Miracle-dz/UniFAR-FTUs huggingface dataset:
An interactive framework for learning user-object associations through human-robot interaction

A great deal of recent research has focused on social and assistive robots that can achieve a more natural and realistic interaction between the agent and its environment. Following this direction, this paper aims to establish a computational framework that can associate objects with their uses and their basic characteristics in an automated manner. The goal is to continually enrich the robot's knowledge regarding objects that are important to the user, through verbal interaction. We address the problem of learning correlations between object properties and human needs by associating visual with verbal information. Although the visual information can be acquired directly by the robot, the verbal information is acquired via interaction with a human user. Users provide descriptions of the objects for which the robot has captured visual information, and these two sources of information are combined automatically. We present a general model for learning these associations using Gaussian Mixture Models. Since learning is based on a probabilistic model, the approach handles uncertainty, redundancy, and irrelevant information. We illustrate the capabilities of our approach by presenting the results of an initial experiment run in a laboratory environment, and we describe the set of modules that support the proposed framework.
Role of cis-Acting Sites NorL, a TATA Box, and AflR1 in nor-1 Transcriptional Activation in Aspergillus parasiticus

ABSTRACT The transcription factor AflR is required for up-regulation of specific pathway genes involved in aflatoxin biosynthesis in the filamentous fungus Aspergillus. nor-1 encodes an early aflatoxin pathway enzyme; its promoter contains a consensus AflR binding site (AflR1). Proteins in Aspergillus parasiticus cell extracts and AflR expressed in Escherichia coli do not bind to A. parasiticus AflR1 in vitro, so it was not clear if this site was required for nor-1 expression or if other transcription factors contributed to gene regulation. In this study we defined the role of AflR1 in nor-1 expression in A. parasiticus and identified additional cis-acting sites required for maximum nor-1 transcriptional activation. Deletion and substitution of AflR1 in the nor-1 promoter in A. parasiticus nor-1::GUS reporter strains showed that this site is required for nor-1 transcriptional activation in vivo. Substitution of a putative TATA box in the nor-1 promoter resulted in nondetectable β-glucuronidase (GUS) activity, demonstrating that this TATA box is functional in vivo. We also identified a novel cis-acting site, designated NorL, between residues −210 and −238 that was required for maximum nor-1 transcriptional activation in A. parasiticus grown in liquid medium and on solid medium. Using an electrophoretic mobility shift assay, we identified a specific NorL-dependent DNA-protein complex that relies on a functional AflR, either directly or indirectly, for maximum binding capacity. Because the NorL site appears only once in the aflatoxin gene cluster, its association with the nor-1 promoter may have important implications for the overall regulatory scheme for the aflatoxin pathway.
GENOMICS . TRANSCRIPTOMICS . PROTEOMICS Analysis of Mitochondrial DNA Lineages in Yakuts

To study the mitochondrial gene pool structure in Yakuts, polymorphism of mtDNA hypervariable segment I (16,024–16,390) was analyzed in 191 people sampled from the indigenous population of the Sakha Republic. In total, 67 haplotypes of 14 haplogroups were detected. Most (91.6%) haplotypes belonged to haplogroups A, B, C, D, F, G, M*, and Y, which are specific for East Eurasian ethnic groups; 8.4% haplotypes represented Caucasian haplogroups H, HV1, J, T, U, and W. A high frequency of mtDNA types belonging to Asian supercluster M was peculiar for Yakuts: mtDNA types belonging to haplogroup C, D, or G and undifferentiated mtDNA types of haplogroup M (M*) accounted for 81% of all haplotypes. The highest diversity was observed for haplogroups C and D, which comprised respectively 22 (44%) and 18 (30%) haplotypes. Yakuts showed the lowest genetic diversity ( H = 0.964) among all Turkic ethnic groups. Phylogenetic analysis testified to common genetic substrate of Yakuts, Mongols, and Central Asian (Kazakh, Kyrgyz, Uighur) populations. Yakuts proved to share 21 (55.5%) mtDNA haplotypes with the Central Asian ethnic groups and Mongols. Comparisons with modern Paleoasian populations (Chukcha, Itelmen, Koryaks) revealed three (8.9%) haplotypes common for Yakuts and Koryaks. The results of mtDNA analysis disagree with the hypothesis of an appreciable Paleoasian contribution to the modern Yakut gene pool.
Building Ensembles of Classi ers for Loss Minimization

