Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Interval Timing by Recurrent Neural Nets

The purpose of this study was to take a new approach in showing how the central nervous system might encode time at the supra-second level using recurrent neural nets (RNNs). This approach utilizes units with a delayed feedback, whose feedback weight determines the temporal properties of specific neurons in the network architecture. When these feedback neurons are coupled, they form a multilayered dynamical system that can be used to model temporal responses to steps of input in multidimensional systems. The timing network was implemented using separate recurrent “Go” and “No-Go” neural processing units to process an individual stimulus indicating the time of reward availability. Outputs from these distinct units on each time step are converted to a pulse reflecting a weighted sum of the separate Go and No-Go signals. This output pulse then drives an integrator unit, whose feedback weight and input weights shape the pulse distribution. This system was used to model empirical data from rodents performing in an instrumental “peak interval timing” task for two stimuli, Tone and Flash. For each of these stimuli, reward availability was signaled after different times from stimulus onset during training. Rodent performance was assessed on non-rewarded trials, following training, with each stimulus tested individually and simultaneously in a stimulus compound. The associated weights in the Go/No-Go network were trained using experimental data showing the mean distribution of bar press rates across an 80 s period in which a tone stimulus signaled reward after 5 s and a flash stimulus after 30 s from stimulus onset. Different Go/No-Go systems were used for each stimulus, but the weighted output of each fed into a final recurrent integrator unit, whose weights were unmodifiable. The recurrent neural net (RNN) model was implemented using Matlab and Matlab’s machine learning tools were utilized to train the network using the data from non-rewarded trials. The neural net output accurately fit the temporal distribution of tone and flash-initiated bar press data. Furthermore, a “Temporal Averaging” effect was also obtained when the flash and tone stimuli were combined. These results indicated that the system combining tone and flash responses were not superposed as in a linear system, but that there was a non-linearity, which interacted between tone and flash. In order to achieve an accurate fit to the empirical averaging data it was necessary to implement non-linear “saliency functions” that limited the output signal of each stimulus to the final integrator when the other was co-present. The model suggests that the central nervous system encodes timing generation as a dynamical system whose timing properties are embedded in the connection weights of the system. In this way, event timing is coded similar to the way other sensory-motor systems, such as the vestibulo-ocular and optokinetic systems, which combine sensory inputs from the vestibular and visual systems to generate the temporal aspects of compensatory eye movements.
Gathering Entropy at Large Scale with HAVEGE and BlobSeer

Large sequences of random information are the foundation for a large class of applications: security, online gambling games, large scale Monte-Carlo simulations, etc. Many such applications are distributed and run on large-scale infrastructures such as clouds and grids. In this context, the random generator plays a crucial role: it needs to achieve a high entropy, a high throughput and last but not least a high degree of security. Several ways to generate high-entropy random information securely exist. For example, HAVEGE generates random information by gathering entropy from internal processor states of the machine where it is running alongside the user applications. These internal states are inheritably volatile and impossible to tamper with in a controlled fashion by the applications running on it. A centralized approach however does not scale to the high throughput requirement in a large scale setting. In order to do so, the output of several such instances needs to be combined into a single output stream. While this certainly has a good potential to solve the high throughput requirement, the way the outputs of the instances are combined in a single stream becomes a new weak link that can negatively impact all three requirements and therefore has to be addressed properly. In this paper we propose a distributed random number generator that efficiently addresses the aforementioned issue. We introduce a series of mechanisms to preserve a high entropy and degree of security for the combined output result and implement them on top of BlobSeer, a data storage service specifically designed to offer a high throughput in large-scale deployments even under heavy access concurrency. Large-scale experiments were performed on the G5K testbed and demonstrate substantial benefits for our approach.
Larger Golomb Rulers

We present the construction of possibly-optimal Golomb rulers through a size of 40,000 marks, which provides support for the existence of subquadratic Golomb rulers for all sizes. Then we explore the subquadratic question at more length, finding a first gap at 492,116 through 492,118 at which sizes subquadratic Golomb rulers may not exist. Finally, we explore some even larger rulers, looking for those with a maximum quality (a measure of how much better than quadratic they are). Our results are strictly computational in nature, but derive from performance improvements to the known algorithms implementing construction methods based
Visual Hand Tracking and Gesture Analysis

