Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Information Acquisition and Portfolio Bias

While international portfolios are still heavily biased towards home assets, the home bias has exhibited a clear downward trend in the last few decades. Interestingly, the underlying rise in foreign investment has been primarily directed to just a handful of OECD countries, and has not given rise to an across the board increase in all foreign investments. To understand the evolution of the home bias, this paper develops a dynamic model of information acquisition and portfolio choice. The dynamic framework introduces two new endogenous forces due to the fact that asset payoffs depend on the future asset prices and hence on the future information sets. First, there is a measure of endogenous unlearnable uncertainty in asset payoffs which generates decreasing returns to information when agents are sufficiently well informed about an asset, and hence gives a reason to diversify information and portfolios. In addition, the dynamic framework introduces a strategic complementarity in learning, due to the “beauty contest” of dynamic asset markets, which is absent in the benchmark static model where learning is purely a strategic substitute. As a result of both of these new endogenous forces, the model can explain the high overall level of the home bias, its decline over time and the fact that the rise in foreign investment has been coordinated on just a handful of destination countries. Moreover, the model predicts that the home bias decline is linked to the fall in information costs, and I find direct evidence of this in the data.
A Novel Sensor Based on a Single-Pixel Microwave Radiometer for Warm Object Counting: Concept Validation and IoT Perspectives

Controlled measurements by a low-cost single-pixel microwave radiometer operating at 12.65 GHz were carried out to assess the detection and counting capability for targets warmer than the surroundings. The adopted reference test targets were pre-warmed water and oil; and a hand, both naked and wearing a glove. The results showed the reliability of microwave radiometry for counting operations under controlled conditions, and its effectiveness at detecting even warm targets masked by unheated dielectric layers. An electromagnetic model describing the scenario sensed by the radiometer antenna is proposed, and comparison with the experimental observations shows a good agreement. The measurements prove that reliable counting is enabled by an antenna temperature increment, for each target sample added, of around 1 K. Starting from this value, an analysis of the antenna filling factor was performed to provide an instrument useful for evaluating real applicability in many practical situations. This study also allows the direct people counting problem to be addressed, providing preliminary operational indications, reference numbers and experimental validation.
In Vitro Phenotypic, Genomic and Proteomic Characterization of a Cytokine-Resistant Murine β-TC3 Cell Line

Type 1 diabetes mellitus (T1DM) is caused by the selective destruction of insulin-producing β-cells. This process is mediated by cells of the immune system through release of nitric oxide, free radicals and pro-inflammatory cytokines, which induce a complex network of intracellular signalling cascades, eventually affecting the expression of genes involved in β-cell survival. The aim of our study was to investigate possible mechanisms of resistance to cytokine-induced β-cell death. To this purpose, we created a cytokine-resistant β-cell line (β-TC3R) by chronically treating the β-TC3 murine insulinoma cell line with IL-1β + IFN-γ. β-TC3R cells exhibited higher proliferation rate and resistance to cytokine-mediated cell death in comparison to the parental line. Interestingly, they maintained expression of β-cell specific markers, such as PDX1, NKX6.1, GLUT2 and insulin. The analysis of the secretory function showed that β-TC3R cells have impaired glucose-induced c-peptide release, which however was only moderately reduced after incubation with KCl and tolbutamide. Gene expression analysis showed that β-TC3R cells were characterized by downregulation of IL-1β and IFN-γ receptors and upregulation of SOCS3, the classical negative regulator of cytokines signaling. Comparative proteomic analysis showed specific upregulation of 35 proteins, mainly involved in cell death, stress response and folding. Among them, SUMO4, a negative feedback regulator in NF-kB and JAK/STAT signaling pathways, resulted hyper-expressed. Silencing of SUMO4 was able to restore sensitivity to cytokine-induced cell death in β-TC3R cells, suggesting it may play a key role in acquired cytokine resistance by blocking JAK/STAT and NF-kB lethal signaling. In conclusion, our study represents the first extensive proteomic characterization of a murine cytokine-resistant β-cell line, which might represent a useful tool for studying the mechanisms involved in resistance to cytokine-mediated β-cell death. This knowledge may be of potential benefit for patients with T1DM. In particular, SUMO4 could be used as a therapeutical target.
Data-Driven Super-Resolution on a Tactile Dome