One of the most active areas of research in supervised learning has been the study of methods for constructing good ensembles of classiiers, that is, a set of classi-ers whose individual decisions are combined to increase overall accuracy of classifying new examples. In many applications classiiers are required to minimize an asym-metric loss function rather than the raw misclassiication rate. In this paper, we present approaches to modifying existing methods for constructing ensembles to incorporate arbitrary loss functions. We compare the performance of the new algorithms with traditional ensemble learners, MetaCost-a novel method for cost-sensitive learning, and single decision tree classiiers. We evaluated the algorithms on multi-class data sets from the UC Irvine ML Repository.
Optimal Dyadic Modeling ( ODM ) Algorithm for Fast Computation of Tomographic Images

In this paper we apply the optimal dyadic modeling method for efficient computation of tomographic images, in view of the fact that the fast increase of sizes of tomographic data, reduction of computation demands of the reconstruction algorithms is of great importance. The present widely accepted algorithm for tomographic image reconstruction is the filtered-backprojection (FBP) algorithm using Fourier transform. Since Walsh transform is the fastest unitary transform known so far, we explored the implementation of the FBP algorithm in the Walsh domain. We applied the optimal dyadic modeling method for generating the Walsh domain filter coefficients based on the observation that the matrices characterizing the linear time invariant (LTI) system and its optimal dyadic invariant (LDI) approximant have identical diagonal elements in the Walsh domain. This fact is used to derive a direct transformation between the impulse-response function of an LTI system and that of its dyadic approximant. The proposed algorithm has been implemented and tested for its performance using the well-known ‘Shepp-Logan head phantom’ test image. The results demonstrate that the reconstruction strategy has comparable performance with a significant reduction of computing time. For example, with a 128×128-pixel image and 180 views, the speedup achieved is seven fold, with a slight increase in root mean square error (RMSE) in reconstructed image.
A gentle introduction to… INFORMATION CONTENT IN TRANSCRIPTION FACTOR BINDING SITES

but not scale) as the assumption of our ability of directly observing without noise the result of a die roll by visual inspection. Binding sites for proteins are inferred by experimental assays that are typically carried out in triplicate (at least), as a way to minimize the noise inherent to the experimental procedure. Different experimental procedures (and the controls used therein) will provide different degrees of certainty with regards to protein binding to a specific site.
Modeling the Implied Volatility Smile-The Sticky-Delta Smile Approximation

This study develops the sticky-delta rule presented by Derman (1999) into a model that is believed to be reasonably efficient for option pricing with a smile. The model enables pricing with respect to different strikes and maturities where the observed implied volatility is not constant over strikes. The smile in implied volatility, using the stickydelta rule, is a function of the delta of a particular strike. This implies that the volatility is stuck to the delta of that particular strike. The sticky-delta rule is here developed into a model that is used for approximating the volatility smile on stock indices on three European markets. It is suggested that the shape of the smile is only dependent on the call delta and thereby on time and volatility and moves with the underlying, leaving atthe-money volatility unchanged. The result is a model that is computationally simple and more accurate than a constant volatility. The model fails to model the smile for lower strikes as the sensitivity of the delta to the underlying for options far-away-fromthe-money is minimal. This problem is in practice overcome by extrapolation.
Injected linguistic tags to improve phrase based SMT

This paper presents Injected Tags (ITs) approach, that improves the phrase based statistical machine translation (PBSMT) approach. This Injected Tags approach has been applied to “English into Arabic translation”. This approach is language independent and can be used with any language pair. It has shown considerable improvement of the translation quality of at least 13% increase of BLEU score. The approach has been evaluated and has been compared with several online Machine Translation (MT) services. The experiments reveal that the results achieved by this approach considered significant enhancements over PBSMT.
Factors behind the presence of agricultural credit cooperatives in Spain, 1900-1935: an econometric model

This paper analyzes the factor determining the presence and spread of agrarian credit cooperatives (rural savings banks) in Spain during the first third of the 20th century. We first provide descriptive evidence on the evolution of the number of agricultural cooperatives and then we analyze their economic and financial activity. We emphasize that the role of the public sector in this process is not always positive. Panel data estimates attest that the number of credit cooperatives is higher in regions with a higher number of potential competitors, i.e. saving banks and public granaries, also called positos. We also find that the higher the number of competitors in a year, the lower the number of cooperatives in the next year. As expected, the level of literacy of the population in a region determines the presence of credit cooperatives in that region.
Test Case Prioritization for Continuous Regression Testing: An Industrial Case Study