The emergence of new display and computing technologies have brought attention to new paradigms of human-computer interactions that are fundamentally different from using traditional mechanical input devices. Communication through visual gesture interface is a potential alternative that could provide multidimensional interactions. Though rich information is embedded in video input, so is a large amount of noise and uncertainties. Extracting correct and useful information from a video is a challenging problem, and this makes capturing the human hand motion an extremely difficult task. This dissertation presents novel techniques to effectively and efficiently track articulate hand motion in a video sequence. Since tracking articulate hand motion is equivalent to a search in a high-dimensional space, an analysis of the feasible configuration space is given, and semiparametric and nonparametric representations are proposed to model the motion constraint. For each of the representations, a corresponding efficient tracking algorithm is proposed. Experiments have shown the effectiveness of these approaches. To fully automate the tracking system, this dissertation also presents an algorithm for automatic initialization which can be done in real time. Based on this result, several vision-based gesture interfaces are built.
Deep Embedded Clustering With Adversarial Distribution Adaptation

Clustering methods based on deep neural networks have been extensively studied and applied in data mining. Most existing unsupervised deep embedded clustering methods jointly learn feature representations and cluster assignments with deep neural networks. However, to the best of our knowledge, most of them ignore the relevance between representation learning and clustering, so that fail to couple feature representation learning and cluster assignments effectively, which leads to non-representative features for clustering and this in turn hurts clustering performance. In this paper, we propose Adversarial Deep Embedded Clustering (ADEC), a novel unsupervised clustering method based on adversarial auto-encoder (AAE) and $k$ -means clustering method. Specifically, ADEC matches distribution of feature representations with the given prior distribution to preserve the data structure with AAE, and $k$ -means based on distribution distance metrics is conducted for clustering with these feature representations, simultaneously. ADEC optimizes a clustering objective iteratively with backpropagation algorithm in learning AAE from data space to feature space. Extensive experiments have demonstrated the effectiveness of our proposed method on images clustering compared to several strong baseline methods.
Subdivisional Growth Logics

This research explores the combination of two different types of algorithms that have so far been treated separately in architectural computational design: recursive subdivision, and differential or cellular growth. The two algorithms appear to act in opposite directions, the first is a refinement going inwards while the latter is growing outwards. However, both algorithms are based on the refinement of mesh geometries by inserting new vertices and faces and can be used in combination. The resulting subdivisional growth can be used to enhance specific geometric traits of either recursive subdivision or cellular growth at different scales or in different sections of the design object. The resulting geometries have been explored through case studies that utilize those possibilities.
Simultaneous Detection of Parasitic Vector Borne Diseases: A Robust Cross-Sectional Survey in Hunting, Stray and Sheep Dogs in a Mediterranean Area

Canine vector-borne diseases (CVBDs) are a spectrum of diseases caused by different pathogens transmitted by blood-feeding arthropoda. The aim of this study was to investigate leishmaniosis, babesiosis, and filarial infections in dogs with three different lifestyles (hunting, stray, and sheep dogs) in Molise, the smallest region of southern Italy, where data available about these parasitic infections are very scant. A cross-sectional survey was conducted on 318 hunting, 180 stray, and 218 sheep dogs. Immunofluorescence antibody test, blood smear, molecular techniques and Knott's test were performed to detect Leishmania infantum, Babesia spp. and filarial nematodes. Association between positivity to CVBDs, age, sex, and living conditions was evaluated. An overall prevalence of 12.3% of CVBDs caused by L. infantum (10.2%), B. canis canis (0.3%) and filarial nematodes (2.1%) was detected. Three dogs showed co-infections of L. infantum and B. c. canis (0.1%) or Acanthocheilonema reconditum (0.3%). A significantly association was found only for filarial infection in hunting dogs. These parasites were reported also in dogs without clinical signs. It is very important to plan effective control programs for CVBDs to guarantee not only the health and welfare of pets, but also the public safety, because some of mentioned parasites are of zoonotic importance.
Real-Time Streaming Point Cloud Compression for 3D LiDAR Sensor Using U-Net

Point cloud data from LiDAR sensors is the currently the basis of most L4 autonomous driving systems. Sharing and storing point clouds will also be important for future applications, such as accident investigation or V2V/V2X networks. Due to the huge volume of data involved, storing point clouds collected over long periods of time and transmitting point clouds in real-time are difficult tasks, making compression an indispensable step before storing or transmitting. Previous streaming point cloud compression methods, such as octree compression or video compression-based approaches, have difficulty compressing this data in real-time into very small volumes with low information loss. To reduce temporal redundancy efficiently and rapidly, in this paper we propose a real-time streaming point cloud data compression method using U-net. By utilizing raw packet data from LiDAR sensors, we can store 3D point cloud information losslessly in a 2D matrix, and convert streaming point cloud data into a video-like format. By designating some frames as reference frames and then using U-net to interpolate the remaining LiDAR frames, we can greatly reduce temporal redundancy. Our use of U-net was inspired by a video interpolation approach employed in another study. Noise in LiDAR data is a big issue which significantly affects network training and compression results. In this paper, we propose a padding strategy to alleviate the negative impact of this noise. As a result of these improvements, our proposed method can outperform octree compression, MPEG-based compression and our previously proposed SLAM-based compression method.
Taxonomy, Diet, and Developmental Stage Contribute to the Structuring of Gut-Associated Bacterial Communities in Tephritid Pest Species

