Miracle-dz/UniFAR-FTUs huggingface dataset:
A new codebook training algorithm for VQ-based speaker recognition

VQ-based speaker recognition has proven to be a successful method. Usually, a codebook is trained to minimize the quantization error for the data from an individual speaker. The codebooks trained based on this criterion have weak discriminative power when used as a classifier. The LVQ algorithm can be used to globally train the VQ-based classifier. However, the correlation between the feature vectors is not taken into consideration, in consequence, a high classification rate for feature vectors does not lead to a high classification rate for the test sentences. A heuristic training procedure is proposed to retrain the codebooks so that they give a lower classification error rate for randomly selected vector-groups. Evaluation experiments demonstrated that the codebooks trained with this method provide much higher recognition rates than that trained with the LBG algorithm alone, and often they can outperform the more powerful Gaussian mixture speaker models.
Birth Order and Child Outcomes: Does Maternal Quality Time Matter?

Higher birth order positions are often associated with poorer outcomes, possibly due to fewer resources received within the household. Using a sample of PSID-CDS children, we investigate whether the birth order effects in their outcomes are due to unequal allocation of the particular resource represented by maternal quality time. OLS regressions show that the negative birth order effects on various test scores are only slightly diminished when maternal time is included among the regressors. This result is confirmed when we account for unobserved heterogeneity at the household level, exploiting the presence of siblings in the data. Our evidence therefore suggests that birth order effects are not due to differences in maternal quality time received.
Multi-Objective Design Exploration for Aerodynamic Configurations

A new approach, Multi-Objective Design Exploration (MODE), is presented to address Multidisciplinary Design Optimization problems. MODE reveals the structure of the design space from the trade-off information and visualizes it as a panorama for Decision Maker. The present form of MODE consists of Kriging Model, Adaptive Range Multi Objective Genetic Algorithms, Analysis of Variance and Self-Organizing Map. The main emphasis of this approach is visual data mining. Two data mining examples using high fidelity simulation codes are presented: four-objective aerodynamic optimization for the fly-back booster and Multidisciplinary Design Optimization problem for a regional-jet wing. The first example confirms that two different data mining techniques produce consistent results. The second example illustrates the importance of the present approach because design knowledge can produce a better design even from the brief exploration of the design space.
Does this list contain what you were searching for? Learning adaptive dialogue strategies for interactive question answering

Abstract Policy learning is an active topic in dialogue systems research, but it has not been explored in relation to interactive question answering (IQA). We take a first step in learning adaptive interaction policies for question answering : we address the question of how to acquire enough reliable query constraints, how many database results to present to the user and when to present them, given the competing trade-offs between the length of the answer list, the length of the interaction, the type of database and the noise in the communication channel. The operating conditions are reflected in an objective function which we use to derive a hand-coded threshold-based policy and rewards to train a reinforcement learning policy. The same objective function is used for evaluation. We show that we can learn strategies for this complex trade-off problem which perform significantly better than a variety of hand-coded policies, for a wide range of noise conditions, user types, types of DB and turn-penalties. Our policy learning framework thus covers a wide spectrum of operating conditions. The learned policies produce an average relative increase in reward of 86.78% over the hand-coded policies. In 93% of the cases the learned policies perform significantly better than the hand-coded ones (p < .001). Furthermore we show that the type of database has a significant effect on learning and we give qualitative descriptions of the learned IQA policies.
If at First You Do Succeed, Do You Try, Try Again? Developing the Persistence–Licensing Response Measure to Understand, Predict, and Modify Behavior Following Subgoal Success

Following subgoal success at a long-term goal, consumers can persist, behaving consistently with the long-term goal, or license, behaving consistently with competing shorter-term goals. The authors extend prior work by proposing that this choice is driven, in part, by chronic differences in consumers’ cognitive and emotional responses to subgoal success. In Studies 1–5, they develop and validate a measure that captures these individual differences: the Persistence–Licensing Response Measure (PLRM). They demonstrate that the PLRM predicts persistence and licensing where existing constructs and measures do not, showing that consumers’ responses to subgoal success represent a unique dimension of self-regulation. In Studies 6–10, the authors demonstrate that the PLRM moderates the effect of subgoal success on persistence and licensing behavior, revealing that the same subgoal success situation can lead to systematically different behaviors. The authors also examine how marketing interventions can be used to increase consumers’ persistence following subgoal success. This work furthers the understanding of the determinants of persistence and licensing, improves prediction of behavior, and offers marketers tools to segment and target consumers, increasing persistence in key goal domains.
Effects of Symbolic and Nonsymbolic Equal-Sign Intervention in Second-Grade Classrooms

