Miracle-dz/UniFAR-FTUs huggingface dataset:
Ketamine and Ceftriaxone-Induced Alterations in Glutamate Levels Do Not Impact the Specific Binding of Metabotropic Glutamate Receptor Subtype 5 Radioligand [18F]PSS232 in the Rat Brain

Several studies showed that [11C]ABP688 binding is altered following drug-induced perturbation of glutamate levels in brains of humans, non-human primates and rats. We evaluated whether the fluorinated derivative [18F]PSS232 can be used to assess metabotropic glutamate receptor 5 (mGluR5) availability in rats after pharmacological challenge with ketamine, known to increase glutamate, or ceftriaxone, known to decrease glutamate. In vitro autoradiography was performed on rat brain slices with [18F]PSS232 to prove direct competition of the drugs for mGluR5. One group of rats were challenged with a bolus injection of either vehicle, racemic ketamine, S-ketamine or ceftriaxone followed by positron emission tomography PET imaging with [18F]PSS232. The other group received an infusion of the drugs during the PET scan. Distribution volume ratios (DVRs) were calculated using a reference tissue model. In vitro autoradiography showed no direct competition of the drugs with [18F]PSS232 for the allosteric binding site of mGluR5. DVRs of [18F]PSS232 binding in vivo did not change in any brain region neither after bolus injection nor after infusion. We conclude that [18F]PSS232 has utility for measuring mGluR5 density or occupancy of the allosteric site in vivo, but it cannot be used to measure in vivo fluctuations of glutamate levels in the rat brain.
Spoof-of-Work - Evaluating Device Authorisation in Mobile Mining Processes

Mobile mining of cryptocurrencies, without relying on CPU-heavy computations, is a novel attempt to foster adoption of a token. However, this approach leaves room for attacks. In this paper, we perform a thorough analysis of Electroneum, one of the first cryptocurrencies to introduce a mobile mining process. We show that mobile mining, without relying on a consensus algorithm (e.g. Proof-Of-Work), is not feasible on current generation Android smartphones. We further demonstrate that the security mechanisms employed by Electroneum can be circumvented and that mobile mining can be exploited successfully. Based on this analysis, we discuss several practical countermeasures, which can be applied on smartphones to enforce device authorisation and prevent
Segment Routing Single Link Failure Congestion Optimization

Segment routing is an implementation of the source routing paradigm built over an Interior Gateway Protocol. It allows improving Traffic Engineering in IP/MPLS networks by decomposing forwarding paths in lists of smaller paths called segments. The flexibility introduced by segment routing enables new responsive and dynamic methods to react to network fault events, such as link failures. Typical responses to single link failures only aim to reestablish loop-free connectivity between affected routers and do not account for impacts in network congestion. As Interior Gateway Protocols recompute shortest-paths after a link failure, SR paths and links utilization are affected by the fault augmenting the overall network congestion. This paper addresses this problem comparing post-convergence network congestion values obtained with industry and new proposal Evolutionary Computation based recovery path computation methods for segment routing. Furthermore, by comparing and analyzing each method’s advantages and disadvantages, we show that a multiplane optimization procedure stands out with best results.
Intellectual Property Protection for Distributed Neural Networks - Towards Confidentiality of Data, Model, and Inference

Capitalizing on recent advances on HPC, GPUs, GPGPUs along with the rising amounts of publicly available labeled data; (Deep) Neural Networks (NN) have and will revolutionize virtually every current application domain as well as enable novel ones such as those on recognition, autonomous, predictive, resilient, selfmanaged, adaptive, and evolving applications. Nevertheless, it is to point out that NN training is rather resource intensive in data, time and energy; turning the resulting trained models into valuable assets representing an Intellectual Property (IP) imperatively worth of being protected. Furthermore, in the wake of Edge computing, NNs are being progressively deployed across decentralized landscapes; as a consequence, IP owners take very seriously the protection of their NN based software products. In this paper we propose to leverage Fully Homomorphic Encryption (FHE) to protect simultaneously the IP of trained NN based software, as well as the input data and inferences. Within the context of a smart city scenario, we outline our NN model-agnostic approach, approximating and decomposing the NN operations into linearized transformations while employing a Single Instruction Multiple Data (SIMD) for vectorizing operations.
On Chronic Bronchitis, Especially as Connected with Gout, Emphysema, and Diseases of the Heart

selves or edifying to the student. It never has been done, they think, and they do not see their way to doing it; so they get through their duties to the patients in the readiest way they can, and bide the time when a charge of in-patients shall give them an opportunity of showing that they really care to advance clinical knowledge. We therefore hail with peculiar pleasure a complete answer to this despair, by which it is left without excuse, in the shape of a volume of clinical lectures, given by an assistant physician,
An Effective Correlation Formula for Enhancing the Detectability of Spread Spectrum-based Watermarking