Insect-symbiont interactions are receiving much attention in the last years. Symbiotic communities have been found to influence a variety of parameters regarding their host physiology and fitness. Gut symbiotic communities can be dynamic, changing through time and developmental stage. Whether these changes represent real differential needs and preferential relationships has not been addressed yet. In this study, we characterized the structure of symbiotic communities of five laboratory populations that represent five Tephritidae species that are targets for pest control management through the sterile insect technique (SIT), namely Bactrocera oleae, Anastrepha grandis, Anastrepha ludens, and two morphotypes of Anastrepha fraterculus (sp.1 and the Andean lineage). These populations are under artificial or semi artificial rearing conditions and their characterization was performed for different developmental stages and age. Our results demonstrate the presence of a symbiotic community comprising mainly from different Enterobacteriaceae genera. These communities are dynamic across developmental stages, although not highly variable, and appear to have a species-specific profile. Additional factors may contribute to the observed structuring, including diet, rearing practices, and the degree of domestication. Comparison of these results with those derived from natural populations could shed light to changes occurring in the symbiotic level during domestication of Tephritidae populations. Further studies will elucidate whether the changes are associated with modification of the behavior in laboratory strains and assess their effects in the quality of the mass rearing insects. This could be beneficial for improving environmentally friendly, species-specific, pest control methods, such as the SIT.
Dark Energy Constraints on RedShift-Based f ( R ) Gravity

We have studied cosmological dynamics in f(R) gravity theory via cosmographic parameters. We have changed variables of field equations from time to red-shift and solved the achieved differential equation analytically for f(R). Then we have used Taylor expansion to find general form of f(R) function around the present day value of scalar curvature. By introducing F(z) = df(R)/dR we would simplify our calculations; if we consider F(z) as a given function we would restrict our answers of f(R). In this paper we offer a linear form of F(z) = 1 + αz which leads us to a specific f(R) function, where α is a constant which depends on the present day value of deceleration parameter. As an example, using Taylor expansion coefficients, we have compared our analytically calculated function with reconstructedf(R) function for Dark Energymodels. To reconstructf(R) action for Dark Energymodels, we have used correspondingH(z) of each Dark Energy model for calculating Taylor expansion coefficients. As our F(z) function is linear, the Taylor expansion coefficients would be a function of present day value of deceleration parameter.
Autonomous localisation of rovers for future planetary exploration

Future Mars exploration missions will have increasingly ambitious goals compared to current rover and lander missions. There will be a need for extremely long distance traverses over shorter periods of time. This will allow more varied and complex scientific tasks to be performed and increase the overall value of the missions. The missions may also include a sample return component, where items collected on the surface will be returned to a cache in order to be returned to Earth, for further study. In order to make these missions feasible, future rover platforms will require increased levels of autonomy, allowing them to operate without heavy reliance on a terrestrial ground station. Being able to autonomously localise the rover is an important element in increasing the rover's capability to independently explore. This thesis develops a Planetary Monocular Simultaneous Localisation And Mapping (PM-SLAM) system aimed specifically at a planetary exploration context. The system uses a novel modular feature detection and tracking algorithm called hybrid-saliency in order to achieve robust tracking, while maintaining low computational complexity in the SLAM filter. The hybrid saliency technique uses a combination of cognitive inspired saliency features with point-based feature descriptors as input to the SLAM filter. The system was tested on simulated datasets generated using the Planetary, Asteroid and Natural scene Generation Utility (PANGU) as well as two real world datasets which closely approximated images from a planetary environment. The system was shown to provide a higher accuracy of localisation estimate than a state-of-the-art VO system tested on the same data set. In order to be able to localise the rover absolutely, further techniques are investigated which attempt to determine the rover's position in orbital maps. Orbiter Mask Matching uses point-based features detected by the rover to associate descriptors with large features extracted from orbital imagery and stored in the rover memory prior the mission launch. A proof of concept is evaluated using a PANGU simulated boulder field.
Using a Multiplex Nucleic Acid in situ Hybridization Technique to Determine HCN4 mRNA Expression in the Adult Rodent Brain