The purposes of this study were (a) to test the efficacy of whole-class equal-sign instruction, (b) to contrast the effects of symbolic and nonsymbolic instructional representation, (c) to determine whether visual representation compensated for language deficits, and (d) to evaluate maintenance of intervention. We randomly assigned 195 children from 21 second-grade classrooms to 3 conditions: symbolic intervention, nonsymbolic intervention, and business-as-usual control. Both active intervention conditions consisted of three 20-minute instructional lessons. The only distinction between the intervention conditions was problem representation (symbolic vs. nonsymbolic); verbal instruction remained consistent. Children in both instructional conditions outperformed children in the control condition on all outcome measures. Language ability moderated the symbolic intervention relative to control and nonsymbolic intervention, suggesting that nonsymbolic representation may compensate for language deficits.
Predators , Prey and Volatility on Wall Street ∗

Much of the activity in sectors of financial services such as sales and trading or the hedge-fund industry is zero-sum in nature. Along with providing transactional and diversification services, participants in this industry also prey upon each other. High-ability traders (predators) capture surplus at the expense of less able and experienced traders (prey). We show that in a dynamic model of such an industry these features amplify real shocks. In the model, traders are randomly matched each period. The presence of more low-ability traders protects others from predation by high-ability traders, leading to equilibrium at a high level of employment. Shocks to aggregate profits may trigger exit by those lower ability, which in turn exposes those of intermediate ability to greater risk of predation and motivating their exit. This leads to more dramatic contractions in financial activity. Depending on parameter values, the industry may remain stuck at the low employment level. In other cases it may, once profits recover, begin rapid growth towards the high employment level until this growth is interrupted by another bad shock. Thus, our relatively simple model generates boom-bust cycles reminiscent of Wall Street employment.
Approximate Nearest Neighbor And Its Many Variants by Sepideh Mahabadi

This thesis investigates two variants of the approximate nearest neighbor problem. First, motivated by the recent research on diversity-aware search, we investigate the k-diverse near neighbor reporting problem. The problem is defined as follows: given a query point q, report the maximum diversity set S of k points in the ball of radius r around q. The diversity of a set S is measured by the minimum distance between any pair of points in S (the higher, the better). We present two approximation algorithms for the case where the points live in a d-dimensional Hamming space. Our algorithms guarantee query times that are sub-linear in n and only polynomial in the diversity parameter k, as well as the dimension d. For low values of k, our algorithms achieve sub-linear query times even if the number of points within distance r from a query q is linear in n. To the best of our knowledge, these are the first known algorithms of this type that offer provable guarantees. In the other variant, we consider the approximate line near neighbor (LNN) problem. Here, the database consists of a set of lines instead of points but the query is still a point. Let L be a set of n lines in the d dimensional euclidean space Rd. The goal is to preprocess the set of lines so that we can answer the Line Near Neighbor (LNN) queries in sub-linear time. That is, given the query point q E Rd, we want to report a line f E L (if there is any), such that dist(q, f) < r for some threshold value r, where dist(q, f) is the euclidean distance between them. We start by illustrating the solution to the problem in the case where there are only two lines in the database and present a data structure in this case. Then we show a recursive algorithm that merges these data structures and solve the problem for the general case of n lines. The algorithm has polynomial space and performs only a logarithmic number of calls to the approximate nearest neighbor subproblem. Thesis Supervisor: Piotr Indyk Title: Professor
Modeling Property Intercorrelations in Conceptual Memory

Behavioral experiments have demonstrated that people encode knowledge of correlations among semantic properties of entities and that this knowledge influences performance on semantic tasks (McRae, 1992; McRae, de Sa, & Seidenberg, 1993). Independently, in connectionist theory, it has been claimed that relationships among semantic properties may provide structure that is required for the relatively arbitrary mapping from word form to word meaning (Hinton & Shallice, 1991). We explored these issues by implementing a modified Hopfield network (1982, 1984) to simulate the computation from word form to meaning. The model was used as a vehicle for developing explanations for the role played by correlated properties in determining short interval semantic priming effects and in determining the ease with which a property is verified as part of a concept. Simulations of the priming and property verification experiments of McRae (1992) are reported. It is concluded that correlations among properties encoded in conceptual memory play a key role in the dynamics of the computation of word meaning. Furthermore, a model in which property intercorrelations are central to forming basins of attraction corresponding to concepts may provide important insights into lexical memory.
Realistic magnetohydrodynamical simulation of solar local supergranulation