While tactile sensor technology has made great strides over the past decades, applications in robotic manipulation are limited by aspects such as blind spots, difficult integration into hands, and low spatial resolution. We present a method for localizing contact with high accuracy over curved, 3-D surfaces, with a low wire count and reduced integration complexity. To achieve this, we build a volume of soft material embedded with individual off-the-shelf pressure sensors. Using data-driven techniques, we map the raw signals from these pressure sensors to known surface locations and indentation depths. Additionally, we show that a finite-element model can be used to improve the placement of the pressure sensors inside the volume and to explore the design space in simulation. We validate our approach on physically implemented tactile domes that achieve high contact localization accuracy (<inline-formula> <tex-math notation="LaTeX">$\text{1.1}\,\text{mm}$</tex-math></inline-formula> in the best case) over a large, curved sensing area (<inline-formula><tex-math notation="LaTeX">$\text{1300-mm}^2$</tex-math></inline-formula> hemisphere). We believe this approach can be used to deploy tactile sensing capabilities over 3-D surfaces such as a robotic finger or palm.
A study on the genetic polymorphisms of CYP3A5 among the Orang Asli in Malaysia using a next generation sequencing platform

Abstract Background: CYP3A5 is the predominant sub-family of biotransformation enzymes in the liver and the genetic variations in CYP3A5 are an important determinant of inter-individual and inter-ethnic differences in CYP3A-mediated drug disposition and response. Aim: This study aims to investigate the genetic polymorphisms of CYP3A5 among the Orang Asli in Peninsular Malaysia using a next generation sequencing platform. Methods: Genomic DNAs were extracted from blood samples of the three main Orang Asli tribes and whole-genome sequencing was performed. Results: A total of 61 single nucleotide polymorphisms were identified and all the SNPs were located in introns except rs15524, which is in the 3’UTR, and 11 of these polymorphisms were novel. Two allelic variants and three genotypes were identified in the Orang Asli. The major allelic variant was the non-functional CYP3A5*3 (66.4%). The percentages of Orang Asli with CYP3A5*3/*3 (47.2%) and CYP3A5*1/*3 (38.1%) genotypes are more than twice the percentage of Orang Asli with CYP3A5*1/*1 (14.8%) genotype. Almost half of the Orang Asli harboured CYP3A5 non-expressor genotype (CYP3A5*3/*3). Conclusions: The predominance of the CYP3A5 non-expressor genotype among the Orang Asli was unravelled and the findings in this study may serve as a guide for the optimisation of pharmacotherapy for the Orang Asli community.
Self-organizing maps and generalization: an algorithmic description of Numerosity and Variability Effects

Category, or property generalization is a central function in the human cognition. It plays a crucial role in a variety of domains, such as learning, everyday reasoning, specialized reasoning, and decision making. Judging the content of a dish as edible, a hormone level as healthy, a building as belonging to the same architectural style as previously seen buildings, are examples of category generalization. In this paper, we propose self-organizing maps as candidates to explain the psychological mechanisms underlying category generalization. Self-organizing maps are psychologically and biologically plausible neural network models that learn after limited exposure to positive category examples, without any need of contrastive information. Just like humans. They reproduce human behavior in category generalization, in particular for what concerns the well-known Numerosity and Variability effects, which are usually explained with Bayesian tools. Where category generalization is concerned, self-organizing maps are good candidates to bridge the gap between the computational level of analysis in Marr's hierarchy (where Bayesian models are situated) and the algorithmic level of aanalysis in Marr's hierarchy (where Bayesian models are situated) and the algorithmic level of analysis in which plausible mechanisms are described.
Increasing believability in animated pedagogical agents

Animated pedagogical agents o(cid:0)er great promise for knowledge(cid:1)based learning environments(cid:2) In addition to coupling feedback capabilities with a strong visual presence(cid:3) these agents play a critical role in motivating students(cid:2) The extent to which they exhibit life(cid:1)like behaviors strongly increases their motivational impact(cid:3) but these behaviors must always complement and never interfere with students(cid:4) problem solving(cid:2) To address this problem(cid:3) we have developed a framework for dynamically sequencing animated pedagogical agents(cid:4) believability(cid:1)enhancing be(cid:1) haviors(cid:2) By monitoring a student(cid:4)s problem(cid:1) solving history and the agent(cid:4)s past activities(cid:3) a competition(cid:0)based behavior sequencing engine produces realtime life(cid:1)like character animations that are pedagogically appropriate(cid:2) Behaviors in the agent(cid:4)s repertoire compete with one another(cid:2) At each moment(cid:3) the strongest eligible behavior is heuristically selected as the winner and is ex(cid:1) hibited(cid:2) We have implemented this framework in Herman the Bug(cid:3) an animated pedagogical agent that inhabits a knowledge(cid:1)based learning envi(cid:1) ronment for the domain of botanical anatomy and physiology(cid:2)
Efficiency enhancement of a three phase Soft Switching Inverter under light load conditions

