Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomimetic and bio-inspired robotics in electric fish research

Summary Weakly electric knifefish have intrigued both biologists and engineers for decades with their unique electrosensory system and agile swimming mechanics. Study of these fish has resulted in models that illuminate the principles behind their electrosensory system and unique swimming abilities. These models have uncovered the mechanisms by which knifefish generate thrust for swimming forward and backward, hovering, and heaving dorsally using a ventral elongated median fin. Engineered active electrosensory models inspired by electric fish allow for close-range sensing in turbid waters where other sensing modalities fail. Artificial electrosense is capable of aiding navigation, detection and discrimination of objects, and mapping the environment, all tasks for which the fish use electrosense extensively. While robotic ribbon fin and artificial electrosense research has been pursued separately to reduce complications that arise when they are combined, electric fish have succeeded in their ecological niche through close coupling of their sensing and mechanical systems. Future integration of electrosense and ribbon fin technology into a knifefish robot should likewise result in a vehicle capable of navigating complex 3D geometries unreachable with current underwater vehicles, as well as provide insights into how to design mobile robots that integrate high bandwidth sensing with highly responsive multidirectional movement.
ExsE Is a Negative Regulator for T3SS Gene Expression in Vibrio alginolyticus

Type III secretion systems (T3SSs) contribute to microbial pathogenesis of Vibrio species, but the regulatory mechanisms are complex. We determined if the classic ExsACDE protein-protein regulatory model from Pseudomonas aeruginosa applies to Vibrio alginolyticus. Deletion mutants in V. alginolyticus demonstrated that, as expected, the T3SS is positively regulated by ExsA and ExsC and negatively regulated by ExsD and ExsE. Interestingly, deletion of exsE enhanced the ability of V. alginolyticus to induce host-cell death while cytotoxicity was inhibited by in trans complementation of this gene in a wild-type strain, a result that differs from a similar experiment with Vibrio parahaemolyticus ExsE. We further showed that ExsE is a secreted protein that does not contribute to adhesion to Fathead minnow epithelial cells. An in vitro co-immunoprecipitation assay confirmed that ExsE binds to ExsC to exert negative regulatory effect on T3SS genes. T3SS in V. alginolyticus can be activated in the absence of physical contact with host cells and a separate regulatory pathway appears to contribute to the regulation of ExsA. Consequently, like ExsE from P. aeruginosa, ExsE is a negative regulator for T3SS gene expression in V. alginolyticus. Unlike the V. parahaemolyticus orthologue, however, deletion of exsE from V. alginolyticus enhanced in vitro cytotoxicity.
An Integrative Approach to the Biomechanical Function and Neuromuscular Control of the Fingers

The exquisite mechanical functionality and versatility of the human hand emerges from complex neuro–musculo–skeletal interactions that are not completely understood. I have found it useful to work within a theoretical/experimental paradigm that outlines the fundamental neuro–musculo–skeletal components and their interactions. In this integrative paradigm, the laws of mechanics, the specifications of the manipulation task, and the sensorimotor signals define the interactions among hand anatomy, the nervous system, and manipulation function. Thus, our collaborative research activities emphasize a firm grounding in the mechanics of finger function, insistence on anatomical detail, and meticulous characterization of muscle activity. This overview of our work on precision pinch (i.e., the ability to produce and control fingertip forces) presents some of our findings around three Research Themes: Mechanics-based quantification of manipulation ability; Anatomically realistic musculoskeletal finger models; and Neural control of finger muscles. I conclude that (i) driving the fingers to some limit of sensorimotor performance is instrumental to elucidating motor control strategies; (ii) that the cross-over of tendons from flexors to extensors in the extensor mechanism is needed to produce force in every direction, and (iii) the anatomical routing of multiarticular muscles makes co-contraction unavoidable for many tasks. Moreover, creating realistic and clinically useful finger models still requires developing new computational means to simulate the viscoelastic tendinous networks of the extensor mechanism, and the muscle–bone–ligament interactions in complex articulations. Building upon this neuromuscular biomechanics paradigm is of immense clinical relevance: it will be instrumental to the development of clinical treatments to preserve and restore manual ability in people suffering from neurological and orthopedic conditions. This understanding will also advance the design and control of robotic hands whose performance lags far behind that of their biological counterparts.
Discovering relative motion patterns in groups of moving point objects