This paper presents a new spread spectrum (SS)based audio watermarking scheme, which provides a striking correlation peak that facilitates watermark detection in the DCT domain. The superiority of the proposed scheme lies in the incorporation of magnitude adaptation, which balances the uneven distribution of the involved DCT coefficients in a selected frequency range. In this preliminary study, the embedding strength characterized by the signal-to-noise ratio is preset at 16 dB. Experiment results confirm that the watermark embedding contributes negligible degradation in perceptual quality. The results of the detectability test also indicate that the proposed scheme significantly outperforms the other two SS-based methods.
Estimation Improvement of Objective Scores of Answer Statements with Consideration of Semantic Similarity

To eliminate mismatches between the intentions of questioners and respondents of Question and Answer (Q&A) sites, we have clarified that the impression of the statements could be captured by nine factors, and the factor scores could be estimated from the feature values of the statements. Objective scores of the statements could be estimated fairly good, and that those of subjective statements could be estimated well by taking the natural logarithm of the factor scores with the consideration of cross-validation. This paper tries to perform multiple regression analysis with the consideration of semantic similarity between Q&A. A statement is represented with an average word vector of the words appearing in the statement. The semantic similarity of two statements is calculated through the cosine of the two average word vectors of the two statements. As a result, there could be a possibility that semantic similarity would be effective in estimating objective scores.
Classifier Level Fusion of Accelerometer and sEMG Signals for Automatic Fitness Activity Diarization

The human activity diarization using wearable technologies is one of the most important supporting techniques for ambient assisted living, sport and fitness activities, healthcare of elderly people. The activity diarization is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a technique for data fusion at classifier level of accelerometer and sEMG signals acquired by using a low-cost wearable wireless system for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications. To demonstrate the capability of the system of diarizing the user’s activities, data recorded from a few subjects were used to train and test the automatic classifier for recognizing the type of exercise being performed.
Decentralized Short-Term Voltage Control in Active Power Distribution Systems

With increasing utilizations of distributed energy resources and smart energy apparatuses, power distribution systems are undergoing a transition from passive to active networks, which challenges distribution system operators in many aspects of energy management and system control. In this paper, a short-term control strategy for active power distribution systems is proposed to regulate voltages into statutory ranges. The voltage control strategy is modeled as a convex optimization problem by applying a second-order cone relaxation. After the network decomposition, the alternating direction method of multipliers is employed to solve the established optimization model in a decentralized mode. Finally, the IEEE 33-bus and 123-bus test systems are employed to demonstrate the performance of the proposed voltage control strategy in active power distribution systems.
Towards Standardization of MQTT-Alert-based Sensor Networks: Protocol Structures Formalization and Low-End Node Security

We present a small scale sensor network application as a testbench to explore different setups in terms of hardware/ software, network protocol and data processing/storage scheme options, focusing on alert-based sensor systems with long idle times. Our specifications required the deployment of the network using the MQTT protocol over an encrypted TLS connection. We focused on using sensor nodes with low-power consumption profile, as well as on the formalization of the MQTT protocol's basic elements, by clearly defining the topic/message scheme used across the network. In addition, we experimented on how to combine locally stored information with data from popular cloud-based platforms and also acquired results regarding the processing performance of the nodes using different data exchange formats and database technologies. Work in progress includes data preprocessing on the network edge targeting distribution of the processing power across the network and network-traffic limitation, and also big data post-processing on server side or on dedicated high performance nodes to reveal hidden data patterns.
Highly Efficient Balanced Power Amplifiers for 4G Applications

This paper presents a highly efficient balanced power amplifier for 4G applications using a microstrip branch line coupler. A compact U-shaped microstrip branch line coupler is used for the balanced power amplifier (PA) architecture. The balanced PA achieves a power aided efficiency (PAE) of 66.8% what makes it very applicable for the 4G and 5G communication systems. The simulated and measured S-parameters (S_11 and S_22), gain, output power and power spectra densities for 3 MHz LTE signals at 1.5 GHz are presented.
Piezoelectric Asymmetric Indirect-Driven Self-Sensing Actuation

