Miracle-dz/UniFAR-FTUs huggingface dataset:
Taloustieteet Normative and Descriptive Theories of Decision Making under Risk : A Short Review

The paper discusses some recent developments in analysis of decision making under risk and uncertainty. First we present the expected utility theory and the basic assumptions of rationality. Second we discuss the critique that has been directed towards the expected utility theory. Next we present the alternative theories: regret theory, prospect theory and theory of stochastic preference. Finally we discuss some practical examples from casinos and gambling markets. It is argued that descriptive theories (e.g. prospect theory) have taken room from normative theories (e.g. expected utility theory). We conclude that we should push forward with empirical testing, both experimental and non-experimental, and develop descriptive theories in the future. However normative and descriptive theories are not mutually exclusive. Both are needed in real life decision making.
Advertised Window Control Based on ACK Throughput Measurement for Improving TCP Performance

We discuss the efficiency of Transmission Control Protocol (TCP) transmission over a bandwidth-guaranteed network, where the bandwidth for each flow is guaranteed by traffic policing at edge nodes. In particular, we explored a method for improving TCP performance, focusing on the effect of queuing delay generated by multiplex TCP flows. We propose a rate control method at an edge node that rewrites the advertised window according to measuring the throughput of acknowledgement (ACK) packets. The proposed method can supplement the decrease in the throughput for the guaranteed bandwidth caused by the increase in the queuing delay. We evaluated the effectiveness of the proposed method, which guarantees fair throughput, regardless of round-trip time, through simulations.
Computing Least Common Subsumers in Description Logics with Existential Restrictions

Computing the least common subsunier (les) is an inference task that can be used to support, the "bottom-up" construction of knowledge bases for KR systems based on description logics. Previous work on how to compute the lcs has concentrated on description logics that allow for universal value restrictions, but not for existential restrictions. The main new contribution of this paper is the treatment of description logics with existential restrictions. Our approach for computing the lcs is based on an appropriate representation of concept descriptions by certain trees, and a characterization of subsumption by homomorphisins between these trees. The lcs operation then corresponds to the product operation on trees.
Influence of Risk Aversion on Distinct Risky Investment Preference: The Mediating Role of Information Search

Risk aversion is one of major psychological determinants of risky decision-making behavior and information search is often used as a way to overcome perceived risk by investors. This study attempted to propose a mediated model of the determinants of risky investment decision making where theorizing that the effect of risk aversion on risky investment decision was mediated by information search, integrating risk aversion and information search. We expected that the results could provide empirical support for (1) the inconsistency of risk aversion across different risky investment in financial domains, and (2) the usefulness of the mediated model which views the information search as a mediator of effects on indirect risky investment preference, especially the mediating role of professional advice. Thus the practical implications for professional advisors or for mutual fund companies could be addressed.
Audio Visual Speech Recognition and Segmentation Based on DBN Models

Automatic speech recognition is of great importance in human-machine interfaces. Despite extensive effort over decades, acoustic-based recognition systems remain too inaccurate for the vast majority of real applications, especially those in noisy environments, e.g. crowed environment. The use of visual features in audio-visual speech recognition is motivated by the speech formation mechanism and the natural speech ability of humans to reduce audio ambiguities using visual cues. Moreover, the visual information provides complementary cues that cannot be corrupted by the acoustic noise of the environment. However, problems such as the selection of the optimal set of visual features, and the optimal models for audio-visual integration remain challenging research topics. In recent years, the most common model fusion methods for audio visual speech recognition are Multi-stream Hidden Markov Models (MSHMMs) such as product HMM and coupled HMM. In these models, audio and visual features are imported to two or more parallel HMMs with different topology structures. These MSHMMs describe the correlation of audio and visual speech to some extent, and allow asynchrony within speech units. Compared with the single stream HMM, system performance is improved especially in noisy speech environment. But at the same time, problems remain due to the inherent limitation of the HMM structure, that is, on some nodes, such as phones, syllables or words, constraints are imposed to limit the asynchrony between audio stream and visual stream to phone (or syllable, word) level. Since for large vocabulary continuous speech recognition task, phones are the basic modeling units, audio stream and visual stream are forced to be synchronized at the timing boundaries of phones, which is not coherent with the fact that the visual activity often precedes the audio signal even by 120 ms.Besides the audio visual speech recognition to improve the word recognition rate in noisy environments, the task of audio visual speech units (such as phones or visemes) segmentation also requires a more reasonable speech model which describes the inherent correlation and asynchrony of audio and visual speech.
Analysis of Polynomial Systems With Time Delays via the Sum of Squares Decomposition