Technological advances in position‐aware devices are leading to a wealth of data documenting motion. The integration of spatio‐temporal data‐mining techniques in GIScience is an important research field to overcome the limitations of static Geographic Information Systems with respect to the emerging volumes of data describing dynamics. This paper presents a generic geographic knowledge discovery approach for exploring the motion of moving point objects, the prime modelling construct to represent GPS tracked animals, people, or vehicles. The approach is based on the concept of geospatial lifelines and presents a formalism for describing different types of lifeline patterns that are generalizable for many application domains. Such lifeline patterns allow the identification and quantification of remarkable individual motion behaviour, events of distinct group motion behaviour, so as to relate the motion of individuals to groups. An application prototype featuring novel data‐mining algorithms has been implemented and tested with two case studies: tracked soccer players and data points representing political entities moving in an abstract ideological space. In both case studies, a set of non‐trivial and meaningful motion patterns could be identified, for instance highlighting the characteristic ‘offside trap’ behaviour in the first case and identifying trendsetting districts anticipating a political transformation in the latter case.
Classes de dynamiques neuronales et correlations structurées par l'experience dans le cortex visuel.

L'activite neuronale est souvent consideree en neuroscience cognitive par la reponse evoquee mais l'essentiel de l'energie consommee par le cerveau permet d'entretenir les dynamiques spontanees des reseaux corticaux. L'utilisation combinee d'algorithmes de classification (K means, arbre hirarchique, SOM) sur des enregistrements intracellulaires du cortex visuel primaire du chat nous permet de definir des classes de dynamiques neuronales et de les comparer l'activite evoquee par un stimulus visuel. Ces dynamiques peuvent etre etudiees sur des systemes simplifies (FitzHugh-Nagumo, systemes dynamiques hybrides, Wilson-Cowan) dont nous presentons l'analyse. Enfin, par des simulations de reseaux composes de colonnes de neurones, un modele du cortex visuel primaire nous permet d'etudier les dynamiques spontanees et leur effet sur la reponse a un stimulus. Apres une periode d'apprentissage pendant laquelle des stimuli visuels sont presentes, des vagues de depolarisation se propagent dans le reseau. L'etude des correlations dans ce reseau montre que les dynamiques spontanees refletent les proprietes fonctionnelles acquises au cours de l'apprentissage.
The Phenomenon of Substance Abuse among the Elderly: Part II: The Mega-Interactive Model

Substance abuse, a major problem a m o n g elderly people , is expec ted to significantly w o r s e n dur ing the 1990s . Thus , hea l thcare prov iders m u s t be better p r e p a r e d to identify and effectively deal with this important area of geriatric care . A n o v e r v i e w of the p h e n o m e n o n of substance abuse a m o n g the elderly w a s presented in Par t I o f this article, wi th a part icular focus on the abuse of alcohol and benzodiazepines , the t w o substances m o s t c o m m o n l y abused b y elderly people . P a r t II presents the Mega-Interact ive Model of Substance A b u s e A m o n g the Elderly ( M I M S A E ) as a f r a m e w o r k for heal thcare prov iders in an effort to assist them to better identify, unders tand, a n d effectively respond to the c o m p l e x a n d g r o w i n g prob lem of substance abuse a m o n g elderly people. By focusing attention on the four interrelated dimensions of this mode l (i.e., e lderly person dimension, substance of abuse d imens ion, societal d imension, a n d t ime dimension) , M I M S A E prov ides heal thcare prov iders wi th a unique and c o m p r e h e n s i v e f r a m e w o r k for effectively deal ing wi th the p h e n o m e n o n of substance abuse in their clinical pract ice and research.
Sentiment Analysis Using Sentiment Features

