Miracle-dz/UniFAR-FTUs huggingface dataset:
Unconstrained ℓ1 — regularized minimization with interpolated transformations for heart motion compensation

Motion compensation constitutes a challenging issue in minimally invasive beating heart surgery. Since the zone to be repaired has a dynamic behaviour, precision and surgeon's dexterity decrease. In order to solve this problem, various proposals have been presented using ℓ2-norm. However, as they present some limitations in terms of robustness and efficiency, motion compensation is still considered an open problem. In this work, a solution based on the class of ℓ1 Regularized Optimization is proposed. It has been selected due to its mathematical properties and practical benefits. In particular, deformation is characterized by cubic B-splines since they offer an excellent balance between computational cost and accuracy. Moreover, due to the non-differentiability of the functional, the logarithmic barrier function is used for generating a standard optimization problem. Results have provided a very good tradeoff between accuracy and efficiency, indicating the potential of the proposed approach and proving its stability even under complex deformations.
Brain globins in physiology and pathology

Globins are globular proteins for either transport or storage of oxygen which are critical for cellular metabolism. Four globins have been identified in rodent and human brains. Among them, neuroglobin, cytoglobin and hemoglobin chains are constitutively expressed in normal brain, while myoglobin is only expressed in some neurological disorders. Studies on the molecular structure, expression and functional features of these brain globins indicated that they may play crucial roles in maintenance of neural cell survival and activity, including neurons and astrocytes. Their regulation in neurological disorders may help thoroughly understand initiation and progression of ischemia, Alzheimer′s disease and glioma, etc. Elucidation of the brain globin functions might remarkably improve medical strategies that sustain neurological homeostasis and treat neurological diseases. Here the expression pattern and functions of brain globins and their involvement in neurological disorders are reviewed.
Runtime modularity in complex structures: a component model for fine grained runtime adaptation

Online modular adaptation and self-adaptation techniques have demonstrated significant benefits in coarse-grained software, enabling agile and high-performance deployments. We are studying the same kinds of runtime adaptation applied to fine-grained software such as graphical user interfaces and web server implementations. However, this kind of software is defined by pervasive use of behaviourally-driven structure. Existing runtime component models fail to capture this necessity due to their exclusive reliance on externally-driven structural composition. In this paper we present a novel runtime component model that both satisfies the need to externally manage software structure, enabling runtime adaptation and self-adaptation, while also satisfying the need for fine-grained software to create elements of its own structure based on application-specific system behaviour. We present the key details of our model along with an initial evaluation.
Comments on "An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks"

Vehicle ad-hoc networks (VANETs) have experienced rapid growth due to the advancement of cloud computing, IoT technologies, and intelligent transportation systems (ITS). Vehicles are required to have enhanced storage capacity, onboard computing capabilities, improved sensing power, and communication systems. To address real-world demands like low latency, affordable storage, and mobility in VANET deployments, There have been efforts to integrate fog computing with VANETs in a practical implementation. "An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks" was proposed by Ma et al. (IEEE Internet of Things Journal, pp 8065–8075, 10.1109/JIOT.2019.2902840). According to their claims, the use of their secure authentication technique can help prevent security threats. However, after careful investigation, we discovered that their authentication protocol is susceptible to vehicle user impersonation attacks and also does not provide vehicle anonymity. In light of this, we have provided some recommendations to address the current flaws in the protocol developed by Ma et al.
CD- NOTEARS: Concept Driven Causal Structure Learning Using NOTEARS

Causal discovery has become increasingly popular in recent years, with the emergence of various methods for inferring causal relationships from observational data. While NOTEARS is a widely-used structure learning method known for its effectiveness in handling scalar-valued continuous data, it is not well-posed for conceptual data. In this study, we present a novel extension of the NOTEARS method, called Concept-Driven NOTEARS (CD-NOTEARS), that leverages concept-level prior knowledge to impose DAGness on concepts instead of the raw high-dimensional data. Our proposed approach preserves the non-parametric nature of the original NOTEARS method and is evaluated on synthetic, benchmark, and real-world datasets. The results demonstrate that CD-NOTEARS outperforms the original implementation and offers a promising tool for causal discovery in scenarios where causality should be imposed on the concept level. Our study provides insights into how incorporating concept-level knowledge improves the performance of causal discovery and paves the way for further research in this direction.
Exploring the Faithfulness of Synthetic Data by Generative Models