Three-dimensional numerical simulations of solar surface magnetoconvection using realistic model physics are conducted. The thermal structure of convective motions into the upper radiative layers of the photosphere, the main scales of convective cells and the penetration depths of convection are investigated. We take part of the solar photosphere with a size of 60×60 Mm2 in the horizontal direction and of depth 20 Mm from the level of the visible solar surface. We use a realistic initial model of the sun and apply the equation of state and opacities of stellar matter. The equations of fully compressible radiation magnetohydrodynamics (MHD) with dynamical viscosity and gravity are solved. We apply (i) the conservative total variation diminishing (TVD) difference scheme for MHD, (ii) the diffusion approximation for radiative transfer and (iii) dynamical viscosity from subgrid-scale modeling. In simulation, we take a uniform two-dimensional grid in the horizontal plane and a nonuniform grid in the vertical direction with the number of cells being 600×600×204. We use 512 processors with distributed memory multiprocessors on the supercomputer MVS-100k at the Joint Computational Centre of the Russian Academy of Sciences.
Structural and Dynamic Analysis of Foursquare Network

The soaring location-based social services not only make it possible to take advantage of one additional source of information: the places people visit, but also present graph structure consisting of individuals and places in certain relationship. In this paper we investigated the interactions between venues in Foursquare network. Provided with the check-in number for each venue as well as the transitions between them, we studied the basic characteristics of the network, detected and analyzed community structures, and modeled the graph evolution with various techniques. Our work has strong application uses and potential business values, such as using community structures to recommend venues for users based on tastes, and to predict future popularity of venues, which can be essential for business owners.
Combining Mobile, Tangible and Virtual World Platforms to Support Participatory Campus Planning

Urban planning, and campus planning in particular, can benefit greatly from technology that fosters and leverages collaborative, participatory planning which includes all affected stakeholders. Interactive surfaces such as tabletops and walls, and spaces such as 3D environments are examples of such technologies. Our research aims to explore and assess the appropriateness and effectiveness of such technologies in participatory campus planning. We present here a prototype system that combines the tangible and multitouch surfaces with a virtual world platform to provide a participatory planning approach for campus development projects.
Prediction of aggregation rate and aggregation‐prone segments in polypeptide sequences

The reliable identification of β‐aggregating stretches in protein sequences is essential for the development of therapeutic agents for Alzheimer's and Parkinson's diseases, as well as other pathological conditions associated with protein deposition. Here, a model based on physicochemical properties and computational design of β‐aggregating peptide sequences is shown to be able to predict the aggregation rate over a large set of natural polypeptide sequences. Furthermore, the model identifies aggregation‐prone fragments within proteins and predicts the parallel or anti‐parallel β‐sheet organization in fibrils. The model recognizes different β‐aggregating segments in mammalian and nonmammalian prion proteins, providing insights into the species barrier for the transmission of the prion disease.
A multi-task learning strategy for unsupervised clustering via explicitly separating the commonality

In this paper, we propose an unsupervised cluster method via a multi-task learning strategy, called Mt-Cluster. Our MtCluster learns a cluster-specific dictionary for each cluster to represent its sample signals and a shared common pattern pool (the commonality) for the essentially complemental representation. By treating learning the cluster-specific dictionary as a single task, MtCluster works in a multi-task learning manner, in which all the tasks are connected by simultaneously learning the commonality. Actually, the learned cluster-specific dictionary spans the feature space of the corresponding cluster, and the commonality is just used for necessary complemental representation. To evaluate our method, we perform several experiments on public available datasets, and the promising results demonstrate the effectiveness of MtCluster.
Improving statistical machine translation by paraphrasing the training data.

Large amounts of training data are essential for training statistical machine translations systems. In this paper we show how training data can be expanded by paraphrasing one side. The new data is made by parsing then generating using a precise HPSG based grammar, which gives sentences with the same meaning, but minor variations in lexical choice and word order. In experiments with Japanese and English, we showed consistent gains on the Tanaka Corpus with less consistent improvement on the IWSLT 2005 evaluation data.
Blind super-resolution using sparse line gradient prior for passive millimeter wave image

Aiming at the low resolution problem of Passive Millimeter Wave Image, a blind super-resolution algorithm based on sparse line gradient (SLG) prior is proposed. Firstly, the line gradient is defined to modeling the specialized prior, which is in line with the characteristics of PMMW image than custom gradient. Then the super-resolution problem under MAP framework is optimized in an alternative iteration progress. Experimental results demonstrate that for the millimeter wave image blind super-resolution, the proposed algorithm is superior to other traditional method.
MARY TTS participation in the Blizzard Challenge 2007

