, I explore the nature of these relationships using data collected from birth to adolescence in a cohort of prospective guide dogs. In Study 1, I quantify the behavior of mothers (n = 21) toward their litters. The results revealed that canine maternal style can be summarized in one principal component that explained a significant proportion of the variation and was stable across weeks, variable across individuals, and related to maternal cortisol and experimental measures. In Study 2, I examine the influence of early maternal style on later behavior, as well as on success in the guide dog program up to two years later. I also evaluated the influence of young adult temperament and cognition on success. Measures of maternal style as well as adolescent temperament and cognition were significantly associated with outcome in the guide dog program, even when controlling for each other. Successful dogs had less involved mothers as puppies, and demonstrated superior problem-solving skills, lower levels of perseveration, and reduced anxious vocal behavior as young adults. Temperament and cognition are frequently assessed in tasks purporting to measure one or the other, but large-scale studies usually only include tasks assigned to either domain. Dogs in our study completed a battery of both temperament and cognitive tasks. Thus, in Study 3, I address the categorization of ‘temperament’ and ‘cognitive’ tasks using both confirmatory and exploratory analyses and validate the findings using subjective ratings from puppy raisers, salivary cortisol, and program outcome measures. Forcing tasks into groups defined by cognition or temperament led to poor results, whereas a bottom-up approach revealed that putative cognitive and temperament measures interact in unanticipated ways. Taken together, these results suggest that mothering and the not-so-straightforward interplay of temperament and cognition provide important clues to the future success of an animal. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Psychology First Advisor Robert M. Seyfarth
On the Asymptotic Geometric Mean of MIMO Channel Eigenvalues

The geometric mean of the eigenvalues of multiple-input multiple-output (MIMO) channels is a parameter occurring in a variety of instances, including MIMO transceiver design based on the so-called geometric mean decomposition (GMD), and MIMO mutual information at high SNR. In this paper, we derive the asymptotic geometric mean of MIMO channel eigenvalues in the case where the entries of the channel matrix are independent and identically distributed. We demonstrate that the result can be easily extended to channels with an exponential correlation model. As an application of the result, we provide an analytical expression for the asymptotic system capacity of GMD-based transceivers
Embedded finite models, stability theory, and the impact of order

We extend bounds on the expressive power of first-order logic over finite structures and over ordered finite structures, by generalizing to the situation where the finite structures are embedded in an infinite structure M, where M satisfies some simple combinatorial properties studied in model-theoretic stability theory. We first consider first-order logic over finite structures embedded in a stable structure, and show that it has the same generic expressive power as first-order logic on unordered finite structures. It follows from this that having the additional structure of, for example, an abelian group or an equivalence relation, does not allow one to define any new generic queries. We also consider first-order logic over finite structures living within any model M that lacks the independence property and show that its expressive power is bounded by first-order logic over finite ordered structures. This latter result gives an enormous class of structures in which the expressive power of first-order logic is sharply limited; it shows that common queries such as parity and connectivity cannot be defined for finite structures living within structures from this huge class. It also gives a pure combinatorial property of an interpreted structure M that is sufficient to extend results on first-order logic on ordered structures to first-order logic on finite structures embedded in M.
Partitioning into Expanders

Let G = (V, E) be an undirected graph, λk be the kth smallest eigenvalue of the normalized laplacian matrix of G. There is a basic fact in algebraic graph theory that λk > 0 if and only if G has at most k -- 1 connected components. We prove a robust version of this fact. If λk > 0, then for some 1 ≤ e ≤ k -- 1, V can be partitioned into e sets P1, . , Pe such that each Pi is a low-conductance set in G and induces a high conductance induced subgraph. In particular, [EQUATION] and [EQUATION]-expander. We make our results algorithmic by designing a simple polynomial time spectral algorithm to find such partitioning of G with a quadratic loss in the inside conductance of Pi's. Unlike the recent results on higher order Cheeger's inequality [6, 9], our results does not use higher order eigenfunctions of G. If there is a sufficiently large gap between λk and λk+1, more precisely if [EQUATION] then our algorithm finds a k partitioning of V into sets P1, . , Pk such that the induced subgraph G[Pi] has a singnificantly larger conductance than the conductance of Pi in G. Such a partitioning may represent the best k clusterings of G. Our algorithm is a simple local search that only uses the Spectral Partitioning algorithm as a subroutine. We expect to see further applications of this simple algorithm in clustering applications. Let ρ(k) = [EQUATION] be the order k conductance constant of G, in words, ρ(k) is the smallest value of the maximum conductance of any k disjoint subsets of V. Our main technical lemma shows that if (1+e)ρ(k) < ρ(k+1), then V can be partitioned into k sets P1, . , Pk such that for each 1 ≤ i ≤ k, φ(G[Pi]) ≳ e·ρ(k+1)/k and φ((Pi) ≤ k · ρ(k). This significantly improves a recent result of Tanaka [13] who assumed an exponential (in k) gap between ρ(k) and ρ(k + 1).
Smart resource-aware multimedia sensor network for automatic detection of complex events

This paper presents a smart resource-aware multimedia sensor network. We illustrate a surveillance system which supports human operators, by automatically detecting the complex events and giving the possibility to recall the detected events and searching them in an intelligent search engine. Four subsystems have been implemented, the tracking and detection system, the network configuration system, the reasoning system and an advanced archiving system in an annotated multimedia database.
Chitosan-Functionalized Graphene Oxide as a Potential Immunoadjuvant

