Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximating optimal state feedback using neural networks

The training and usage of multilayer neural networks on discontinuous (e.g. bang-bang) feedback control problems are discussed. Training sets are created from optimal open loop trajectory information and a heuristic for trimming the base data set is presented. A priori knowledge about solution trajectories is seen to improve the training process. <<ETX>>
Taking Situations Seriously

After years of neglect, situations are beginning to be taken seriously in psychological research. Two recent steps include the development of a theoretical framework, the Situation Construal Model (SCM), and an assessment tool, the Riverside Situational Q-sort (RSQ). The SCM describes behavior not only as a function of direct effects of personality and situations but also as a function of construal—how the individual perceives and responds to each situation he or she confronts. The RSQ assesses situations in terms of 89 descriptive phrases that can be rated by observers or participants; the consensus of socially competent observers represents the “objective” nature of a situation. The SCM provides a basis for an ongoing program of research using the RSQ to examine topics including the consistency of behavior, person-environment congruence, consequences of situational construal in social and medical contexts, classification of situations based on evolutionary theory, and the comparison of situational experience across cultures. Promising directions for future research include applications to educational, medical, and industrial settings. Taking situations seriously opens the door to many potential theoretical advances and practical applications.
The Intelligence Automated Thread-Fastening Assembly using the Newton Raphson

principle of the thread fastenings have been known and used for decades. Its long applications become to a common manufacturing with the purpose of joining one component to another. Recently, the thread fastenings are a common assembly method, which is widely used and popularly applied. Because of the thread fastenings can be permitted easy to reassembly and disassembly for maintenance, repair, relocation, and recycling. Screw insertions are typically carried out manually with having the purpose of joining one component to another. Its is more complex to automate, and hence has admitted relatively very small research attention on automating threaded fastenings, and most automated assembly research is focused on the peg-in-hole assembly problem. This paper investigates the problem of an intelligence control strategy for automated Threaded Fastening process based on an analytical model and on-line parameter estimation. The monitoring problem deals with predicting the integrity of the self-tapping Threaded Fastening process, based on the torque vs. insertion angle curves generated during the insertions. A technique for estimating three unknown parameters during a general self-tapping Threaded Fastening is presented. It is shown that these parameters, required by the mathematical model, can be reliably estimated on-line. Test results are presented to validate procedure.
Enforcing Structural Regularities in Source Code using IntensiVE

The design and implementation of a software system is often governed by many different coding conventions, design patterns, architectural design rules, and other so-called structural regularities. To prevent a deterioration of the system's source code, it is important that these regularities are verified and enforced in subsequent evolutions of the system. The Intensional Views Environment (IntensiVE), which is the subject of this demonstration, is a tool suite for documenting such structural regularities in (object-oriented) software systems and verifying their consistency in later versions of those systems.
Local and distributed interaction in a collaborative knowledge building scenario

The structure and organization of a learning environment has implications for how students organize their collaborative interactions and learning activities. Investigating how students understand and utilize the collaborative conditions in a learning environment is thus a key issue to obtain insight into how to improve the design of such environments. In this paper we identify how collaborative knowledge building is produced in distributed and co-located interactions between students and discuss how this is related to the design of the learning environment.
Trustworthiness and IT Suspicion: An Evaluation of the Nomological Network

Objective: The authors evaluated the validity of trust in automation and information technology (IT) suspicion by examining their factor structure and relationship with decision confidence. Background: Research on trust has burgeoned, yet the dimensionality of trust remains elusive. Researchers suggest that trust is a unidimensional construct, whereas others believe it is multidimensional. Additionally, novel constructs, such as IT suspicion, have yet to be distinguished from trust in automation. Research is needed to examine the overlap between these constructs and to determine the dimensionality of trust in automation. Method: Participants (N = 72) engaged in a computer-based convoy scenario involving an automated decision aid. The aid fused real-time sensor data and provided route recommendations to participants who selected a route based on (a) a map with historical enemy information, (b) sensor inputs, and (c) automation suggestions. Measures for trust in automation and IT suspicion were administered after individuals interacted with the automation. Results: Results indicated three orthogonal factors: trust, distrust, and IT suspicion. Each variable was explored as a predictor of decision confidence. Distrust and trust evidenced unique influences on decision confidence, albeit at different times. Higher distrust related to less confidence, whereas trust related to greater confidence. Conclusion: The current study found that trust in automation was best characterized by two orthogonal dimensions (trust and distrust). Both trust and distrust were found to be independent from IT suspicion, and both distrust and trust uniquely predicted decision confidence. Application: Researchers may consider using separate measures for trust and distrust in future studies.
Action Potential Generation at an Axon Initial Segment-Like Process in the Axonless Retinal AII Amacrine Cell