This paper studies the performance of a three phase Soft Switching Inverter (SSI) under extremely light load conditions (7W, 150 mA-peak, 50 V-peak which is 10% of its thermally maximum permissible power). An Auxiliary Resonant Commutated Pole Inverter (ARCPI) has been selected as soft switching topology for this research. Analytic approaches are used to compute the losses and to select the power transistor which best fits this application. Two options to enhance the efficiency are discussed. First, the switching losses of the auxiliary switches are reduced by optimizing the turn on and off sequence. Second, a Resonant Gate Drive (RGD) is employed to reduce the gate losses of the switches. A single phase prototype has been realized. Measurements and computed losses are compared to validate the model and the calculations.
A Practical Evaluation of a High-Security Energy-Efficient Gateway for IoT Fog Computing Applications

Fog computing extends cloud computing to the edge of a network enabling new Internet of Things (IoT) applications and services, which may involve critical data that require privacy and security. In an IoT fog computing system, three elements can be distinguished: IoT nodes that collect data, the cloud, and interconnected IoT gateways that exchange messages with the IoT nodes and with the cloud. This article focuses on securing IoT gateways, which are assumed to be constrained in terms of computational resources, but that are able to offload some processing from the cloud and to reduce the latency in the responses to the IoT nodes. However, it is usually taken for granted that IoT gateways have direct access to the electrical grid, which is not always the case: in mission-critical applications like natural disaster relief or environmental monitoring, it is common to deploy IoT nodes and gateways in large areas where electricity comes from solar or wind energy that charge the batteries that power every device. In this article, how to secure IoT gateway communications while minimizing power consumption is analyzed. The throughput and power consumption of Rivest–Shamir–Adleman (RSA) and Elliptic Curve Cryptography (ECC) are considered, since they are really popular, but have not been thoroughly analyzed when applied to IoT scenarios. Moreover, the most widespread Transport Layer Security (TLS) cipher suites use RSA as the main public key-exchange algorithm, but the key sizes needed are not practical for most IoT devices and cannot be scaled to high security levels. In contrast, ECC represents a much lighter and scalable alternative. Thus, RSA and ECC are compared for equivalent security levels, and power consumption and data throughput are measured using a testbed of IoT gateways. The measurements obtained indicate that, in the specific fog computing scenario proposed, ECC is clearly a much better alternative than RSA, obtaining energy consumption reductions of up to 50% and a data throughput that doubles RSA in most scenarios. These conclusions are then corroborated by a frame temporal analysis of Ethernet packets. In addition, current data compression algorithms are evaluated, concluding that, when dealing with the small payloads related to IoT applications, they do not pay off in terms of real data throughput and power consumption.
Multipath-based opportunistic routing scheme for large-scale wireless sensor network

Opportunistic routing (OR) has been emerged as a promising routing strategy to improve transmission reliability for wireless networks. By taking broadcast nature of wireless medium, a set of forwarding candidate nodes receive a data packet simultaneously, so one of them can relay it unless they all fail to receive. This strategy can dramatically improve one-hop reliability even in unreliable wireless links, however, it cannot be directly applied into large-scale wireless multi-hop networks since multi-hop communication significantly decreases end-to-end reliability. In this letter, we propose a novel opportunistic routing scheme for large-scale wireless multi-hop networks, which adopts multipath based routing in order to improve end-to-end reliability. While taking advantages of opportunistic routing, each intermediate node can determine whether it branches into multiple paths or not without any network topology information, so that a desired delivery ratio from a source to a destination given by application demands can be achieved in a fully localized manner.
Evaluation of Flick and Ring Scrolling on Touch-Based Smartphones

This study examined the performance of two scrolling techniques (flick and ring) for document navigation in touch-based mobile phones using three input methods (index finger, pen, and thumb), with specific consideration given to two postures: sitting and walking. The findings are as follows: (a) in both sitting and walking postures, for the three input methods, flick resulted in shorter movement time and fewer crossings than ring, suggesting flick is superior to ring for document navigation; (b) for sitting posture, regarding pen and thumb input, ring led to shorter movement time than flick for large target distances, indicating ring has a potential interaction advantage; (c) regarding sitting and walking postures, both flick and ring document scrolling in touch-based mobile phones can be modeled by the Anderson model (Andersen, 2005). Designers of future scrolling techniques should consider these differences, as well as exploit the advantages and avoid the disadvantages of ring and flick scrolling.
Novel Hybrid Scheduling Technique for Sensor Nodes with Mixed Criticality Tasks