Evaluating the quality of synthetically generated data is an open problem, still not adequately debated when compared to the interest in how it could be generated, and still far from an effective standardization stage. The evaluation of such artificial data produced, for example, by GANs or other generative AI models, should be approached having in mind several relevant questions concerning, among the others, the following ones: i) the meaning of reliability, faithfulness, and overall quality of artificial data as respect to genuine data; ii) the appropriate set of metrics and tools to estimate the quality of synthetic data; iii) the identification of dependencies occurring among the quality of the artificial data, the generative models to produce them and the kind of native data used to train the generative processes. This work aims to contribute to this challenge by providing a reconnaissance study supporting the groundwork for a more structured analysis of the problem. More exactly, a particular kind of Generative models, such as the Tabular GANs, is discussed in details to focus the attention on effective methods for analyzing and of evaluating the quality of synthetically generated data.
A Critical Review on Quality of Experience for Videos and User Engagement on Social Media Platforms

Videos have been used to attract consumers and this has become an important marketing tool on social media platforms today. Research on users' perceptions of video Quality of Experience (QoE) has attracted increasing global attention in recent years. This paper critically reviews previous research works on factors influencing users’ perceptions of video quality on social media and the related variables. Through a systematic literature review, the paper contributes to understanding social media adoption, specifically looking into video quality and its connection to user engagement in social media advertising. The analysis of related works can highlight the gaps that have yet to be addressed.
Identifying subjective statements in news titles using a personal sense annotation framework

This is the accepted version of the following article: Panicheva, P.; Cardiff, J.; Rosso, P. (2013). Identifying subjective statements in news titles using a personal sense annotation framework. Journal of the American Society for Information Science and Technology. 64(7):1411-1422 , which has been published in final form at http://dx.doi. org/10.1002/asi.22841.
Analysis of Requirements for Developing an mHealth-Based Health Management Platform

Background Studies have consistently shown that mobile and Web-based apps have positive impacts on people’s daily lifestyles, health management, and disease treatment. As the development of medical and health informatization in China has evolved, different kinds of mobile-based apps for individuals and hospitals have been developed by software vendors. However, doubts and challenges posed by the media have prevented these apps from having a stable and substantial user base. Analyses of user requirements have not typically been performed prior to the design of such mobile apps. The health information government authority in Liaoning Province, China, was planning to establish a mobile health (mHealth)-based health management platform, aiming to alleviate the difficulties citizens have in seeking hospital services. Objective The goal of this study was to determine the actual health and medical needs of citizens that may be addressed by medical information technologies. The results may contribute to the functional design and development of health management and appointed treatment-oriented mobile apps. Methods In this study, a semi-structured questionnaire on mHealth requirements was designed and tested, and 240 questionnaires were given to the outpatients of the First Hospital of the China Medical University in Shenyang, Liaoning Province, China; of these, 228 valid responses were collected, for a response rate of 95%. We discussed the current development of mHealth with 50 related experts and engineers from health authorities and a medical information company. SPSS 13.0 was used for statistical analyses. Results After detailed analyses of the questionnaire data, several findings were evident: first, most citizens and patients were unclear about their health conditions (64.5%, 147/228) and were interested in receiving a mobile app as a tool to manage their health and medical needs (71.1%, 162/228). Patients in different outpatient departments had different opinions regarding online registration. Conversely, the main problems for outpatients were long waiting times (66.4%, 148/223) and difficulties in making appointments (46.5%, 106/228), and they also worried about payments and Internet problems when using a mobile app for appointment reservations. Furthermore, as the main service target of mHealth is the health management of the general population, we first need to solve the associated interoperability and data security problems associated with such apps. Conclusions This study provides insight into the health and medical requirements of smartphone apps, and draws attention to some of the challenges and opportunities of mHealth. We suggest several value-added features and characteristics that app developers should take into consideration when developing health and medical-related apps. The findings also highlight some major challenges that require further consideration and research to ensure that these apps meet the core needs of patients and aid the development of the health information system in Liaoning Province, China.
Pedestrian Counting with Occlusion Handling Using Stereo Thermal Cameras

The number of pedestrians walking the streets or gathered in public spaces is a valuable piece of information for shop owners, city governments, event organizers and many others. However, automatic counting that takes place day and night is challenging due to changing lighting conditions and the complexity of scenes with many people occluding one another. To address these challenges, this paper introduces the use of a stereo thermal camera setup for pedestrian counting. We investigate the reconstruction of 3D points in a pedestrian street with two thermal cameras and propose an algorithm for pedestrian counting based on clustering and tracking of the 3D point clouds. The method is tested on two five-minute video sequences captured at a public event with a moderate density of pedestrians and heavy occlusions. The counting performance is compared to the manually annotated ground truth and shows success rates of 95.4% and 99.1% for the two sequences.
Implementation of Social Media Fake Profile Identification Using A Novel Machine Learning Technique

