Miracle-dz/UniFAR-FTUs huggingface dataset:
Extended Neural Metastability in an Embodied Model of Sensorimotor Coupling

The hypothesis that brain organization is based on mechanisms of metastable synchronization in neural assemblies has been popularized during the last decades of neuroscientific research. Nevertheless, the role of body and environment for understanding the functioning of metastable assemblies is frequently dismissed. The main goal of this paper is to investigate the contribution of sensorimotor coupling to neural and behavioral metastability using a minimal computational model of plastic neural ensembles embedded in a robotic agent in a behavioral preference task. Our hypothesis is that, under some conditions, the metastability of the system is not restricted to the brain but extends to the system composed by the interaction of brain, body and environment. We test this idea, comparing an agent in continuous interaction with its environment in a task demanding behavioral flexibility with an equivalent model from the point of view of “internalist neuroscience. ” A statistical characterization of our model and tools from information theory allow us to show how (1) the bidirectional coupling between agent and environment brings the system closer to a regime of criticality and triggers the emergence of additional metastable states which are not found in the brain in isolation but extended to the whole system of sensorimotor interaction, (2) the synaptic plasticity of the agent is fundamental to sustain open structures in the neural controller of the agent flexibly engaging and disengaging different behavioral patterns that sustain sensorimotor metastable states, and (3) these extended metastable states emerge when the agent generates an asymmetrical circular loop of causal interaction with its environment, in which the agent responds to variability of the environment at fast timescales while acting over the environment at slow timescales, suggesting the constitution of the agent as an autonomous entity actively modulating its sensorimotor coupling with the world. We conclude with a reflection about how our results contribute in a more general way to current progress in neuroscientific research.
Long-range enhancer–promoter interactions in the Scr-Antp interval of the Drosophila Antennapedia complex

Long-range enhancer–promoter interactions are commonly seen in complex genetic loci such as Hox genes and globin genes. In the case of the Drosophila Antennapedia complex, the T1 enhancer bypasses the neighboring ftz gene and interacts with the distant Scr promoter to activate expression in posterior head segments. Previous studies identified a 450-bp promoter-proximal sequence, the tethering element, which is essential for T1–Scr interactions. To obtain a more comprehensive view of how individual enhancers selectively interact with appropriate target genes, we used bioinformatic methods to identify new cis-regulatory DNAs in the ≈50-kb Scr-Antp interval. Three previously uncharacterized regulatory elements were identified: a distal T1 tethering sequence mapping >40 kb from the proximal tethering sequence, a repressor element that excludes activation of Scr by inappropriate enhancers, and a new ftz enhancer that directs expression within the limits of stripes 1 and 5. Many of the regulatory DNAs in the Scr-Antp interval are transcribed, including the proximal and distal tethering elements. We suggest that homotypic interactions between the tethering elements stabilize long-range T1–Scr interactions during development.
Daily load forecasting based on rough sets and relevance vector machine

Relevance vector machine (RVM) is a novel kernel methods based on Bayesian frame. It is similar to support vector machine (SVM), but it needs much less learning parameters to adjust and has a better forecast accuracy. However, due to the fact that the RVM is not suitable for the forecast task with large sample dataset, the application of RVM in daily load forecasting is limited. This paper proposes a daily load forecasting model based on rough sets and RVM. This model takes load data and weather information as condition attributes, classifies the historical load dataset into 24 hourly sub-dataset, utilizes the theory of rough sets for each sub-dataset to obtain the reduced condition attribute which is used as the input of the RVM model, thus avoids the blindness in building the load forecast model. Therefore the issue of daily load forecasting is transformed into the issue of small sample forecast. In experiments, this model illustrates a high practicability and forecast accuracy.
Performance study of cooperative diversity schemes for Wireless Sensor Networks based on UltraWideBand

Ultra Wide Band (UWB) technology is nowadays emerging as a strong candidate for indoor wireless applications, thanks to its low power consumption and high data rates. On the other hand, the use of multiple antennas in transmission and/or reception aims to achieve spatial diversity. It offers significant increases in data throughput without additional bandwidth or transmitted power. In Wireless Sensor Networks (WSNs) the use of several transmitting or receiving elements is highly limited due to size constraints and complexity. In this scenario the idea of cooperative diversity appears as an alternative to the deployment of several antennas in the nodes. In this paper, we analyze and compare several cooperative diversity schemes for WSN using IR-UWB techniques in terms of error probability for different scenarios with multipath fading.
Prediction of Stock Market Shift using Sentiment Analysis of Twitter Feeds, Clustering and Ranking

