Miracle-dz/UniFAR-FTUs huggingface dataset:
Developments in Canadian Adult Guardianship and Co-Decision-Making Law

Guardianship was originally developed as a social and legal tool meant to protect vulnerable persons. It is now evolving as a mechanism to promote autonomy. Th is paper examines the Canadian law of guardianship, including its historical evolution, the social and legal catalysts for its reform and related constitutional issues. Guardianship law has a long history in Western society, and has traditionally been paternalistic and property-focused. Early Canadian guardianship laws were largely based on English lunacy acts, and continued unchanged into the second half of the twentieth century. Reformation in Canadian guardianship law began in the 1970s and 80s, with criticism that the current law intruded unjusti fi ably into an individual’s personal sphere of autonomy. Th is criticism arose from an increased understanding of human capacity and the recognition of autonomy as a foundational human right. In 2000, Saskatchewan introduced comprehensive guardianship legislation: Th e Adult Guardianship and Co-decision-making Act. Th is Act authorizes the appointment of co-decision-makers as an alternative to the traditional court- appointed guardian. Th is alternative provides Saskatchewan courts the ability to e ff ectively address the need and capacity of the adult in question. Th e co-decision- making provisions of the Act are unique in Canadian guardianship law: the co- decision-maker shares legal authority with the adult, must acquiesce to an adult’s reasonable decision and is statutorily required to minimally interfere in the adult’s life and decision-making process. Th e co-decision-maker is further required to act in a manner that protects the adult’s civil and human rights. Th e Saskatchewan Act represents an important attempt to rethink guardianship concepts in Canada, and should form the model for future guardianship legislation in this country.
Complexity Analysis and Variational Inference for Interpretation-based Probabilistic Description Logic

This paper presents complexity analysis and variational methods for inference in probabilistic description logics featuring Boolean operators, quantification, qualified number restrictions, nominals, inverse roles and role hierarchies. Inference is shown to be PEXP-complete, and variational methods are designed so as to exploit logical inference whenever possible.
Quantification of protein half-lives in the budding yeast proteome

A complete description of protein metabolism requires knowledge of the rates of protein production and destruction within cells. Using an epitope-tagged strain collection, we measured the half-life of >3,750 proteins in the yeast proteome after inhibition of translation. By integrating our data with previous measurements of protein and mRNA abundance and translation rate, we provide evidence that many proteins partition into one of two regimes for protein metabolism: one optimized for efficient production or a second optimized for regulatory efficiency. Incorporation of protein half-life information into a simple quantitative model for protein production improves our ability to predict steady-state protein abundance values. Analysis of a simple dynamic protein production model reveals a remarkable correlation between transcriptional regulation and protein half-life within some groups of coregulated genes, suggesting that cells coordinate these two processes to achieve uniform effects on protein abundances. Our experimental data and theoretical analysis underscore the importance of an integrative approach to the complex interplay between protein degradation, transcriptional regulation, and other determinants of protein metabolism.
The Design of a Low-Power Asynchronous DES Coprocessor for Sensor Network Encryption

Sensor network nodes have a very tight power budget and the power efficiency is the biggest design concern in sensor network circuits. A general-purpose processor (e.g. an ARM processor) is not efficient to execute encryption algorithms because it has no special instructions to support encryption operations, for example very often-used permutation operations. In the paper, we propose a low-power ASIC encryption coprocessor for sensor network nodes. A DES algorithm is used because the algorithm does not include power-hungry and complex mathematic operations, such as multiplication, division and addition. An asynchronous logic style is used to design the coprocessor. With an asynchronous controller, a global clock is not necessary when idle, resulting in zero standby dynamic power. Using the DES coprocessor, the power consumed by encryption can be saved by 4 orders of magnitude than a pure software calculation.
An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter

This paper proposes an efficient implementation of the generalized labeled multi-Bernoulli (GLMB) filter by combining the prediction and update into a single step. In contrast to an earlier implementation that involves separate truncations in the prediction and update steps, the proposed implementation requires only one truncation procedure for each iteration. Furthermore, we propose an efficient algorithm for truncating the GLMB filtering density based on Gibbs sampling. The resulting implementation has a linear complexity in the number of measurements and quadratic in the number of hypothesized objects.
Unobserved Risk Type and Sorting : Signaling Game in Online Credit Markets