Sentiment Analysis (SA) or opinion mining has recently become the focus of many researchers, because analysis of online text is beneficial and demanded for market research, scientific surveys from psychological and sociological perspective, political polls, business intelligence, enhancement of online shopping infrastructures, etc. This paper introduces a novel solution to SA of short informal texts with a main focus on Twitter posts known as "tweets". We compare state-of-the-art SA methods against a novel hybrid method. The hybrid method utilizes a Sentiment Lexicon to generate a new set of features to train a linear Support Vector Machine (SVM) classifier. We further illustrate that our hybrid method outperforms the state-of-the-art unigram baseline.
The psychology of problem solving

Part I. Introduction: 1. Recognizing, defining, and representing problems 2. The acquisition of expert performance as problem solving: construction and modification of mediating mechanisms through deliberate practice Part II. Relevant Abilities and Skills: 3. Is success or failure at problem solving complex problems related to intellectual ability? 4. Creativity: a source of difficulty in problem solving 5. Insights about insightful problem solving 6. The role of working memory in problem solving 7. Comprehension of text in problem solving Part III. States and Strategies: 8. Motivating self-regulated problem solvers 9. Feeling and thinking: implications for problem solving 10. The fundamental computational biases of human cognition: heuristics that (sometimes) impair decision making and problem solving 11. Analogical transfer in problem solving Part IV. Conclusions and Integration: 12. Problem-solving, large/small, hard/easy problem-space/problem-solver: the issue of dichotomization.
Formation of nascent secretory vesicles from the trans-Golgi network of endocrine cells is inhibited by tyrosine kinase and phosphatase inhibitors

Recent evidence suggests that secretory vesicle formation from the TGN is regulated by cytosolic signaling pathways involving small GTP- binding proteins, heterotrimeric G proteins, inositol phospholipid metabolism, and protein serine/threonine phosphorylation. At the cell surface, protein phosphorylation and dephosphorylation on tyrosine residues can rapidly modulate cytosolic signaling pathways in response to extracellular stimuli and have been implicated in the internalization and sorting of signaling receptors. to determine if phosphotyrosine metabolism might also regulate secretory vesicle budding from the TGN, we treated permeabilized rat pituitary GH3 cells with inhibitors of either tyrosine phosphatases or tyrosine kinases. We demonstrate that the tyrosine phosphatase inhibitors pervanadate and zinc potently inhibited budding of nascent secretory vesicles. Tyrphostin A25 (TA25) and other tyrosine kinase inhibitors also prevented secretory vesicle release, suggesting that vesicle formation requires both phosphatase and kinase activities. A stimulatory peptide derived from the NH2 terminus of the small GTP-binding protein ADP ribosylation factor 1 (ARF1) antagonized the inhibitory effect of TA25, indicating that both agents influence the same pathway leading to secretory vesicle formation. Antiphosphotyrosine immunoblotting revealed that protein tyrosine phosphorylation was enhanced after treatment with tyrosine phosphatase or kinase inhibitors. Subcellular fractionation identified several tyrosine phosphorylated polypeptides of approximately 175, approximately 130, and 90-110 kD that were enriched in TGN-containing Golgi fractions and tightly membrane associated. The phosphorylation of these polypeptides correlated with inhibition of vesicle budding. Our results suggest that in endocrine cells, protein tyrosine phosphrylation and dephosphorylation are required for secretory vesicle release from the TGN.
A new multichannel access protocol for IEEE 802.11 ad hoc wireless LANs

The IEEE 802.11 wireless local area networks (WLANs) standard supports several equal-capacity communication channels which can be simultaneously shared and accessed by mobile stations. In such multichannel communication system, a mobile station basically can transmit on any of these channels based on a suitable access control protocol. However, with the feature of one transceiver per mobile station, the standard restricts mobile stations to operate in one selected channel and the other channel capacities are wasted inevitably. In this paper, we propose a new carrier sense multiple access (CSMA) based protocol, called multichannel access protocol (MAP), to support parallel transmissions in IEEE 802.11 ad hoc WLANs. To realize the proposed MAP protocol over contemporary ad hoc WLANs, the MAP protocol is not only compliant with the IEEE 802.11 standard but also taking one transceiver constrain into consideration. All mobile stations with MAP will contend for channel access right in a dedicated channel during a periodical contention reservation interval (CRI) and then transmit data frames over different channels by a channel scheduling algorithm (CSA). Given a number of requests, the problem of finding a proper schedule for these requests to be served on a multichannel system so that the longest channel busy period is minimal is known to be NP-hard (Hou, et al. 1994). The time complexity of proposed heuristic CSA is O(|X| log |X| + |X|M/sup 2/) where |X| and M denote the number of successful requests in the CRI and the number of available channels respectively. Simulation results show that the proposed MAP protocol with CSA achieves an obviously higher throughput than conventional IEEE 802.11 WLAN with single channel.
Fingerprint Recognition System Based on Mapping Approach