Regression testing in continuous integration environment is bounded by tight time constraints. To satisfy time constraints and achieve testing goals, test cases must be efficiently ordered in execution. Prioritization techniques are commonly used to order test cases to reflect their importance according to one or more criteria. Reduced time to test or high fault detection rate are such important criteria. In this paper, we present a case study of a test prioritization approach ROCKET (Prioritization for Continuous Regression Testing) to improve the efficiency of continuous regression testing of industrial video conferencing software. ROCKET orders test cases based on historical failure data, test execution time and domain-specific heuristics. It uses a weighted function to compute test priority. The weights are higher if tests uncover regression faults in recent iterations of software testing and reduce time to detection of faults. The results of the study show that the test cases prioritized using ROCKET (1) provide faster fault detection, and (2) increase regression fault detection rate, revealing 30% more faults for 20% of the test suite executed, comparing to manually prioritized test cases.
Homogenization of Periodic Multi-Dimensional Structures II: The Linearly Elastic/Perfectly Plastic Case

In this paper we study the asymptotic behaviour via Gamma-convergence of some integral functionals which model some multi-dimensional structures and depend explicitly on the linearized strain tensor. The functionals are defined in particular classes of functions with bounded deformation while the limit problem is set in the usual framework of Sobolev spaces or BD. We also construct an example of such functionals showing that under some special assumptions we can have non local effects.
A New Optimization Method for Centrifugal Compressors Based on 1D Calculations and Analyses

This paper presents an optimization design method for centrifugal compressors based on one-dimensional calculations and analyses. It consists of two parts: (1) centrifugal compressor geometry optimization based on one-dimensional calculations and (2) matching optimization of the vaned diffuser with an impeller based on the required throat area. A low pressure stage centrifugal compressor in a MW level gas turbine is optimized by this method. One-dimensional calculation results show that D3/D2 is too large in the original design, resulting in the low efficiency of the entire stage. Based on the one-dimensional optimization results, the geometry of the diffuser has been redesigned. The outlet diameter of the vaneless diffuser has been reduced, and the original single stage diffuser has been replaced by a tandem vaned diffuser. After optimization, the entire stage pressure ratio is increased by approximately 4%, and the efficiency is increased by approximately 2%.
How Should a Speech Recognizer Work?

Although researchers studying human speech recognition (HSR) and automatic speech recognition (ASR) share a common interest in how information processing systems (human or machine) recognize spoken language, there is little communication between the two disciplines. We suggest that this lack of communication follows largely from the fact that research in these related fields has focused on the mechanics of how speech can be recognized. In Marr's (1982) terms, emphasis has been on the algorithmic and implementational levels rather than on the computational level. In this article, we provide a computational-level analysis of the task of speech recognition, which reveals the close parallels between research concerned with HSR and ASR. We illustrate this relation by presenting a new computational model of human spoken-word recognition, built using techniques from the field of ASR that, in contrast to current existing models of HSR, recognizes words from real speech input.
Graph Regularized Feature Selection with Data Reconstruction

Feature selection is a challenging problem for high dimensional data processing, which arises in many real applications such as data mining, information retrieval, and pattern recognition. In this paper, we study the problem of unsupervised feature selection. The problem is challenging due to the lack of label information to guide feature selection. We formulate the problem of unsupervised feature selection from the viewpoint of graph regularized data reconstruction. The underlying idea is that the selected features not only preserve the local structure of the original data space via graph regularization, but also approximately reconstruct each data point via linear combination. Therefore, the graph regularized data reconstruction error becomes a natural criterion for measuring the quality of the selected features. By minimizing the reconstruction error, we are able to select the features that best preserve both the similarity and discriminant information in the original data. We then develop an efficient gradient algorithm to solve the corresponding optimization problem. We evaluate the performance of our proposed algorithm on text clustering. The extensive experiments demonstrate the effectiveness of our proposed approach.
Oivos: Simple and Efficient Distributed Data Processing

The complexity of implementing large scale distributed computations has motivated new programming models. Google's MapReduce model has gained widespread use and aims to hide the complex details of data partitioning and distribution, scheduling, synchronization, and fault tolerance. However, our experiences from the enterprise search business indicate that many real-life applications must be implemented as a collection of related MapReduce programs. Since the execution of these programs must be monitored and coordinated externally, several issues concerning scheduling, synchronization, and fault tolerance resurface. To address these limitations, we introduce Oivos; a high-level declarative programming model and its underlying runtime. We show how Oivos programs may specify computations that span multiple heterogeneous and interdependent data sets, how the programs are compiled and optimized, and how our run-time orchestrates and monitors their distributed execution. Our experimental evaluation reveals that Oivos programs do less I/O and execute significantly faster than the equivalent sequences of MapReduce passes.
Group identity, empathy and shared suffering: Understanding the ‘community’ impacts of anti-LGBT and Islamophobic hate crimes