This paper studies how signaling can facilitate the functioning of a market with classical adverse selection problems. Using data from Prosper.com, an online credit market where loans are funded through auctions, we provide evidence that reserve interest rates that potential borrowers post work as a signaling device. We then develop and estimate a structural model of borrowers and lenders where low reserve interest rate can credibly signal low default risk. Announcing high reserve interest rate increases the probability of trade at the cost of higher expected interest payment conditional on trade. Borrowers regard this trade-o¤ di¤erentially, which results in a separating equilibrium. In our counterfactual, we estimate the credit supply curve when signaling is not available.
Ultra-Wideband Dual-Polarized Patch Antenna With Four Capacitively Coupled Feeds

A novel dual-polarized patch antenna for ultra-wideband (UWB) applications is presented. The antenna consists of a square patch and four capacitively coupled feeds to enhance the impedance bandwidth. Each feed is formed by a vertical isosceles trapezoidal patch and a horizontal isosceles triangular patch. The four feeds are connected to the microstrip lines that are printed on the bottom layer of the grounded FR4 substrate. Two tapered baluns are utilized to excite the antenna to achieve high isolation between the ports and reduce the cross-polarization levels. In order to increase the antenna gain and reduce the backward radiation, a compact surface mounted cavity is integrated with the antenna. The antenna prototype has achieved an impedance bandwidth of 112% at (|S11 | ≤ -10 dB) whereas the coupling between the two ports is below -28 dB across the operating frequency range. The measured antenna gain varies from 3.91 to 10.2 dBi for port 1 and from 3.38 to 9.21 dBi for port 2, with a 3-dB gain bandwidth of 107%.
Power Increases Anchoring Effects on Judgment

Four experiments test the impact of power (versus powerlessness) on anchoring effects. Anchoring refers to the tendency to assimilate one's judgment to a previously considered numeric standard. Based on the notion that power facilitates the activation of and reliance on accessible information, we hypothesized that power increases numeric anchoring effects on judgment, compared to powerlessness. Across studies, we found consistent support for this idea, when testing estimations of factual values (Experiments 1 and 2), subjective evaluations (Experiment 3), and negotiation behavior (Experiment 4). The findings of Studies 2 to 4 qualify the dominant idea in power literature that power reduces conformity to others' opinion. Power increases conformity to others' opinion, if such opinion is presented as an anchor and therefore processed more automatically. These findings also have important methodological implications for power research. They show that differences in stimulus presentation can steer observed eff.
p-HInT : Interactive Educational environment for improving large-scale lecture with mobile game terminals

We have developed an interactive educational environment called “p-HInT”. Purpose of the p-HInT is improvement of speech style lectures with 200 or more attendees at non-computer classrooms. A most feature of the p-HInT is usage of mobile game terminal NINTENDO DS with Opera browser. The p-HInT has main two functions: (1)an attendant list with sit down position in a classroom, (2) test in hand-written style. By using the p-HInT with DSs, because teachers can quickly grasp attendees’ understanding, the teachers can improve then and there the lecture. As a result of adapting lecture on the regular curriculum, we confirmed effectiveness and potential of the p-HInT.
Peroxynitrite Mediates Neurotoxicity of Amyloid β-Peptide1–42- and Lipopolysaccharide-Activated Microglia

The amyloid β-peptide (Aβ) activates microglia and promotes the generation of cytokines and oxygen species, including nitric oxide (NO) and tumor necrosis factor α (TNF-α), which can be either neurotoxic or neuroprotective. We show that neuron death in cocultures of rat cortical microglia and neurons activated by lipopolysaccharide (LPS) or Aβ1–42 plus interferon γ (IFNγ) is caused by short-lived diffusible molecules and follows the generation of superoxide and/or peroxynitrite as determined by electron paramagnetic spectroscopy. Neurotoxicity induced by LPS or Aβ1–42 plus IFNγ is blocked by inhibitors of NO synthesis and by the peroxynitrite (ONOO−) decomposition catalysts FeTMPyP [5,10,15,20-tetrakis(n-methyl-4′-pyridyl)porphinato iron (III) chloride] and FeTPPS [5,10,15,20-tetrakis(4-sulfonatophenyl)prophyrinato iron (III) chloride] but not by the TNF-α inhibitor pentoxifylline. The specificity of FeTMPyP for ONOO− was confirmed by its ability to block the toxicity of a peroxynitrite donor but not of NO donors or of high levels of superoxide in a yeast mutant lacking superoxide dismutase 1. These results implicate peroxynitrite as a mediator of the toxicity of activated microglia, which may play a major role in Aβ1–42 neurotoxicity and Alzheimer's disease.
Inferring influenza global transmission networks without complete phylogenetic information