The stock market is fluctuating constantly. The rise and fall in stock prices are seemingly random. However, this is not so. Even a minute happening in the company can have a huge effect on the stock price. As each investor buys and sells the stock, the price rises and falls depending on the sale and purchase, the demand and supply. Whether or not an investor buys a particular company's stock is based on his knowledge and impression of the company. The latter is what we will employ to decide whether or not to buy a certain company's stock at the current price. There are 6 accepted discrete moods. Millions of people tweet every second. A fairly accurate prediction and analysis of the tweet's underlying mood can be made using sentiment analysis. Each word has a certain grammatical signature that tells us which mood it belongs to. Depending on what the users are feeling about a company as they tweet about it this engine will decide whether or not one should buy stocks of that company. This paper describes how to map this mood with market sentiment and in turn with prediction of rise/fall of stock prices.
The Online Cooperating View Maintenance Based on Source View Increment

View maintenance is an important field in the research of dataware house. This paper provides an online view self-maintenance method based on source view's increment to keep the materialized view consistent with the data source. We use the cooperation between the integrator at warehouse and the monitor at data source to maintenance the view, and the method does not require to query back to data source, it can accelerate view maintenance and lessen the burden of communication between dataware house and data sources.
Multiple Voltage Vectors Control Strategy for the Direct Torque Control Using Matrix Converters

The electromagnetic torque ripple is one of the most important drawbacks of the Direct Torque Control for Induction Motors using Matrix Converters at low speed. This paper investigate the use of multiple voltage vectors of Matrix Converters to reduce the electromagnetic torque ripple which appears when Direct Torque Control technique is used in Induction Motors. The standard look up table for Direct Torque Control is improved in order to include the multiple voltage vectors (long voltage vectors, short voltage vectors and zero voltage vectors). A hysteresis comparator with five-level output is proposed to determine the small and large torque errors and consequently the electromagnetic torque ripple is reduced. Finally, the simulation results have verified the feasibilities of this novel control strategy.
Deposition of piezoelectric AlN thin film on diamond substrate for SHF SAW devices

The preparation of piezoelectric AIN thin films of high quality onto diamond substrates using DC-TFTS method and its application to SHF SAW devices are discussed. FWHM (=3.4deg) of the rocking curve of XRD suggested that the c-axes of AIN films may well align perpendicular to the film surface. Transversal filters based on Sezawa wave were fabricated to verify whether the prepared AIN/diamond structure can be applied to low loss SAW devices in an SHF range. The propagation loss was experimentally estimated to be 0.08 dB/lambda at the centre frequency of 6.12 GHz. Further reduction in the propagation loss was attempted by using small-grain-diamond substrates.
Hybrid active learning for cross-domain video concept detection

Cross-domain video concept detection is a challenging task due to the distribution difference between the source domain and target domain. In order to avoid expensive labeling the target-domain data, Active Learning can be used to incrementally learn a target classifier by reusing the one in the source domain. It uses a discriminative query strategy and picks the most ambiguous samples to label, which could fail if the distribution difference is too large. In this paper, to deal with large difference in data distributions, we propose a generative query strategy which is then combined with the existing discriminative one to yield a hybrid method. This method adaptively fits the distribution differences and gives a mixture strategy that performs more robustly compared to both single strategies. Experimental results on TRECVID semantic concept detection task demonstrate superior performance of our hybrid method.
A phonological representation in the infant brain

IN adults, neural networks for phonological processing distinct from those involved in acoustical processing are located in the left temporal lobe. We now report that by the age of 3 months, infants display phonological processing devices analogous to those found in adults. Within a stream of identical syllables, acoustic deviants were introduced, either crossing a phonetic boundary or remaining within the same category. Event-related potentials were recorded using a 64-electrode net. Although the acoustical change was of similar amplitude in the two deviants, the electrophysiological response was larger for a phonological change and involved a more posterior and dorsal temporal region than for an acoustical change. These results demonstrate that infants, like adults, already possess a dedicated neuronal network for phonetic processing.
Planet formation bursts at the borders of the dead zone in 2D numerical simulations of circumstellar disks