This article examines the indirect impacts of hate crimes on lesbian, gay, bisexual and transgender and Muslim communities in the United Kingdom. Based on 34 qualitative interviews, we explore both the perceived meaning of ‘community’ in the context of targeted victimization and the emotional and behavioural effects that anti-lesbian, gay, bisexual and transgender and Islamophobic hate crimes have on other members of the victim’s group. Building on previous quantitative data undertaken as part of a larger programme of research, this study helps to explain how and why hate crimes have significant indirect consequences on two distinct but commonly targeted communities. The focus on lesbian, gay, bisexual and transgender and Muslim communities allowed us to draw out similarities and commonalities across different groups, further enhancing the understanding of the impacts of hate crime. In particular, the article highlights how for many lesbian, gay, bisexual and transgender and Muslim people feelings of anger and anxiety about hate crimes were linked to enhanced levels of empathy towards those that they share a group identity with. These empathic bonds often gave rise to a sense of ‘shared suffering’, with participants frequently feeling connected to group members worldwide through their common experiences of hate and prejudice. Although group identity was important to many participants’ sense of belonging to lesbian, gay, bisexual and transgender or Muslim communities, it was clear that the most profound impacts of hate crime were experienced when incidents occurred within someone’s local area. This highlighted the importance of location as a key variable in understanding both the meaning of ‘community’ and the indirect impacts of hate crime.
Integration of supply and demand for water in central Illinois urban areas. Research report

Water demand functions were estimated using two sets of data for Central Illinois -- community-wide data and household data. The community-wide data consist of total residential consumption for each of four pre-selected medium-sized cities in Central Illinois. The household data consist of residents from five cities who responded to a mail survey. The study investigates comparability of parameter estimates from the two approaches. If the parameter estimates are comparable, it would suggest water demand estimates need not require costly and time-consuming household surveys. Estimates of price elasticity are negative and less than unitary based on the two data sets used. The estimated price elasticity based on community-wide data is -. 037, while using household data estimated price elasticities are in the range from -. 14 to -. 16. The reasons for these differences are not immediately apparent and warrant further investigation.
Personalization efforts and the relationship to school climate in select Michigan high schools

The American high school is on the verge of a reform movement like that seen in American middle schools throughout the early and mid-1990s. In 1996 the National Association of Secondary School Principals (NASSP) released its recommendations for reform in the study Breaking Ranks: Changing an American Institution. Among these are recommendations that call for high schools to become smaller, less bureaucratic, and more responsive to student needs, where students feel a sense of belonging. Hoy and Miskel (2001) defined school climate as a “relatively enduring quality of the school’s environment that is experienced by participants, affects their behavior, is based on their collective perceptions of behavior in schools” (p. 190). The purpose of this study was to determine what relationship existed between the implementation of the Breaking Ranks recommendations and school climate. Principals of Michigan high schools of similar size and geography were asked to respond to a survey indicating the level of implementation of the Breaking Ranks recommendations on Personalization and Relationships in their high schools. To assess perceptions of school climate, staff members from these schools were asked to respond to the Organizational Climate Description Questionnaire for Secondary Schools (OCDQ-RS). A Spearman correlation between the level of implementation and the climate of the school was analyzed. The results indicated that while all of the Breaking Ranks recommendations and most of the strategies have been implemented to varying degrees in high schools, there is only a moderately strong relationship between implementation of the recommendations and strategies and school climate items related to principal qualities, teacher/principal relations, and teacher/student relations.
Learning classifiers from remote RDF data stores augmented with RDFS subclass hierarchies

Rapid growth of RDF data in the Linked Open Data (LOD) cloud offers unprecedented opportunities for analyzing such data using machine learning algorithms. The massive size and distributed nature of LOD cloud present a challenging machine learning problem where the data can only be accessed remotely, i.e. through a query interface such as the SPARQL end-point of the data store. Existing approaches to learning classifiers from RDF data in such a setting fail to take advantage of RDF schema (RDFS) associated with the data store that asserts subclass hierarchies which provide information that can potentially be exploited by the learner. Against this background, we present a general approach that augments an existing directed graphical model with hidden variables that encode subclass hierarchies via probabilistic constraints. We also present an algorithm ProbAVT that adopts the variational Bayesian expectation maximization approach to efficiently learn parameters in such settings. Our experiments with several synthetic and real world datasets show that: (i) ProbAVT matches or outperforms its counterpart that does not incorporate background knowledge in the form of subclass hierarchies; (ii) ProbAVT remains competitive compared to other state-of-art models that incorporate subclass hierarchies, and is able to scale up to large hierarchies consisting of over tens of thousands of nodes.