Influenza is one of the most severe respiratory infections affecting humans throughout the world, yet the dynamics of its global transmission network are still contentious. Here, I describe a novel combination of phylogenetics, time series, and graph theory to analyze 14.25 years of data stratified in space and in time, focusing on the main target of the human immune response, the hemagglutinin gene. While bypassing the complete phylogenetic inference of huge data sets, the method still extracts information suggesting that waves of genetic or of nucleotide diversity circulate continuously around the globe for subtypes that undergo sustained transmission over several seasons, such as H3N2 and pandemic H1N1/09, while diversity of prepandemic H1N1 viruses had until 2009 a noncontinuous transmission pattern consistent with a source/sink model. Irrespective of the shift in the structure of H1N1 diversity circulation with the emergence of the pandemic H1N1/09 strain, US prevalence peaks during the winter months when genetic diversity is at its lowest. This suggests that a dominant strain is generally responsible for epidemics and that monitoring genetic and/or nucleotide diversity in real time could provide public health agencies with an indirect estimate of prevalence.
Security of Wireless Devices using Biological-Inspired RF Fingerprinting Technique

Radio Frequency (RF) fingerprinting is a security mechanism inspired by biological fingerprint identification systems. RF fingerprinting is proposed as a means of providing an additional layer of security for wireless devices. RF fingerprinting classification is performed by selecting an “unknown” signal from the pool, generating its RF fingerprint, and using a classifier to correlate the received RF fingerprint with each profile RF fingerprint stored in the database. Unlike a human biological fingerprint, RF fingerprint of a wireless device changes with the received Signal to Noise Ratio (SNR) and varies due to mobility of the transmitter/receiver and environment. The variations in the features of RF fingerprints affect the classification results of the RF fingerprinting. This chapter evaluates the performance of the KNN and neural network classification for varying SNR. Performance analysis is performed for three scenarios that correspond to the situation, when either transmitter or receiver is mobile, and SNR changes from low to high or vice versa.
miR-375 Inhibits Differentiation of Neurites by Lowering HuD Levels

ABSTRACT Neuronal development and plasticity are maintained by tightly regulated gene expression programs. Here, we report that the developmentally regulated microRNA miR-375 affects dendrite formation and maintenance. miR-375 overexpression in mouse hippocampus potently reduced dendrite density. We identified the predominantly neuronal RNA-binding protein HuD as a key effector of miR-375 influence on dendrite maintenance. Heterologous reporter analysis verified that miR-375 repressed HuD expression through a specific, evolutionarily conserved site on the HuD 3′ untranslated region. miR-375 overexpression lowered both HuD mRNA stability and translation and recapitulated the effects of HuD silencing, which reduced the levels of target proteins with key functions in neuronal signaling and cytoskeleton organization (N-cadherin, PSD-95, RhoA, NCAM1, and integrin α1). Moreover, the increase in neurite outgrowth after brain-derived neurotrophic factor (BDNF) treatment was diminished by miR-375 overexpression; this effect was rescued by reexpression of miR-375-refractory HuD. Our findings indicate that miR-375 modulates neuronal HuD expression and function, in turn affecting dendrite abundance.
The Magic of UI Physics

High-fidelity physics enhances user interfaces with realism that more closely engage users. However, indirect control of UI objects through physical forces interferes with direct control of the user experience, which limits the use of physics in productivity UIs. This paper demonstrates how UI physics should and can be enhanced with magic that violates physically consistent reasoning to allow for control over the user experience. We have found that magical forces such as teleportation, telekinesis, morphing, and lucky shots can be implemented in a straightforward way via a UI physics engine that supports direct constraints on positions. We demonstrate the compatibility of magic and high-fidelity physics by applying such a physics engine to a UI case study. ACM Classification: H5.2 [Information interfaces and presentation]: User Interfaces. Graphical user interfaces. General terms: Design, Human Factors
Autoencoder-derived Features as Inputs to Classification Algorithms for Predicting Well Failures

The invention relates to using autoencoder-derived features for predicting well failures (e.g., rod pump failures) using a machine learning classifier (e.g., a Support Vector Machine (SVMs)). Features derived from dynamometer card shapes are used as inputs to the machine learning classifier algorithm. Hand-crafted features can lose important information whereas autoencoder-derived abstract features are designed to minimize information loss. Autoencoders are a type of neural network with layers organized in an hourglass shape of contraction and subsequent expansion; such a network eventually learns how to compactly represent a data set as a set of new abstract features with minimal information loss. When applied to card shape data, it can be demonstrated that these automatically derived abstract features capture high-level card shape characteristics that are orthogonal to the hand-crafted features. In addition, experimental results show improved well failure prediction accuracy by replacing the hand crafted features with more informative abstract features.
Routing metric for Wireless Mesh Networks