identification system is mainly consisted of fingerprint achieving, fingerprint classification and fingerprint matching. Fingerprint matching is the key to the system and effects on the precision and efficiency of the whole system directly. Fingerprints are matched mainly based on their fingerprint texture pattern which can be described with the orientation field of fingerprints. A fingerprint, which has the different orientation angle structure in different local area of the fingerprint and has a texture pattern correlation among the neighboring local areas of the fingerprint, can be viewed as a Markov stochastic field. A novel method of fingerprint matching, which is based on embedded Hidden Markov Model (HMM) that is used for modeling the fingerprint's orientation field, is described in this paper. The accurate and robust fingerprint matching can be achieved by matching embedded Hidden Markov Model parameters which were built after the processing of extracting features from a fingerprint, forming the samples of observation vectors and training the embedded Hidden Markov Model parameters.
ADS: A framework for running 5G radio access network in the cloud

In this paper we present the Automatic Distribution System (ADS). ADS is a novel framework that enables quick development and deployment of 5G radio access networks. ADS automatically parallelize radio algorithms and distributes their execution among standard servers in order to fulfill their real-time constraints and minimize power consumption. ADS was at the operational heart of the first large-scale 5G field trial demonstration in the world. The field trial took place in Chengdu and showed a peak performance of 3.6 Gbps downlink throughput.
The State of Social Studies Teacher Preparation: An Analysis of Program Requirements in the United States

What is the current state of social studies TPPs for licensure across the United States? The purpose of this research study is to fill the gap in the academic research, providing an overview of social studies Teacher Preparation Programs (TPPs) across the United States. In order to best answer that question the researchers examined the required pedagogical preparation courses, social science content courses, classroom management courses, and length of student teaching experiences of all public/state university teacher preparation programs in the United States.
Modeling SRAM start-up behavior for Physical Unclonable Functions

One of the emerging technologies for cryptographic key storage is hardware intrinsic security based on Physical Unclonable Functions (PUFs); a PUF is a physical structure of a device that is hard to clone due to its inherent, device-unique and deep-submicron process variations. SRAM PUF is an example of such technology that is becoming popular. So far, only a little is published about modeling and analysis of their start-up values (SUVs). Reproducing the same start-up behavior every time the chip is powered-on is crucial to produce the same cryptographic key. This paper presents an analytical model for SUVs of an SRAM PUF based on Static Noise Margin (SNM), and reports some industrial measurements to validate the model. Simulation of the impact of different sensitivity parameters (such as variation in power supply, temperature, transistor geometry) has been performed. The results show that out of all sensitivity parameters, variation in threshold voltage is the one with the highest impact. Industrial measurements on real memory devices validate the simulation results.
Optimum placement of capacitor in distribution system using a DVR with ANN Technique S

This paper reports concerns of capacitor placement in power distribution systems in the locality of a dynamic voltage restorer (DVR). A feedback controlled simulation model of an actual DVR is developed to perform case studies. Cogency of this model is established by associat ing the waveforms from the model with field measurements. Issues relating to expansion of a DVR in the locality of power factor correction capacitors are analyzed. Some considerations regarding the placement of capacitors in the vicinity of a DVR are drawn based on the simulation models and a novel Artificial Neural Network Based converter had been introduced in the DVR system and location of capacitor is justified.
SIMPL: A DSL for Automatic Specialization of Inference Algorithms