In axon-bearing neurons, action potentials conventionally initiate at the axon initial segment (AIS) and are important for neuron excitability and cell-to-cell communication. However in axonless neurons, spike origin has remained unclear. Here we report in the axonless, spiking AII amacrine cell of the mouse retina a dendritic process sharing organizational and functional similarities with the AIS. This process was revealed through viral-mediated expression of channelrhodopsin-2-GFP with the AIS-targeting motif of sodium channels (NavII-III). The AII processes showed clustering of voltage-gated Na+ channel 1.1 (Nav1.1) as well as AIS markers ankyrin-G and neurofascin. Furthermore, NavII-III targeting disrupted Nav1.1 clustering in the AII process, which drastically decreased Na+ current and abolished the ability of the AII amacrine cell to generate spiking. Our findings indicate that, despite lacking an axon, spiking in the axonless neuron can originate at a specialized AIS-like process.
A Negativity Bias in Reframing Shapes Political Preferences Even in Partisan Contexts

Humans evolved to attend to valence and group membership when learning about their environment. The political domain offers a unique opportunity to study the simultaneous influence of these two broad, domain-general features of human experience. We examined whether the pervasive tendency for negatively valenced frames to “stick” in the mind applies to both intergroup and intragroup political contexts. In a preregistered experiment, we tested the effects of negative-to-positive (vs. positive-to-negative) reframing on people’s candidate preferences, first in the absence of party cue information and then in two partisan contexts: an intergroup context (analogous to a U.S. general election between opposing political parties) and an intragroup context (analogous to a U.S. primary election between candidates of the same party). We observed a persistent negativity bias in reframing effects, even in the presence of party cues. The results pave the way for future research at the intersection of psychology and political science.
t Synchrony Generation in Recurrent Networks with Frequency-Dependent Synapses

Throughout the neocortex, groups of neurons have been found to fire synchronously on the time scale of several milliseconds. This near coincident firing of neurons could coordinate the multifaceted information of different features of a stimulus. The mechanisms of generating such synchrony are not clear. We simulated the activity of a population of excitatory and inhibitory neurons randomly interconnected into a recurrent network via synapses that display temporal dynamics in their transmission; surprisingly, we found a behavior of the network where action potential activity spontaneously self-organized to produce highly synchronous bursts involving virtually the entire network. These population bursts were also triggered by stimuli to the network in an all-or-none manner. We found that the particular intensities of the external stimulus to specific neurons were crucial to evoke population bursts. This topographic sensitivity therefore depends on the spectrum of basal discharge rates across the population and not on the anatomical individuality of the neurons, because this was random. These results suggest that networks in which neurons are even randomly interconnected via frequency-dependent synapses could exhibit a novel form of reflex response that is sensitive to the nature of the stimulus as well as the background spontaneous activity.
Video enhancement using an iterative multiframe SRR based on a robust stochastic estimation with an improved observation model

This paper proposes a video enhancement method using a novel super-resolution reconstruction (SRR) framework for real standard sequences that are corrupted by any noise models. The traditional SRR algorithms are very sensitive to their assumed model of data and noise, which limits their utility. The real noise models that corrupt the measure sequence are unknown; consequently, SRR algorithm using LI or L2 norm may degrade the image sequence rather than enhance it. The robust norm applicable to several noise and data models is desired in SRR algorithms. First, this paper proposes a robust SRR algorithm based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. The Huber norm with Tikhonov regularization is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data. Second, in order to cope with real sequences and complex motion sequences, this paper proposes an improved SRR observation model, affine block-based transform, devoted to the case of nonisometric inter-frame motion. The experimental results show that the proposed reconstruction can enhance real complex motion sequences, such as Suzie and Foreman sequence, and confirm the effectiveness of our algorithm and demonstrate its superiority to other SRR algorithms based on LI and L2 norm for several noise models such as AWGN, Poisson, Salt&Pepper and Speckle noise.
Large-scale Image Retrieval using Neural Net Descriptors