Online Social Networks OSNs have become more popular among individuals in today's society for usage in their regular social activities. Because of this, a significant amount of information on the users' social lives, personal lives, and professional lives is being saved on these OSNs. The growth of bogus profiles is one of the problems associated with utilizing these sites, despite the fact that they have improved the quality of people's social lives. Other problems are also associated with their use. The user data that is made accessible on OSNs is drawing the attention of academics and social analyzers, but it is also attracting the attention of hackers. These cybercriminals take advantage of the openness and vulnerability of an OSN by creating phoney identities, then use those profiles to engage in illegal, deceptive, and destructive behavior. Some examples of this behavior include identity theft, defamation, trolling, bullying, and spamming. In this research Paper, we will examine a variety of methods, both supervised and unsupervised, for determining if an Instagram profile is authentic or false. After putting a number of readily accessible Machine Learning Models through their paces, we have developed a Hybrid Model, which can be found in the file Maulik Shah Fake Profile Identifier.ipynb. This dataset has room for improvement in terms of quantity and quality metrics; however, this paper also demonstrate how accurate it can become to identify fake profile.
Combined Antisense and Pharmacological Approaches Implicate hTASK as an Airway O2 Sensing K+Channel*

Neuroepithelial bodies act as airway oxygen sensors. The lung carcinoma line H146 is an established model for neuroepithelial body cells. Although O2 sensing in both cells is via NADPH oxidase H2O2/free radical production and acute hypoxia promotes K+ channel closure and cell depolarization, the identity of the K+channel is still controversial. However, recent data point toward the involvement of a member of the tandem P domain family of K+channels. Reverse transcription-polymerase chain reaction screening indicates that all known channels other than hTWIK1 and hTRAAK are expressed in H146 cells. Our detailed pharmacological characterization of the O2-sensitive K+ current described herein is compatible with the involvement of hTASK1 or hTASK3 (pH dependence, tetraethylammonium and dithiothreitol insensitivity, blockade by arachidonic acid, and halothane activation). Furthermore, we have used antisense oligodeoxynucleotides directed against hTASK1 and hTASK3 to suppress almost completely the hTASK1 protein and show that these cells no longer respond to acute hypoxia; this behavior was not mirrored in liposome-only or missense-treated cells. Finally, we have used Zn2+ treatment as a maneuver able to discriminate between these two homologues of hTASK and show that the most likely candidate channel for O2 sensing in these cells is hTASK3.
A probabilistic collocation method based statistical gate delay model considering process variations and multiple input switching

Since the advent of new nanotechnologies, the variability of gate delay due to process variations has become a major concern. This paper proposes a new gate delay model that includes impact from both process variations and multiple input switching. The proposed model uses an orthogonal polynomial based probabilistic collocation method to construct a delay analytical equation from circuit timing performance. From the experimental results, our approach has less that 0.2% error on the mean delay of gates and less than 3% error on the standard deviation.
Design and Control Strategy of Galvanic Isolated Two-Stage Power Supply for Motor Drive

This paper provides the design and control strategy to achieve high efficiency for a two-stage converter as the voltage source for the motor drive. The proposed design procedure is based on matching the load profile as well as the maximum power limit by the motor. The front-end LLC provides the galvanic isolation capability and high transfer efficiency, while the back-end buck converter provides voltage step-down ability for an extended output voltage range. To further improve the efficiency under different voltage and current profiles, a special control strategy with high efficiency is proposed for a two-stage LLC-Buck converter. Finally, a 200W prototype circuit with both simulation and experimental results is provided to prove the feasibility of the proposed design and control strategy.
Predicting demographically sustainable rates of adaptation: can great tit breeding time keep pace with climate change?

Populations need to adapt to sustained climate change, which requires micro-evolutionary change in the long term. A key question is how the rate of this micro-evolutionary change compares with the rate of environmental change, given that theoretically there is a ‘critical rate of environmental change’ beyond which increased maladaptation leads to population extinction. Here, we parametrize two closely related models to predict this critical rate using data from a long-term study of great tits (Parus major). We used stochastic dynamic programming to predict changes in optimal breeding time under three different climate scenarios. Using these results we parametrized two theoretical models to predict critical rates. Results from both models agreed qualitatively in that even ‘mild’ rates of climate change would be close to these critical rates with respect to great tit breeding time, while for scenarios close to the upper limit of IPCC climate projections the calculated critical rates would be clearly exceeded with possible consequences for population persistence. We therefore tentatively conclude that micro-evolution, together with plasticity, would rescue only the population from mild rates of climate change, although the models make many simplifying assumptions that remain to be tested.
Asynchronous Circuit Design in Chisel Using Phase-Decoupled Click Elements