Self-sensing actuation allows combining the actuator and sensor into a single piezoelectric element. In this paper, the Asymmetric Indirect-Driven Self-Sensing Actuation (AIDSSA) circuit for piezoelectric actuators has been proposed. Unlike conventional self-sensing circuits relying on a differential amplifier, the proposed circuit uses active feedback mechanism in op-amps to reject the common-mode interference. The analysis results reveal that the proposed circuit requires simpler matching conditions and is able to sense the mechanical strain in a uniform gain without compromising the actuation stroke. To show the usefulness in piezoelectric-actuated systems, the AIDSSA circuit has been synthesized into a dual-stage hard disk drive. Experimental results show that the position error variance, a measure of disturbance rejection, has been reduced by more than 15% in the track-following mode. The proposed circuit should be of usefulness in a wide range of applications involving piezoelectric actuators.
Difference in control between spring and autumn migration in birds: insight from seasonal changes in hypothalamic gene expression in captive buntings

We hypothesized differences in molecular strategies for similar journeys that migrants undertake to reproduce in spring and to overwinter in autumn. We tested this in redheaded buntings (Emberiza bruniceps) photoinduced into spring and autumn migratory states, with winter and summer non-migratory states as controls. Compared with controls, buntings fattened, gained weight and showed Zugunruhe (nocturnal migratory restlessness) in the migratory state. Spring migration was associated with greater fat and body mass, and higher intensity of Zugunruhe, compared with autumn migration. Circulating corticosterone levels were higher in spring, while T3 levels were higher in autumn. Hypothalamic expression of thyroid hormone-responsive (dio2, dio3), light-responsive (per2, cry1, adcyap1) and th (tyrosine hydroxylase, involved in dopamine biosynthesis) genes showed significant changes with transition from non-migratory to the migratory state. There were significantly higher mRNA expressions in autumn, except for higher th levels in the spring. Furthermore, the expression patterns of dnmt3a (not dnmt3b) and tet2 genes suggested an epigenetic difference between the non-migrant and migrant periods, and the spring and autumn migrant periods. These results demonstrate for the first time seasonal transition in hypothalamic gene expressions, and suggest differences in regulatory strategies at the transcriptional level for spring and autumn migrations in songbirds.
Slip Sensing for Intelligent, Improved Grasping and Retraction in Robot-Assisted Surgery

Grasping and manipulation of biological tissue are crucial processes during Robot-Assisted Surgery (RAS). An intelligent system that aids surgeons in maintaining a reliable, atraumatic grasp on tissue, would allow them to focus on the clinical task, and potentially improve safety and performance. To this end, we investigate slip detection of grasped tissue. Because tissue is moist, conformable, and delicate, and because the sensor must work in a surgical environment, this application requires a departure from traditional slip-sensing methods. We present a method that uses sensor fusion with a da Vinci surgical system to identify slip after a reliable grasp is made. A transient simulation of the thermal-based sensor was used to describe its expected behavior. The sensor's response to slips was then validated. Subsequent experiments involved building a custom da Vinci 8 mm instrument with the sensor integrated and interacting with ex vivo porcine tissue. Results demonstrated slip detection within 2.1 mm average motion across five tissue types. During freeform grasping experiments, slip was detected with a 92% true positive rate and 94% positive predictive value.
5G OS: Control and Orchestration of Services on Multi-Domain Heterogeneous 5G Infrastructures

A heterogeneous 5G infrastructure includes physical and virtual computation, storage, and networking resources, exploiting different technologies and spanning across several administrative domains. These resources can be combined into end-to-end slices that can host different services. Services consist of virtual or physical elements and have different requirements, e.g., in terms of resource demands, latency, performance guarantees. The underlying resources need to be efficiently allocated to the slices and their corresponding services. To abstract away the complexities of the underlying 5G infrastructure and to provide the common functionalities required for efficient and flexible service and slice management and orchestration, we propose 5G Operating System (5G OS). We describe the requirements of such a system, present the high-level architecture of 5G OS, and describe the design challenges for different interfaces within 5G OS. Our proposed architecture includes scalable hierarchies of multi-domain and domain-specific slice and service orchestrators, software-defined network controllers, and network function virtualization management and orchestration frameworks.
Synthetic Optimisation Techniques for Epidemic Disease Prediction Modelling