This paper describes the second participation of the open source MARY TTS unit selection system in a Blizzard challenge. Compared to last year’s system, a number of welldefined changes have been made to the algorithm, concerning unit definition, prosody models, and signal modification. The results in this year’s challenge are considerably improved, confirming that the changes were worthwhile. The paper also reports on an approach to the selection of a subset of the utterances provided, in order to build a voice with good coverage not larger than the pre-defined “Arctic” subset. Results show that this small voice is perceived slightly better than the voice we built from the Arctic subset.
WaterlooClarke: TREC 2015 Clinical Decision Support Track

Abstract : Clinical decision support systems help physicians with finding additional information about a particular medical case. In this paper, we develop a clinical decision support system that, based on a short medical case description, can recommend research articles to answer some common medical questions (diagnosis, test and treatment articles). The two different full-text search engines we adopted in order to search over the collection of articles are Terrier and Apache Solr. We test each search engine with different settings and retrieval algorithms. Additionally, we combine the results of the two different search engines using reciprocal rank fusion. The evaluation of the submitted runs using partially marked results of Text Retrieval Conference (TREC) from the previous year shows that the methodologies are promising.
N-Acetylglucosamine Induces White to Opaque Switching, a Mating Prerequisite in Candida albicans

To mate, the fungal pathogen Candida albicans must undergo homozygosis at the mating-type locus and then switch from the white to opaque phenotype. Paradoxically, opaque cells were found to be unstable at physiological temperature, suggesting that mating had little chance of occurring in the host, the main niche of C. albicans. Recently, however, it was demonstrated that high levels of CO2, equivalent to those found in the host gastrointestinal tract and select tissues, induced the white to opaque switch at physiological temperature, providing a possible resolution to the paradox. Here, we demonstrate that a second signal, N-acetylglucosamine (GlcNAc), a monosaccharide produced primarily by gastrointestinal tract bacteria, also serves as a potent inducer of white to opaque switching and functions primarily through the Ras1/cAMP pathway and phosphorylated Wor1, the gene product of the master switch locus. Our results therefore suggest that signals produced by bacterial co-members of the gastrointestinal tract microbiota regulate switching and therefore mating of C. albicans.
Lightest sterile neutrino abundance within the nuMSM

We determine the abundance of the lightest (dark matter) sterile neutrinos created in the Early Universe due to active-sterile neutrino transitions from the thermal plasma. Our starting point is the field-theoretic formula for the sterile neutrino production rate, derived in our previous work [JHEP 06(2006)053], which allows to systematically incorporate all relevant effects, and also to analyse various hadronic uncertainties. Our numerical results differ moderately from previous computations in the literature, and lead to an absolute upper bound on the mixing angles of the dark matter sterile neutrino. Comparing this bound with existing astrophysical X-ray constraints, we find that the Dodelson-Widrow scenario, which proposes sterile neutrinos generated by active-sterile neutrino transitions to be the sole source of dark matter, is only possible for sterile neutrino masses lighter than 3.5 keV (6 keV if all hadronic uncertainties are pushed in one direction and the most stringent X-ray bounds are relaxed by a factor of two). This upper bound may conflict with a lower bound from structure formation, but a definitive conclusion necessitates numerical simulations with the non-equilibrium momentum distribution function that we derive. If other production mechanisms are also operative, no upper bound on the sterile neutrino mass can be established.
Maternal Dietary Patterns and Gestational Diabetes Mellitus in a Multi-Ethnic Asian Cohort: The GUSTO Study

Gestational Diabetes Mellitus (GDM) is associated with an increased risk of perinatal morbidity and long term health issues for both the mother and offspring. Previous research has demonstrated associations between maternal diet and GDM development, but evidence in Asian populations is limited. The objective of our study was to examine the cross-sectional relationship between maternal dietary patterns during pregnancy and the risk of GDM in a multi-ethnic Asian cohort. Maternal diet was ascertained using 24-h dietary recalls from participants in the Growing up in Singapore towards healthy outcomes (GUSTO) study—a prospective mother-offspring cohort, and GDM was diagnosed according to 1999 World Health Organisation guidelines. Dietary patterns were identified using factor analysis, and multivariate regression analyses performed to assess the association with GDM. Of 909 participants, 17.6% were diagnosed with GDM. Three dietary patterns were identified: a vegetable-fruit-rice-based-diet, a seafood-noodle-based-diet and a pasta-cheese-processed-meat-diet. After adjusting for confounding variables, the seafood-noodle-based-diet was associated with a lower likelihood of GDM (Odds Ratio (95% Confidence Interval)) = 0.74 (0.59, 0.93). The dietary pattern found to be associated with GDM in our study was substantially different to those reported previously in Western populations.