This paper explores using the hardware construction language Chisel to describe, implement, and test asynchronous circuits. The Chisel language supports clocked registers and the use of multiple clocks. It is hence capable of describing asynchronous designs that are built from clocked flip-flops and logic, such as circuits using the asynchronous (phase-decoupled) click-element template that we target in this paper. Previous work has used VHDL to describe a library of asynchronous data-flow components as well as an example circuit demonstrating the use of the components. In this paper, we re-implement this work using Chisel and compare against VHDL. As we see, Chisel offers more efficient and elegant support for describing asynchronous handshake channels and for connecting components. Compared to the VHDL version, the code is reduced by up to 80 %. The example circuit computes the greatest common divisor of two integers, and it is synthesized and tested on an FPGA board. All code is open source giving those interested easy access to asynchronous design and prototyping.
Functions of delay-period activity in the prefrontal cortex and mnemonic scotomas revisited

Working memory (WM) is one of key concepts to understand functions of the prefrontal cortex. Delay-period activity is an important neural correlate to understand the role of WM in prefrontal functions. The importance of delay-period activity is that this activity can encode not only visuospatial information but also a variety of information including non-spatial visual features, auditory and tactile stimuli, task rules, expected reward, and numerical quantity. This activity also participates in a variety of information processing including sensory-to-motor information transformation. These mnemonic features of delay-period activity enable to perform various important operations that the prefrontal cortex participates in, such as executive controls, and therefore, support the notion that WM is an important function to understand prefrontal functions. On the other hand, although experiments using manual versions of the delayed-response task had revealed many important findings, an oculomotor version of this task enabled us to use multiple cue positions, exclude postural orientation during the delay period, and further prove the importance of mnemonic functions of the prefrontal cortex. In addition, monkeys with unilateral lesions exhibited specific impairment only in the performance of memory-guided saccades directed toward visual cues in the visual field contralateral to the lesioned hemisphere. This result indicates that memories for visuospatial coordinates in each hemifield are processed primarily in the contralateral prefrontal cortex. This result further strengthened the idea of mnemonic functions of the prefrontal cortex. Thus, the mnemonic functions of the prefrontal cortex and delay-period activity may not need to be reconsidered, but should be emphasized.
Benchmarking YOLO Models for Automatic Reading in Smart Metering Systems: A Performance Comparison Analysis

In the smart grid ecosystem, the automated reading of water meters is a critical task that can be accomplished with deep learning algorithms instead of the traditional way that relies on the physical presence of employees or utility representatives. The YOLO (You Only Look Once) family of models has gained prominence among these approaches because of its excellent accuracy and quick processing time. In this study, we compare the performance of four YOLO models for automated reading in water metering systems (YOLOv5, YOLOv6, YOLOv7, and YOLOv8). We assess the model's precision, recall, and processing time using a custom dataset of water meter images. In terms of precision and processing time, our testing results demonstrate that YOLOv8 surpasses YOLOv7, YOLOv6, and YOLOv5 by scoring 0.889 in precision, making it a suitable alternative for automated reading in smart metering systems. This benchmarking study provides a valuable reference for researchers and practitioners in the field of smart grid and deep learning, and it can aid in the selection of the most appropriate model for this task.
Plasmonic Optical Fiber-Grating Immunosensing: A Review

Plasmonic immunosensors are usually made of a noble metal (in the form of a film or nanoparticles) on which bioreceptors are grafted to sense analytes based on the antibody/antigen or other affinity mechanism. Optical fiber configurations are a miniaturized counterpart to the bulky Kretschmann prism and allow easy light injection and remote operation. To excite a surface plasmon (SP), the core-guided light is locally outcoupled. Unclad optical fibers were the first configurations reported to this end. Among the different architectures able to bring light in contact with the surrounding medium, a great quantity of research is today being conducted on metal-coated fiber gratings photo-imprinted in the fiber core, as they provide modal features that enable SP generation at any wavelength, especially in the telecommunication window. They are perfectly suited for use with cost-effective high-resolution interrogators, allowing both a high sensitivity and a low limit of detection to be reached in immunosensing. This paper will review recent progress made in this field with different kinds of gratings: uniform, tilted and eccentric short-period gratings as well as long-period fiber gratings. Practical cases will be reported, showing that such sensors can be used in very small volumes of analytes and even possibly applied to in vivo diagnosis.