In this paper, research is presented for improving optimisation performance using sparse training data for disease vector classification. Optimisation techniques currently available such as Bayesian, Evolutionary and Global optimisation and are capable of providing highly efficient and accurate results however, performance potential can often be restricted when dealing with limited training resources. In this study, a novel approach is proposed to address this issue by introducing Sequential Model-based Algorithm Configuration(SMAC) optimisation in combination with Synthetic Minority Over-sampling Technique(SMOTE) for optimised synthetic prediction modelling. This approach generates additional synthetic instances from a limited training sample while concurrently seeking to improve best algorithm performance. As results show, the proposed Synthetic Instance Model Optimisation (SIMO) technique presents a viable, unified solution for finding optimum classifier performance when faced with sparse training resources. Using the SIMO approach, noticeable performance accuracy and f-measure improvements were achieved over standalone SMAC optimisation. Many results showed significant improvement when comparing collective training data with SIMO instance optimisation including individual performance accuracy increases of up to 46% and a mean overall increase for the entire 240 configurations of 13.96% over standard SMAC optimisation.
Estimating the Active and Reactive Power Flexibility Area at the TSO-DSO Interface

The penetration of distributed renewable energy sources in the distribution grid is increasing considerably in the last years. This is one of the main causes that contributed to the growth of technical problems in both transmission and distribution systems. An effective solution to improve system security is to exploit the flexibility that can be provided by distributed energy resources (DER), which are mostly located at the distribution grids. Their location combined with the lack of power flow coordination at the system operators interface creates difficulties in taking advantage of these flexible resources. This paper presents a methodology based on the solution of a set of optimization problems that estimate the flexibility ranges at the distribution and transmission system operators (TSO-DSO) boundary nodes. The estimation is performed while considering the grid technical constraints and a maximum cost that the user is willing to pay. The novelty behind this approach comes from the development of flexibility cost maps, which allow the visualization of the impact of DER flexibility on the operating point at the TSO-DSO interface. The results are compared with a sampling method and suggest that a higher accuracy in the TSO-DSO information exchange process can be achieved through this approach.
Interactive Edge-Aware Segmentation of Character Illustrations for Articulated 2D Animations

Fascinating articulated animations can be created from character illustrations using commercial tools such as Live2D or spine. However, to compose articulated deformable models, artists have to draw each body part separately or segment input illustrations into several body parts manually. In this paper, we present an efficient technique for the segmentation of character illustrations with simple user interactions. The user first draws strokes (i.e., scribbles) on an input illustration to roughly specify body parts, and then the system segments the input character accordingly. Unlike an existing method for this interactive segmentation, our method has the following three advantages. First, our method recognizes weak colored edges as body-part boundaries, which can be missed by the existing method. Second, it lets the user directly specify body-part boundaries around articulations, which are not necessarily image edges and thus often missed by the existing method. Third, our method automatically enlarges body parts around articulations so that adjacent body parts have some overlaps and the resultant character models can be animated without exhibiting gaps between adjacent body parts. We demonstrate that the proposed system enables more efficient segmentation of character illustrations than the conventional technique.
Using Document Classification to Improve the Performance of a Plagiarism Checker

The performance of a plagiarism checker can be prohibitively expensive if the size of the document database to be checked against is large. To improve the checker's performance, we propose a method that organizes the document database into categories based on a label assigned to the document. The label is derived from simple heuristics applied over the first few pages of the document. For the original document to be checked for possible plagiarism, a set of probability values is assigned based on the likelihood of its belonging to specific categories. The checker then examines only the relevant categories, obviating the need to check against the whole database. This paper focuses primarily on documents in the Thai language. We use the datasets that contain theses and journals from Kasetsart University from years 1998 to 2010. We found that, for the given datasets, only 5 out of 20 classes need to be searched against to maintain the same accuracy as when all the classes are searched against.
Unscented Transform-Based Dual-Channel Noise Estimation: Application to Speech Enhancement on Smartphones

Some speech processing approaches rely on a noise estimation stage and their performance depends on the accuracy of the noise estimates. In this paper we propose a novel minimum mean square error (MMSE) noise estimator that takes advantage of dual-channel noisy observations to avoid the use of a clean speech model while keeping a simple formulation. The parameters of this estimator are obtained through the unscented transform (UT), which is able to compute better quality statistics in a more efficient way than through classical vector Taylor series (VTS) linearization. For evaluation, speech enhancement on a dual-microphone smartphone in close-talk conditions is considered, which is a particular application of interest. Results show the superiority of our proposal with respect to other single-and dual-channel noise estimation methods in terms of different measures such as estimation accuracy as well as on the enhanced speech signal, i.e., speech quality and intelligibility.
The Combination of Features Extracted from Different Parts for Person Re-Identification

Person re-identification is an important technique towards automatic search of a person's presence in a video surveillance system. It is inherently difficult due to appearance variations in pose, illumination and view. In this paper, we propose a method that extracting features from different parts of a person and calculating the weighted distance between different persons to realize re-identification. In the method, the person is segmented into three parts: head, torso and leg. And different distance metrics are utilized to calculate distances between diverse features. The effectiveness of our method is verified in experiments.