One of current big challenges in computer science is development of data management and retrieval techniques that would keep pace with the evolution of contemporary data and with the growing expectations on data processing. Various digital images became a common part of both public and enterprise data collections and there is a natural requirement that the retrieval should consider more the actual visual content of the image data. In our demonstration, we aim at the task of retrieving images that are visually and semantically similar to a given example image; the system should be able to online evaluate k nearest neighbor queries within a collection containing tens of millions of images. The applicability of such a system would be, for instance, on stock photography sites, in e-shops searching in product photos, or in collections from a constrained Web image search.
Resource and Performance Distribution Prediction for Large Scale Analytics Queries

Efficient resource consumption and performance estimation of data-intensive workloads is central to the design and development of workload management techniques. Recent work has explored the efficacy of using distribution-based estimation of workload performance as opposed to single point prediction for a number of workload management problems such as query scheduling, admission control, and the like. However, the proposed approaches lack an efficient workload performance distribution prediction in that they simply assume that the probability distribution function (pdf) of the target value is already available. This paper aims to address this problem for an inseparable portion of big data analytics workloads, Hive queries. To this end, we combine knowledge of Hive query executions with the novel usage of mixture density networks to predict the whole spectrum of resource and performance as probability density functions. We evaluate our technique using the TPC-H benchmark, showing that it not only produces accurate pdf predictions but outperforms the state of the art single point techniques in half of experiments.
Exploiting thermal reflection for interactive systems

Thermal cameras have recently drawn the attention of HCI researchers as a new sensory system enabling novel interactive systems. They are robust to illumination changes and make it easy to separate human bodies from the image background. Far-infrared radiation, however, has another characteristic that distinguishes thermal cameras from their RGB or depth counterparts, namely thermal reflection. Common surfaces reflect thermal radiation differently than visual light and can be perfect thermal mirrors. In this paper, we show that through thermal reflection, thermal cameras can sense the space beyond their direct field-of-view. A thermal camera can sense areas besides and even behind its field-of-view through thermal reflection. We investigate how thermal reflection can increase the interaction space of projected surfaces using camera-projection systems. We moreover discuss the reflection characteristics of common surfaces in our vicinity in both the visual and thermal radiation bands. Using a proof-of-concept prototype, we demonstrate the increased interaction space for hand-held camera-projection system. Furthermore, we depict a number of promising application examples that can benefit from the thermal reflection characteristics of surfaces.
A Normalized Levenshtein Distance Metric

Although a number of normalized edit distances presented so far may offer good performance in some applications, none of them can be regarded as a genuine metric between strings because they do not satisfy the triangle inequality. Given two strings X and Y over a finite alphabet, this paper defines a new normalized edit distance between X and Y as a simple function of their lengths (|X| and |Y|) and the Generalized Levenshtein Distance (GLD) between them. The new distance can be easily computed through GLD with a complexity of O(|X| \cdot |Y|) and it is a metric valued in [0, 1] under the condition that the weight function is a metric over the set of elementary edit operations with all costs of insertions/deletions having the same weight. Experiments using the AESA algorithm in handwritten digit recognition show that the new distance can generally provide similar results to some other normalized edit distances and may perform slightly better if the triangle inequality is violated in a particular data set.
Multi-grained level of detail using a hierarchical seamless texture atlas

Previous algorithms for view-dependent level of detail provide local mesh refinements either at the finest granularity or at a fixed, coarse granularity. The former provides triangle-level adaptation, often at the expense of heavy CPU usage and low triangle rendering throughput; the latter improves CPU usage and rendering throughput by operating on groups of triangles. We present a new multiresolution hierarchy and associated algorithms that provide adaptive granularity. This multi-grained hierarchy allows independent control of the number of hierarchy nodes processed on the CPU and the number of triangles to be rendered on the GPU. We employ a seamless texture atlas style of geometry image as a GPU-friendly data organization, enabling efficient rendering and GPU-based stitching of patch borders. We demonstrate our approach on both large triangle meshes and terrains with up to billions of vertices.
A Sex Difference in the Effect of Low Levels of Caffeine on the Stroop Task