Inference algorithms in probabilistic programming languages (PPLs) can be thought of as interpreters, since an inference algorithm traverses a model given evidence to answer a query. As with interpreters, we can improve the efficiency of inference algorithms by compiling them once the model, evidence and query are known. We present SIMPL, a domain specific language for inference algorithms, which uses this idea in order to automatically specialize annotated inference algorithms. Due to the approach of specialization, unlike a traditional compiler, with SIMPL new inference algorithms can be added easily, and still be optimized using domain-specific information. We evaluate SIMPL and show that partial evaluation gives a 2-6x speedup, caching provides an additional 1-1.5x speedup, and generating C code yields an additional 13-20x speedup, for an overall speedup of 30-150x for several inference algorithms and models.
Image and video retargeting using adaptive scaling function

An image and video retargeting algorithm using an adaptive scaling function is proposed in this work. We first construct an importance map which uses multiple features: gradient, saliency, and motion difference. Then, we determine an adaptive scaling function, which represents a scaling factor of each column in the source image. Finally, the target image is constructed with a weighted average filter using those scaling factors. Moreover, we extend this algorithm to video sequence. Simulation results demonstrate that the proposed algorithm provides better results than conventional retargeting methods.
Detecting late-life depression in Alzheimer’s disease through analysis of speech and language

Alzheimer’s disease (AD) and depression share a number of symptoms, and commonly occur together. Being able to differentiate between these two conditions is critical, as depression is generally treatable. We use linguistic analysis and machine learning to determine whether automated screening algorithms for AD are affected by depression, and to detect when individuals diagnosed with AD are also showing signs of depression. In the first case, we find that our automated AD screening procedure does not show false positives for individuals who have depression but are otherwise healthy. In the second case, we have moderate success in detecting signs of depression in AD (accuracy = 0.658), but we are not able to draw a strong conclusion about the features that are most informative to the classification.
Maternal Behaviors during Pregnancy Impact Offspring Obesity Risk

This study investigated the effects of maternal changes during pregnancy in diet, exercise, and psychosocial factors on offspring weight parameters at birth and 6 months. In overweight/obese (OW/OB; n = 132) mothers, greater % kcal from sweets early in pregnancy was the strongest, independent predictor of higher weight for age (WFA) (beta = 0.19; P = 0.004), higher odds of macrosomia (OR = 1.1 (1.0–1.2); P = 0.004) andWFA >90th percentile at birth (OR = 1.2 (1.1–1.3); P = 0.002) and higher WFA at 6 months (beta = 0.30; P = 0.002). In normal weight (n = 153) mothers, higher intake of soft drinks was the strongest predictor of higher offspring WFA at birth (beta = 0.16; P = 0.04) but not at 6 months. Prenatal physical activity, depressive symptoms, and sleep-related variables did not significantly predict offspring weight outcomes. Mothers' eating behaviors during pregnancy, especially intake of sweets in OW/OB mothers, may have a lasting effect on child weight.
Analog Signal Transmission in a High-Contrast-Gratings-Based Hollow-Core-Waveguide

In this paper, the performance of an on-chip hollow-core-waveguide (HW) using high-contrast gratings (HCG) for analog signal transmission is analyzed numerically. Simulation results indicate that after propagating 100 m in a HCG-HW with optimally designed parameters, there is very little degradation of either third-order intermodulation distortion spur-free dynamic range (IM3 SFDR) or third-order harmonic distortion (THD) SFDR. Due to the chromatic dispersion of the HCG-HW, the highest second-order harmonic distortion (SHD) SFDR is limited to 107.3 dB · Hz1 / 2. In addition, >; 100 dB · Hz 2/3 IM3 SFDR can be achieved over a radio frequency (RF) range of 80 GHz and an optical wavelength bandwidth of 50 nm after propagation 100 m through a HCG-HW. The parameter dependence of the waveguide performance is also investigated. With a ± 20 nm variation on all parameters, the propagation length in an HCG-HW is limited to ~ 6 m in order to maintain an IM3 SFDR of >; 100 dB · Hz 2 / 3.