Context. As accretion in protoplanetary disks is enabled by turbulent viscosity, the border between active and inactive (dead) zones constitutes a location where there is an abrupt change in the accretion flow. The gas accumulation that ensues triggers the Rossby wave instability, which in turn saturates into anticyclonic vortices. It has been suggested that the trapping of solids within them leads to a burst of planet formation on very short timescales. Aims. We study in the formation and evolution of the vortices in greater detail, focusing on the implications for the dynamics of embedded solid particles and planet formation. Methods. We performed two-dimensional global simulations of the dynamics of gas and solids in a non-magnetized thin protoplanetary disk with the Pencil code. We used multiple particle species of radius 1, 10, 30, and 100 cm. We computed the particles' gravitational interaction by a particle-mesh method, translating the particles' number density into surface density and computing the corresponding self-gravitational potential via fast Fourier transforms. The dead zone is modeled as a region of low viscosity. Adiabatic and locally isothermal equations of state are used. Results. The Rossby wave instability is triggered under a variety of conditions, thus making vortex formation a robust process. Inside the vortices, fast accumulation of solids occurs and the particles collapse into objects of planetary mass on timescales as short as five orbits. Because the drag force is size-dependent, aerodynamical sorting ensues within the vortical motion, and the first bound structures formed are composed primarily of similarly-sized particles. In addition to erosion due to ram pressure, we identify gas tides from the massive vortices as a disrupting agent of formed protoplanetary embryos. We find evidence that the backreaction of the drag force from the particles onto the gas modifies the evolution of the Rossby wave instability, with vortices being launched only at later times if this term is excluded from the momentum equation. Even though the gas is not initially gravitationally unstable, the vortices can grow to Q ≈ 1 in locally isothermal runs, which halts the inverse cascade of energy towards smaller wavenumbers. As a result, vortices in models without self-gravity tend to rapidly merge towards a $m = 2$ or $m =1 $ mode, while models with self-gravity retain dominant higher order modes ($m = 4$ or $m = 3$) for longer times. Non-selfgravitating disks thus show fewer and stronger vortices. We also estimate the collisional velocity history of the particles that compose the most massive embryo by the end of the simulation, finding that the vast majority of them never experienced a collision with another particle at speeds faster than 1 m s -1 . This result lends further support to previous studies showing that vortices provide a favorable environment for planet formation.
Learning a$ne transformations

Under the assumption of weak perspective, two views of the same planar object are related through an a$ne transformation. In this paper, we consider the problem of training a simple neural network to learn to predict the parameters of the a$ne transformation. Although the proposed scheme has similarities with other neural network schemes, its practical advantages are more profound. First of all, the views used to train the neural network are not obtained by taking pictures of the object from di! erent viewpoints. Instead, the training views are obtained by sampling the space of a$ne transformed views of the object. This space is constructed using a single view of the object. Fundamental to this procedure is a methodology, based on singular-value decomposition (SVD) and interval arithmetic (IA), for estimating the ranges of values that the parameters of a$ne transformation can assume. Second, the accuracy of the proposed scheme is very close to that of a traditional least squares approach with slightly better space and time requirements. A front-end stage to the neural network, based on principal components analysis (PCA), shows to increase its noise tolerance dramatically and also to guides us in deciding how many training views are necessary in order for the network to learn a good, noise tolerant, mapping. The proposed approach has been tested using both arti"cial and real data. ( 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Applying symmetric and asymmetric key algorithms for the security in wireless networks: proof of correctness

In a wireless network, open and wireless communication channels set the restriction of wired connections free, because wireless signal acts as the platform of data transmission. On the other hand, the open and shared nature of wireless networks also causes a number of security concerns. In particular, data confidentiality is one of the most important issues. As usual, cryptosystems are adopted to protect the data confidentiality and integrity, including symmetric and asymmetric encryption algorithms. However, due to the limitations of these algorithms, how to apply them to wireless and mobile networks is always a debatable topic. In this paper, we explore the issue of applying both symmetric and asymmetric key algorithms for data encryption and authentication. Then we propose an advisable solution for the application of these cryptographic algorithms, which not only takes advantage of symmetric key to provide the effective protection of data transmission, but also employs asymmetric key to authenticate a node reliably. We formalize the security analysis and show that our system owns effective and provable secure attributes based on rational combination of classic encryption algorithms. Thus, we avoid the weakness of these two cryptosystems and make use of their advantages to establish a secure environment, by the combination of symmetric and asymmetric key algorithms.
Automatically annotating textual resources with human intentions

Annotations represent an increasingly popular means for organizing, categorizing and finding resources on the "social" web. Yet, only a small portion of the total resources available on the web are annotated. In this paper, we describe a prototype - iTAG - for automatically annotating textual resources with human intent, a novel dimension of tagging. We investigate the extent to which the automatic analysis of human intentions in textual resources is feasible. To address this question, we present selected evidence from a study aiming to automatically annotate intent in a simplified setting, that is transcripts of speeches given by US presidential candidates in 2008.
A Full $Ku$ -Band Reduced-Height Waveguide-to-Microstrip Transition With a Short Transition Length

A systematic design of a full Ku -band reduced-height waveguide-to-microstrip transition using commercial High Frequency Structure Simulator software is presented. The basic transition is composed of a shorted microstrip probe to a 50- Ω ridge waveguide. An impedance transformer necessary to adapt the 50-Ω ridge waveguide to a reduced-height waveguide was designed using multistage short-length ridge waveguides connected in cascade. The resulting transition length is 6.2 mm. The separately designed shorted microstrip probe transition and impedance transformer were combined and adjusted for full-Ku-band operation. The combined transition was fabricated and measured. The measured insertion and return losses are about 0.41 dB and below 15 dB for the full-Ku-band, respectively.
EFFECT OF NODE TRANSITION PROBABILITY BASED ROUTING PROTOCOL ON MEDIA ACCESS CONTROL PROTOCOLS IN MOBILE AD HOC NETWORKS