Low levels of caffeine may decrease performance on complex cognitive tasks. To test this hypothesis 63 psychology students participated in a study examining the effects of 45 mg of caffeine on the Stroop task. The participants consumed either a 12 -oz (355-ml) can of Coca-Cola or a 12-oz (355-ml) can of caffeine-free CocaCola. A short questionnaire was then answered by the participants, after which they completed a computerized version of the Stroop task. We observed that women had slower reaction times than men (p < .001), and that the effects of caffeine were sex specific, having a significant effect only on men (p < .01). We also found a significant main effect of display (p < .001), and an interaction between sex and display (p < .05), which occurred only in the conflict condition of the Stroop task. These sex differences may reflect the lipid-soluble nature of caffeine. The decreased performance in the caffeine group can be explained by the Yerkes-Dodson law.
Development of high-sensitivity slip sensor using special characteristics of pressure conductive rubber

Even with the eyes closed, humans are able to grip an object with minimal force without such information as the coefficient of friction or the weight. Tactile sensors capable of detecting slippage are necessary for this gripping action to be realized in a robot hand. Heretofore, many slip sensors were developed and produced, but there was not a slip sensor of simple structure practical for installation on fingertips of a robot hand. Therefore, we propose a low-profile/lightweight slip sensor of simple structure. The special properties of pressure conductive rubber are utilized as a detection device in this sensor. In this paper, we discuss the results of trial manufacture and of slip detection property testing of this sensor. Moreover, we will report the results of slip prevention experiments by this prototype slip sensor, and indicate that the pressure conductive rubber is promising as a material of slip sensor.
ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression

ATTED-II (http://atted.jp) is a coexpression database for plant species with parallel views of multiple coexpression data sets and network analysis tools. The user can efficiently find functional gene relationships and design experiments to identify gene functions by reverse genetics and general molecular biology techniques. Here, we report updates to ATTED-II (version 8.0), including new and updated coexpression data and analysis tools. ATTED-II now includes eight microarray- and six RNA sequencing-based coexpression data sets for seven dicot species (Arabidopsis, field mustard, soybean, barrel medick, poplar, tomato and grape) and two monocot species (rice and maize). Stand-alone coexpression analyses tend to have low reliability. Therefore, examining evolutionarily conserved coexpression is a more effective approach from the viewpoints of reliability and evolutionary importance. In contrast, the reliability of species-specific coexpression data remains poor. Our assessment scores for individual coexpression data sets indicated that the quality of the new coexpression data sets in ATTED-II is higher than for any previous coexpression data set. In addition, five species (Arabidopsis, soybean, tomato, rice and maize) in ATTED-II are now supported by both microarray- and RNA sequencing-based coexpression data, which has increased the reliability. Consequently, ATTED-II can now provide lineage-specific coexpression information. As an example of the use of ATTED-II to explore lineage-specific coexpression, we demonstrate monocot- and dicot-specific coexpression of cell wall genes. With the expanded coexpression data for multilevel evaluation, ATTED-II provides new opportunities to investigate lineage-specific evolution in plants.
Dot Text Detection Based on FAST Points

In this paper, we propose a method for dot text detection based on FAST points. This problem is different from general scene text detection because of discontinuous text stroke. Unlike many other methods which assume that text is horizontally oriented, our method is able to deal with slant dot text. We extract interesting patches from FAST points and define four features based on the stroke and gray value similarity of dot text to describe a patch. Then, we generate some candidate regions from these patches and utilize SVM to filter out non-dot text ones with the first and second order moments of FAST points in them. Experimental results show that the proposed method is effective and fast to detect dot text.
Pseudo Labeling for Scalable Semi-supervised Learning of Domain-speciﬁc Sentiment Lexicons

Since manually constructing domain-speciﬁc sentiment lexi-cons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available. Research on the automatic construction of domain-speciﬁc sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain-speciﬁc sentiment lexicons. More speciﬁcally, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpus-base statistical learning to make domain-speciﬁc sentiment extraction scalable with respect to the sheer volume of opin-ionated documents archived on the Internet these days. Another novelty of the proposed method is that it can utilize the readily available user-contributed labels of opinion-ated documents (e.g., the user ratings of product reviews) to bootstrap the performance of sentiment lexicon construction. Our experiments show that the proposed method can generate high quality domain-speciﬁc sentiment lexicons as directly assessed by human experts. Moreover, the system generated domain-speciﬁc sentiment lexicons can improve polarity prediction tasks at the document level by 2 . 18% when compared to other well-known baseline methods. Our research opens the door to the development of practical and scalable methods for domain-speciﬁc sentiment analysis.