We present a methodology for analyzing robust independent-of-delay and delay-dependent stability of equilibria of systems described by nonlinear Delay Differential Equations by algorithmically constructing appropriate Lyapunov-Krasovskii functionals using the sum of squares decomposition of multivariate polynomials and semidefinite programming. We illustrate the methodology using an example from population dynamics.
Mindfulness-based stress reduction in a mental health outpatient setting: Benefits beyond symptom reduction

ABSTRACT Mindfulness-based stress reduction (MBSR) originated from Buddhist spiritual practices with the aim of restoring wholeness and well-being among medical patients suffering from pain and illness. MBSR effectively reduces a wide range of symptoms, but investigations measuring positive outcomes tend to include only nonclinical or nondistressed individuals. In the current study, 30 participants seeking psychological services reported significant reductions on symptom measures at their final MBSR session, accompanied by increased mindfulness, personal growth, life satisfaction, and improved quality of life. The spiritual origins of MBSR may reflect its potential to enhance personal growth and quality of life beyond mere symptom reduction.
System anomaly detection in distributed systems through MapReduce-Based log analysis

System anomaly detection is very important for development, maintenance and performance refinement in large scale distributed systems. It's a good way to obtain the troubleshooting and problem diagnosis by analyzing system logs produced by distributed systems. However, due to the increasing scale and complexity of distributed systems, the size of logs must be very large. Thus, it's inefficient for common methods to analyze system logs on single node. Therefore, there is a great demand to adopt a distributed method for anomaly detection techniques based on log analysis. In this paper, a MapReduce-Based Framework is implemented to analyze the distributed log for detecting anomaly. The framework is built on top of Hadoop, an open source distributed file system and MapReduce implementation. We first make use of Random Access File to realize an incremental way for aggregating system logs from each node of the monitored cluster, and collect them to the analysis cluster. Then, we apply the K-means clustering algorithm to integrate the collected logs. After that, we implement a MapReduce-Based algorithm to parser these clustered log files. Furthermore, in order to make the best use of this collected data, a flexible and powerful way is utilized to display monitoring and analysis results. Thus, we can monitor system status of large distributed cluster and detect its anomalies.
Choosing the Velvet Glove: Women Voters, Ambivalent Sexism, and Vote Choice in 2016

Abstract This paper examines the extent to which ambivalent sexism toward women influenced vote choice among American women during the 2016 Presidential election. I examine how this varied between white women and women of color. The 2016 American National Election Study (ANES) features several measures from the Ambivalent Sexism Inventory (ASI)—a scale developed by Glick and Fiske (1996) to assess sexist attitudes toward women. An index of these measures is used to examine the extent to which ambivalent sexist attitudes influenced women's vote choice for Donald Trump, controlling for racial resentment, partisanship, attitudes toward immigrants, economic anxiety, and socio-demographics. On the one hand, my findings indicate that ambivalent sexism was a powerful influence on women's Presidential vote choice in 2016, controlling for other factors. However, this finding, based on a model of all women voters is misleading, once an intersectional approach is undertaken. Once the data are disaggregated by gender and race, white women's political behavior proves very different than women of color. Among white women, ambivalent sexist views positively and significantly predicts vote choice for Trump, controlling for all other factors. However, for women of color, this relationship was negative and posed no statistical significant relationship to voting for Trump. Scholarship in gender and politics that does not account for group differences in race/ethnicity may present misleading results, which are either underestimated or overestimated.
Control Synthesis of Discrete Manufacturing Systems using Timed Finite Automata

An application of timed game automaton to the control synthesis of discrete manufacturing systems is presented in the paper. This approach adopts the algorithm for optimal control synthesis of timed automata proposed in [1] as a game-theoretic approach between controller and the model of the plant. KeyWords: Timed automata, control synthesis, model verification, production systems.
Bittorrent peer identification based on behaviors of a choke algorithm

Bittorrent is currently one of the most popular peer-to-peer (P2P) file sharing protocols. However, it incurs such excessive amount of traffic that it may adversely affect users of legacy internet applications. To limit this adverse impact, an efficient methodology for bittorrent identification may be needed. In this paper, we propose a novel approach to identify local bittorrent peers. Our approach is based on behaviors of the choke algorithm, a main algorithm used in bittorrent. An advantage of our approach is that we can identify bittorrent peers without examining the packet pay-load. Therefore, our approach is free from privacy issues and still effective even though the packet payload is encrypted. Unlike previous works, we identify bittorrent hosts at the peer level instead of the flow level. Given that we use only information from network layer instead of transport layer, our work maintains fewer states and achieves robustness to changes in the transport layer. Furthermore, our work is effective even in restricted environments such as networks equipped with NAT devices or firewalls without modifications to existing network equipments. Our experimental result indicates that our approach can efficiently identify most of excessive bandwidth-consuming peers (i.e., peers transfer a large amount of data in our traces) with a low false-positive rate.
PERMA and the building blocks of well-being