In this paper, we study the effect of routing protocols on the performance of Media Access Control (MAC) protocols in Mobile Ad hoc Networks (MANET). Three well known MAC protocols such as 802.11, Carrier Sense Multiple Access (CSMA) and Multiple Access with Collision Avoidance (MACA) are considered for performance study. The main focus of this paper was to study how the Node Transition Probability based Routing protocols, which was proposed by us, affect the performance of the MAC protocols when the underlying network and traffic parameters are varied. The performance of the protocols was measured with respect to number of packets received, average delay of each packet, throughput and number of control packets. The experimental analysis was carried out using GloMosim simulator. Our results shows that the performance of Node Transition Probability based Routing (NTPR) under different MAC protocol tends to vary and performs better under IEEE 802.11. Finally, we conclude by saying this observation also emphasizes the critical need for studying interactions between protocol layers when designing wireless networks protocols.
A Survey of Opinion Mining and Sentiment Analysis

A huge amount of online information, rich web resources are highly unstructured and such natural language are not solvable by machine directly. The increased demand to capture opinions of general public about social events, campaigns and sales of the product has led to study of the field opinion mining and sentiment analysis. Opinion refers to extraction of lines in raw data which expresses an opinion. Sentiment analysis identifies polarity of extracted opinions. The major challenge lies in analyzing the sentiments and identifying emotions expressed in texts. This paper presents a survey which covers a problem of sentiment analysis, techniques and methods used for the same.
Intervention Program To Reduce The Level Of Test Anxiety In A Primary School Class. A Pilot Study

The aim of study is to evaluate the effectiveness of an intervention program to reduce the level of test anxiety in a primary school class in the urban environment (N = 25, 2nd class). The level of test anxiety was evaluated using the Test Anxiety in Children, developed by Wren and Benson in 2004. The intervention program took place over a period of six weeks with a frequency of three times a week, consisting of six activities implemented in school in the personal development discipline with the help of the formative stories created for the purpose of the program, six activities based on independent work in solving some assessment task of the Language and Communication and Mathematics disciplines of didactic auxiliaries in order to prepare pupils for the Second Class National Assessment and six self-employed activities in homework completion after school classes. The program's effectiveness was performed by analyzing the two-stage results of the test using the intragroup comparison test. The results obtained lead to the conclusion that the level of test anxiety in second-class students in the experimental group is lower in the posttest (t = 2,328, p = 0,029), and the magnitude of the effect obtained indicates a moderate value. The results on subscales indicate statistically significant differences in the thought subscale (t = 2.54, p = 0.027) with lower posttest scores, and the effect size is moderate too.
A study on the relationship between resilience and forgiveness

Background : We are living in a world grappled with issues of terrorist attacks, racial discrimination, manmade or natural disasters, hunger, poverty and many more. Can forgiveness be the answer to build resilience to deal with such adversities? Forgiveness means a conscious decision to release the feelings of resentment and vengeance towards the offender. Resilience is the process of adapting well in the face of adversity, stress, trauma or sources of significant stress. Many studies have shown that primary factor in influencing Resilience ishaving caring and supportive relationships. Methodology : The purpose of the study was to explore relationship between forgiveness and resilience. 95 undergraduate female college students participated in the survey. Students were given self report questionnaires that included Wagnild and Young’s Resilience Scale, and the Heartland Forgiveness Scale. The Pearson’s product moment correlation was done to find out the association between the two variables. Results : The results indicated a significant relationship between self-forgiveness and resilience (p < 0.01), forgiving others and resilience (p < 0.01), forgiveness of situations and resilience (p < 0.05) and overall forgiveness with resilience (p < 0.05). Conclusions : There is definite relationship between resilience and forgiveness as well as self forgiveness. These results may be applied in creating modules for personal counseling and couples therapy to build resilience through inculcating trait forgiveness.
Emotions and Prosody in Dialogues: An Algebraic Approach Based on User Modelling

The model of the dialogue based on dialogue automata presented in the paper relates internal states of the participant of a dialogue with the attributes of dialogue utterances by means of transition and output functions. Consequently, this model relates emotions (that are described by attributes of the internal states) with prosody (which is described by attributes of the dialogue utterances) and other quantities that are entering the model. This approach can be used for predicting the behaviour of the user as well as determining unknown attribute value in the context of dialogue.