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels carry a non-selective cationic conductance, Ih, which is important for modulating neuron excitability. Four genes (HCN1-4) encode HCN channels, with each gene having distinct expression and biophysical profiles. Here we use multiplex nucleic acid in situ hybridization to determine HCN4 mRNA expression within the adult mouse brain. We take advantage of this approach to detect HCN4 mRNA simultaneously with either HCN1 or HCN2 mRNA and markers of excitatory (VGlut-positive) and inhibitory (VGat-positive) neurons, which was not previously reported. We have developed a Fiji-based analysis code that enables quantification of mRNA expression within identified cell bodies. The highest HCN4 mRNA expression was found in the habenula (medial and lateral) and the thalamus. HCN4 mRNA was particularly high in the medial habenula with essentially no co-expression of HCN1 or HCN2 mRNA. An absence of Ih-mediated “sag” in neurons recorded from the medial habenula of knockout mice confirmed that HCN4 channels are the predominant subtype in this region. Analysis in the thalamus revealed HCN4 mRNA in VGlut2-positive excitatory neurons that was always co-expressed with HCN2 mRNA. In contrast, HCN4 mRNA was undetectable in the nucleus reticularis. HCN4 mRNA expression was high in a subset of VGat-positive cells in the globus pallidus external. The majority of these neurons co-expressed HCN2 mRNA while a smaller subset also co-expressed HCN1 mRNA. In the striatum, a small subset of large cells which are likely to be giant cholinergic interneurons co-expressed high levels of HCN4 and HCN2 mRNA. The amygdala, cortex and hippocampus expressed low levels of HCN4 mRNA. This study highlights the heterogeneity of HCN4 mRNA expression in the brain and provides a morphological framework on which to better investigate the functional roles of HCN4 channels.
Synergistic Graph and SQL Analytics Inside IBM Db2

To meet the challenge of analyzing rapidly growing graph and network data created by modern applications, a large number of specialized graph databases have emerged, such as Neo4j, JanusGraph, and Sqlg. At the same time, RDBMSs and SQL continue to support mission-critical business analytics. However, real-life analytical applications seldom contain only one type of analytics. They are often made of heterogeneous workloads, including SQL, machine learning, graph, and other analytics. In particular, SQL and graph analytics are usually accompanied together in one analytical workload. This means that graph and SQL analytics need to be synergistic with each other. Unfortunately, most existing graph databases are standalone and cannot easily integrate with relational databases. In addition, as a matter of fact, many graph data (data about relationships be-tween objects or people) are already prevalent in relational databases, although they are not explicitly stored as graphs. Performing graph analytics on these relational graph data today requires exporting large amount of data to the specialized graph databases. A natural question arises: can SQL and graph analytics be performed synergistically in a same system? In this demo, we present such a working sys-tem called IBM Db2 Graph. Db2 Graph is an in-DBMS graph query approach. It is implemented as a layer inside an experimental IBM Db2 ™ , and thus can support synergistic graph and SQL analytics eﬃciently. Db2 Graph employs a graph overlay approach to expose a graph view of the relational data. This approach ﬂexibly retroﬁts graph queries to existing graph data stored in relational tables. We use an example scenario on health insurance claim analysis to demonstrate how Db2 Graph is used to support synergistic graph and SQL analytics inside Db2.
Credit : 1 PDH Course Title : Smart Brain Interaction Systems for Office Access and Control in Smart City Context 3 Easy Steps to Complete the Course : 1

Over the past decade, the term “smart cities” has been worldwide priority for city plan‐ ning by governments. Planning smart cities implies identifying key drivers for trans‐ forming into more convenient, comfortable, and safer life. This requires equipping the cities with appropriate smart technologies and infrastructure. Smart infrastructure is a key component in planning smart cities: smart places, transportation, health and educa‐ tion systems. Smart offices present the concept of workplaces that respond to user’s needs and allow less commitment to routine tasks. Smart offices solutions enable employees to change status of the surrounding environment upon the change of user’s preferences using the changes in the user’s biometrics measures. Meanwhile, smart office access and control through brain signals is quite recent concept. Hence, smart offices provide access and services availability at each moment using smart personal identification (PI) interfaces that responds only to the personal thoughts/preferences issued by the office employee not any other person. Hence, authentication and control systems could benefit from the biometrics. Yet these systems are facing efficiency and accessibility challenges in terms of unimodality. This chapter addresses those problems and proposes a prototype for multimodal biometric person identification control system for smart office access and control as a solution.