Sensor networks become increasingly a key technology for complex control applications. Their potential use in safety- and time-critical domains has raised the need for task scheduling mechanisms specially adapted to sensor node specific requirements, often materialized in predictable jitter-less execution of tasks characterized by different criticality levels. This paper offers an efficient scheduling solution, named Hybrid Hard Real-Time Scheduling (H2RTS), which combines a static, clock driven method with a dynamic, event driven scheduling technique, in order to provide high execution predictability, while keeping a high node Central Processing Unit (CPU) utilization factor. From the detailed, integrated schedulability analysis of the H2RTS, a set of sufficiency tests are introduced and demonstrated based on the processor demand and linear upper bound metrics. The performance and correct behavior of the proposed hybrid scheduling technique have been extensively evaluated and validated both on a simulator and on a sensor mote equipped with ARM7 microcontroller.
Development Tendency of Measurement Methods for Residual Stresses

The existence of residual stresses has a significant impact on the material performance,such as fatigue strength,stress corrosion and shape accuracy,so it is very important to accurately measure the residual stresses.The current measurement methods of residual stress were summarized,and the instrumented nanoindentation measurement for surface residual stresses of small volume material was emphasized.By combining nanoindentation with the finite element analysis method,not only residual stress field can be calculated under a given condition,but also the formation and evolution of residual stress can be determined,and especially it is possible to analyze the mechanics when the indentation process enters into plastic phase and to establish mechanics models.The characteristics and application scope of several residual stress measurement method were compared,and the development trend was analyzed.This paper also pointed out that the measurement technology should be developed towards the nondestructive,quick and unpolluted direction.
Geometric Lower Bounds for Distributed Parameter Estimation Under Communication Constraints

We consider parameter estimation in distributed networks, where each sensor in the network observes an independent sample from an underlying distribution and has <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> bits to communicate its sample to a centralized processor which computes an estimate of a desired parameter. We develop lower bounds for the minimax risk of estimating the underlying parameter for a large class of losses and distributions. Our results show that under mild regularity conditions, the communication constraint reduces the effective sample size by a factor of <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> when <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is small, where <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> is the dimension of the estimated parameter. Furthermore, this penalty reduces at most exponentially with increasing <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, which is the case for some models, e.g., estimating high-dimensional distributions. For other models however, we show that the sample size reduction is re-mediated only linearly with increasing <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, e.g. when some sub-Gaussian structure is available. We apply our results to the distributed setting with product Bernoulli model, multinomial model, Gaussian location models, and logistic regression which recover or strengthen existing results. Our approach significantly deviates from existing approaches for developing information-theoretic lower bounds for communication-efficient estimation. We circumvent the need for strong data processing inequalities used in prior work and develop a geometric approach which builds on a new representation of the communication constraint. This approach allows us to strengthen and generalize existing results with simpler and more transparent proofs.
Deterioration of R-Wave Detection in Pathology and Noise: A Comprehensive Analysis Using Simultaneous Truth and Performance Level Estimation

<italic>Objective:</italic> For long-term electrocardiography (ECG) recordings, accurate R-wave detection is essential. Several algorithms have been proposed but not yet compared on large, noisy, or pathological data, since manual ground-truth establishment is impossible on such large data. <italic>Methods:</italic> We apply the simultaneous truth and performance level estimation (STAPLE) method to ECG signals comparing nine R-wave detectors: Pan and Tompkins (1985), Chernenko (2007), Arzeno <italic>et al.</italic> (2008), Manikandan <italic>et al.</italic> (2012), Lentini <italic>et al.</italic> (2013), Sartor <italic>et al.</italic> (2014), Liu <italic>et al.</italic> (2014), Arteaga-Falconi <italic>et al.</italic> (2015), and Khamis <italic>et al.</italic> (2016). Experiments are performed on the MIT-BIH database, TELE database, PTB database, and 24/7 Holter recordings of 60 multimorbid subjects. <italic>Results:</italic> Existing approaches on R-wave detection perform excellently on healthy subjects (F-measure above 99% for most methods), but performance drops to a range of <italic>F</italic> = 90.10% (Khamis <italic>et al.</italic>) to <italic>F</italic> = 30.10% (Chernenko) when analyzing the 37 million R-waves of multimorbid subjects. STAPLE improves existing approaches (Δ<italic>F</italic> = 0.04 for the MIT-BIH database and Δ<italic>F</italic> = 0.95 for the TELE database) and yields a relative (not absolute) scale to compare algorithms’ performances. <italic>Conclusion:</italic> More robust R-wave detection methods or flexible combinations are required to analyze continuous data captured from pathological subjects or that is recorded with dropouts and noise. <italic>Significance:</italic> STAPLE algorithm has been adopted from image to signal analysis to compare algorithms on large, incomplete, and noisy data without manual ground truth. Existing approaches on R-wave detection weakly perform on such data.