Abstract Seligman (2011) hypothesized that PERMA (Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment) are the elements of well-being. Goodman, Disabato, Kashdan & Kaufmann (2017) reported strong evidence that subjective well-being is the final common path of such elements and their data are entirely consistent with Seligman’s hypothesis. They argued, incorrectly however, that he suggested that PERMA constituted a different kind of well-being rather than just its building blocks. The complicated issue, one that transcends psychometrics, of how to decide on elements of well-being is discussed.
Recognizing facial expressions by spatio-temporal analysis

An approach for analysis and representation of facial dynamics for recognition of facial expressions from image sequences is proposed. The algorithms the authors develop utilize optical flow computation to identify the direction of rigid and non-rigid motions that are caused by human facial expressions. A mid-level symbolic representation that is motivated by linguistic and psychological considerations is developed. Recognizing six facial expressions, as well as eye blinking, are demonstrated on a collection of image sequences.
Deep Cuboid Detection: Beyond 2D Bounding Boxes

We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects). Contrary to classical approaches which fit a 3D model from low-level cues like corners, edges, and vanishing points, we propose an end-to-end deep learning system to detect cuboids across many semantic categories (e.g., ovens, shipping boxes, and furniture). We localize cuboids with a 2D bounding box, and simultaneously localize the cuboid's corners, effectively producing a 3D interpretation of box-like objects. We refine keypoints by pooling convolutional features iteratively, improving the baseline method significantly. Our deep learning cuboid detector is trained in an end-to-end fashion and is suitable for real-time applications in augmented reality (AR) and robotics.
Preliminary and incomplete Comments welcome Price setting in online markets : Basic facts , international comparisons , and cross-border integration *

We document basic facts about prices in online markets in the U.S. and Canada, a rapidly growing segment of the retail sector. Relative to prices in regular stores, prices in online markets are more flexible as well as exhibit stronger pass-through (60-75 percent) and faster convergence (half-life less than 2 months) in response to movements of the nominal exchange rate. Multiple margins of adjustment (frequency of price changes, direction of price changes, size of price changes, exit of sellers) are active in the process of responding to nominal exchange rate shocks. Furthermore, we use the richness of our dataset to show that degree of competition, stickiness of prices, synchronization of price changes, reputation of sellers, and returns to search effort are important determinants of pass-through and speed of price adjustment for international price differentials. JEL: E3, F3, F40, F41
LPQP for MAP: Putting LP Solvers to Better Use

MAP inference for general energy functions remains a challenging problem. While most efforts are channeled towards improving the linear programming (LP) based relaxation, this work is motivated by the quadratic programming (QP) relaxation. We propose a novel MAP relaxation that penalizes the Kullback-Leibler divergence between the LP pairwise auxiliary variables, and QP equivalent terms given by the product of the unaries. We develop two efficient algorithms based on variants of this relaxation. The algorithms minimize the non-convex objective using belief propagation and dual decomposition as building blocks. Experiments on synthetic and real-world data show that the solutions returned by our algorithms substantially improve over the LP relaxation.
Authoritative Scholarly Paper Recommendation Based on Paper Communities

With the rapid growth of the number of papers, the traditional methods of searching papers by scholar search engine are becoming unacceptable. These methods can't meet the needs of users and users still need to take a lot of time to filter the search results. To solve the problem, this paper uses the concepts and methods of community partition and introduces a model to recommend authoritative papers based on the specific community. Above all, this model uses Greedy Clique Expansion Algorithm to discover communities. Then, we study the diffusion of influence based on the specific community. At last, our model uses Paper Rank Algorithm to compute the influence of papers and gets a recommendation list. Compared with existing paper recommendation methods, our method narrows the scope of recommended papers, and further improves the recommending speed. Besides, our method improves the quality of recommended papers by ranking papers' influence.
The acquisition of /p/ and /k/ word-mid codas of English (L2) by learners from Southern Brazil (L1) : a gestural analysis in stochastic optimality theory

In this article, we formalize the acquisition of word-mid /pt/ and /kt/ sequences in English (L2) by learners from Southern Brazil. The participants, who presented a basic proficiency level in English, had their productions recorded both in English and in Brazilian Portuguese, which allowed for an analysis of the acoustic patterns found in the production of /p/ and /k/ obstruent codas. The acoustic patterns produced by the learners were analyzed using the Stochastic-Optimality Theory, and the constraints used in the analysis were based on the framework of Gestural Phonology (BROWMAN & GOLDSTEIN, 1992) and in the gestural landmarks proposed by Gafos (2002). We conclude that a gestural analysis allows for the formalization of a wider range of acoustic patterns which tended not to be considered in traditional accounts of phonology, as these patterns assume a different status since they are then considered to be part of the grammar.
Vision-based adaptive cruise control for intelligent road vehicles