The Wireless Mesh Network (WMN) is a highly capable technology that can offer low-cost and easy-deployable network connectivity to both small-size community networks and large-scale metropolitan networks. As a key emerging technology to provide the next generation broadband networking, WMN combines the advantages of both mobile ad hoc network (MANET) and traditional fixed network, attracting significant industrial and academic attentions. We present a new metric for routing in multi-radio, multihop wireless networks. We focus on wireless networks with stationary nodes, such as community wireless networks. The goal of the metric is to choose a high-throughput path between a source and a destination. Our metric assigns weights to individual links based on the Expected Transmission Time (ETT) of a packet over the link. The ETT is a function of the loss rate and the bandwidth of the link.
Segmentation of Camera Captured Business Card Images for Mobile Devices

Due to huge deformation in the camera captured images, variety in nature of the business cards and the computational constraints of the mobile devices, design of an efficient Business Card Reader (BCR) is challenging to the researchers. Extraction of text regions and segmenting them into characters is one of such challenges. In this paper, we have presented an efficient character segmentation technique for business card images captured by a cell-phone camera, designed in our present work towards developing an efficient BCR. At first, text regions are extracted from the card images and then the skewed ones are corrected using a computationally efficient skew correction technique. At last, these skew corrected text regions are segmented into lines and characters based on horizontal and vertical histogram. Experiments show that the present technique is efficient and applicable for mobile devices, and the mean segmentation accuracy of 97.48% is achieved with 3 mega-pixel (500-600 dpi) images. It takes only 1.1 seconds for segmentation including all the preprocessing steps on a moderately powerful notebook (DualCore T2370, 1.73 GHz, 1GB RAM, 1MB L2 Cache).
Goal-Directed Decision Making with Spiking Neurons

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level.
Anomaly Based Host Intrusion Detection System using semantic based system call patterns

The Host Based Intrusion Detection System (HIDS) is to prevent the host system from being compromised by intruders. To prevent the execution of malicious codes on the host, HIDS monitors the system audit and event logs. But the design of HIDS is very challenging due to the presence of high false alarm rate. This paper mainly focuses on reducing the problem of false alarm rate, using semantic based system call patterns. Here, we make use of the semantic approach to apply on the underlying kernel level system calls which can help understand the anomaly behavior. The semantic tool used is the data dictionary. The data dictionary containing every possible combinations of sequence of system call names of particular phrase length was constructed. The features satisfying the semantic hypothesis are extracted and then normalized. The normalized values are then given as input to the decision engine. The decision engine used is the Extreme Learning Machine - a new type of neural network. Performance was evaluated using the modern ADFA-LD dataset.
Prediction of interconnect delay in logic synthesis

IC designers are now increasingly concerned about the delay due to interconnection wires. In the past, these effects have been largely ignored during logic design-primarily due to their negligible contributions and also because of the difficulty of predicting the wiring resulting from the subsequent layout stage. In this paper, an estimation model is proposed to predict the average wire length for each net in a given gate-level netlist and a particular layout tool. <<ETX>>
Derivation of Adipocytes from Human Endometrial Stem Cells (EnSCs)

Background Due to increasing clinical demand for adipose tissue, a suitable cell for reconstructive adipose tissue constructs is needed. In this study, we investigated the ability of Human Endometrial-derived stem cells (EnSCs) as a new source of mesenchymal stem cells to differentiate into adipocytes. EnSCs are the abundant and easy available source with no immunological response, for cell replacement therapy. Methods Single-cell suspensions of EnSCs were obtained from endometrial tissues from 10 women experiencing normal menstrual cycles, and were cultured at clonal density (10 cells/cm 2) or limiting dilution. Endometrial mesenchymal stem cell markers were examined flow cytometry. These cells were treated with adipogenic-inducing medium for 28 days. The adipogenic differentiation of the EnSC was assessed by cellular morphology and further confirmed by Oil Red O staining and RT-PCR. The BM-MSC differentiated into adipocytes in the presence of adipogenic stimuli for 3 weeks. Results The flow cytometric analysis showed that the cells were positive for CD90, CD105, CD146 and were negative for CD31, CD34.We showed that the key adipocytes marker PPARa was expressed in mRNA level after 28 days post treatment (PT). Conclusion According to our finding, it can be concluded that EnSCs represent a useful in vitro model for human adipogenesis, and provide opportunities to study the stages prior to commitment to the adipocyte lineage.