The application of graphene oxide (GO) as a potential vaccine adjuvant has recently attracted considerable attention. However, appropriate surface functionalization of GO is crucial to improve its biocompatibility and enhance its adjuvant activity. In this study, we developed a simple method to prepare chitosan (CS)-functionalized GO (GO-CS) and further investigated its potential as a nanoadjuvant. Compared with GO, GO-CS possessed considerably smaller size, positive surface charge, and better thermal stability. The functionalization of GO with CS was effective in decreasing the non-specific protein adsorption and improving its biocompatibility. Furthermore, GO-CS significantly activated RAW264.7 cells and stimulated more cytokines for mediating cellular immune response, which was mainly due to the synergistic immunostimulatory effect of both GO and CS. GO-CS exhibits strong potential as a safe nanoadjuvant for vaccines and immunotherapy.
AMCTD: Adaptive Mobility of Courier Nodes in Threshold-Optimized DBR Protocol for Underwater Wireless Sensor Networks

In dense underwater sensor networks (UWSN), the major confronts are high error probability, incessant variation in topology of sensor nodes, and much energy consumption for data transmission. However, there are some remarkable applications of UWSN such as management of seabed and oil reservoirs, exploration of deep sea situation and prevention of aqueous disasters. In order to accomplish these applications, ignorance of the limitations of acoustic communications such as high delay and low bandwidth is not feasible. In this paper, we propose Adaptive mobility of Courier nodes in Threshold-optimized Depth-based routing (AMCTD), exploring the proficient amendments in depth threshold and implementing the optimal weight function to achieve longer network lifetime. We segregate our scheme in 3 major phases of weight updating, depth threshold variation and adaptive mobility of courier nodes. During data forwarding, we provide the framework for alterations in threshold to cope with the sparse condition of network. We ultimately perform detailed simulations to scrutinize the performance of our proposed scheme and its comparison with other two notable routing protocols in term of network lifetime and other essential parameters. The simulations results verify that our scheme performs better than the other techniques and near to optimal in the field of UWSN.
UNFOLD: uniform fast on-line boundary detection for dynamic 3D wireless sensor networks

A wireless sensor network becomes dynamic if it is monitoring a time-variant event (e.g., expansion of oil spill in ocean). In such applications, on-line boundary detection is a crucial function, as it allows us to track the event variation in a timely fashion. However, the problem becomes very challenging as it demands a highly efficient algorithm to cope with the dynamics introduced by the evolving event. Moreover, as many physical events occupy volumes rather than surfaces (e.g., oil spill again), the algorithm has to work for 3D cases. To this end, we propose UNiform Fast On-Line boundary Detection (UNFOLD) to tackle the challenge. The essence of UNFOLD is to inverse node coordinates such that a "notched" surface is "unfolded" into a convex one, which in turn reduces boundary detection to simple convexity test. UNFOLD is uniform as every node behaves the same (performing coordinate inversion and convexity test), and it is super fast as both computation and communication involve only one-hop neighbors. We prove the correctness and efficiency of UNFOLD; we also use simulations and implementations to evaluate its performance, which demonstrates that UNFOLD is 100 times more time and energy efficient than the most up-to-date proposal.
Future research challenges in wireless sensor and actuator networks targeting industrial automation

A growing trend in the automation industry is to use wireless technologies to reduce cable cost, deployment time, unlocking of stranded information in previously deployed devices, and enabling wireless control applications. Despite a huge research effort in the area of wireless sensor networks (WSNs), there are several issues that have not been addressed properly such that WSNs can be adopted properly in the process automation domain. This article presents the major requirements for typical applications in process automation and we also aim to outline the research direction for industrial wireless sensor networks (IWSNs) in industrial automation. The major issues that need to be addressed are safety, security and availability before industrial wireless sensor networks will be adopted in full scale in process automation.
Utilizing deep learning for content-based community detection

Online social networks have been wildly spread in recent years. They enable users to identify other users with common interests, exchange their opinions, and expertise. Discovering user communities from social networks have become one of the major challenges which help its members to interact with relevant people who have similar interests. Community detection approaches fall into two categories: the first one considers user' networks while the other utilizes usergenerated content. In this paper, a multi-layer community detection model based on identifying topics of interest from user published content is presented. This model applies Gaussian Restricted Boltzmann Machine for modeling user's posts within a social network which yields to identify their topics of interest, and finally construct communities. The effectiveness of the proposed multi-layer model is measured using KL divergence which measures similarity between users of the same community. Experiments on the real Twitter dataset show that the proposed deep model outperforms traditional community detection models that directly maps users into corresponding communities using several baseline techniques.
Takeover Times of Noisy Non-Generational Selection Rules that Undo Extinction

The takeover time of some selection method is the expected number of iterations of this selection method until the entire population consists of copies of the best individual under the assumption that the initial population consists of a single copy of the best individual. We consider a class of nongenerational selection rules that run the risk of loosing all copies of the best individual with positive probability. Since the notion of a takeover time is meaningless in this case these selection rules are modi ed in that they undo the last selection operation if the best individual gets extinct from the population. We derive exact results or upper bounds for the takeover time for three commonly used selection rules via a random walk or Markov chain model. The takeover time for each of these three selection rules is O(n logn) with population size n.