There is a broad range of robotics technologies that are currently being applied to the generic topic of intelligent transportation systems (ITS). One of the most important research topics in this field is adaptive cruise control (ACC), aiming at adapting the vehicle speed to a predefined value while keeping a safe gap with regard to potential obstacles. For this purpose, a monocular vision system provides the distance between the ego vehicle and the preceding vehicle on the road. The complete system can be understood as a vision-based ACC controller, based on fuzzy logic, which assists the velocity vehicle control offering driving strategies and actuation over the throttle of a car. This controller is embedded in an automatic driving system installed in two testbed mass-produced cars operating in a real environment. The results obtained in these experiments show a very good performance of the vision-based gap controller, which is adaptable to all speeds and safe gap selections.
Context-Dependent Multiple Distribution Phonetic Modeling with MLPs

A number of hybrid multilayer perceptron (MLP)/hidden Markov model (HMM) speech recognition systems have been developed in recent years (Morgan and Bourlard, 1990). In this paper, we present a new MLP architecture and training algorithm which allows the modeling of context-dependent phonetic classes in a hybrid MLP/HMM framework. The new training procedure smooths MLPs trained at different degrees of context dependence in order to obtain a robust estimate of the context-dependent probabilities. Tests with the DARPA Resource Management database have shown substantial advantages of the context-dependent MLPs over earlier context-independent MLPs, and have shown substantial advantages of this hybrid approach over a pure HMM approach.
Estimating Better Position Alignments for Statistical Machine Translation

In Statistical Machine Translation, we often find forward or backword jumps while translating from a source position to a target position. We propose several position alignment models for estimating these jump probabilities. Our initial jump probability model is a coarse model with no dependencies. The maximum likelihood estimation of the jump probabilities is performed during post word alignment using maximal approximations from the IBM 4 word alignment model output. We systematically add intutive parameters to provide more accurate models and evaluate these models based on their perplexity on a test set. We also report our results with smoothing via linear interpolation to account for data sparseness issues. The improvement in perplexity can provide an indication of how well the additional parameters model the jump probabilities. The best model can then be applied in various MT components like the decoder or in the iterative word-alignment model training itself.
PPAR-γ Regulates Trophoblast Differentiation in the BeWo Cell Model

Common pregnancy complications, such as severe preeclampsia and intrauterine growth restriction, disrupt pregnancy progression and impair maternal and fetal wellbeing. Placentas from such pregnancies exhibit lesions principally within the syncytiotrophoblast (SCT), a layer in direct contact with maternal blood. In humans and mice, glial cell missing-1 (GCM-1) promotes differentiation of underlying cytotrophoblast cells into the outer SCT layer. GCM-1 may be regulated by the transcription factor peroxisome proliferator-activated receptor-gamma (PPAR-γ); in mice, PPAR-γ promotes labyrinthine trophoblast differentiation via Gcm-1, and, as we previously demonstrated, PPAR-γ activation ameliorates disease features in rat model of preeclampsia. Here, we aimed to characterize the baseline activity of PPAR-γ in the human choriocarcinoma BeWo cell line that mimics SCT formation in vitro and modulate PPAR-γ activity to study its effects on cell proliferation versus differentiation. We report a novel negative autoregulatory mechanism between PPAR-γ activity and expression and show that blocking PPAR-γ activity induces cell proliferation at the expense of differentiation, while these remain unaltered following treatment with the agonist rosiglitazone. Gaining a deeper understanding of the role and activity of PPAR-γ in placental physiology will offer new avenues for the development of secondary prevention and/or treatment options for placentally-mediated pregnancy complications.
Break on through : Tunnel-based exploration to learn about outdoor terrain

We study the problem of designing a ground robot that learns how to navigate in natural, outdoor settings. This robot must learn, through its own experience, a traversability model. This model provides a mapping from the robot’s sensors to a traversability measure. We propose a novel approach to exploration in which the robot purposefully probes into apparent obstacles, with the goal of improving its traversability model. We formulate a decision-theoretic algorithm that identifies potential “tunnels” through apparent obstacles and estimates the benefit of exploring them. We have implemented and tested the algorithm on an outdoor robot platform and we present results from initial experiments that demonstrate the operation of our algorithm and quantify its performance advantage over a baseline system.