Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient channel assignment algorithms for infrastructure WLANs under dense deployment

It is well known that WLAN based on the IEEE 802.11 standard suffers from interference and scalability problems due to a limited number of non-overlapping channels. In order to mitigate the interference problem, channel assignment algorithms have been a popular research topic in recent years. It has been shown that such algorithms can greatly reduce the interference among wireless access points. However, in this paper we show that previously proposed channel assignment algorithms may lead to an increased number of hidden nodes in dense network deployments. We also show that this can significantly decrease the performance of the network. Furthermore, we present results from experiments showing that the RTS/CTS mechanism is unable to solve the hidden node problem in infrastructure WLANs and therefore, careful consideration needs to be taken when choosing channel assignment strategies in densely deployed wireless networks. To this end, we propose two novel channel assignment algorithms. Using a simulation study, we show that the proposed algorithms can outperform traditional channel assignment in densely deployed scenarios, in terms of QoS sensitive VoIP support without compromising the aggregate throughput and are therefore a better performing alternative in such settings.
Reproducibility of Post-Amphetamine [11C]FLB 457 Binding to Cortical D2/3 Receptors

In a recent positron emission tomography (PET) study, we demonstrated the ability to measure amphetamine-induced dopamine (DA) release in the human cortex with the relatively high affinity dopamine D2/3 radioligand [11C]FLB 457. Herein we report on reproducibility and reliability of [11C]FLB 457 binding potential relative to non-displaceable uptake (BPND) following an acute amphetamine challenge. Ten healthy human subjects were studied twice with [11C]FLB 457 following an acute amphetamine (oral, 0.5 mg kg-1 dose) challenge on two-separate days approximately one week apart. D2/3 receptor binding parameters were estimated using a two-tissue compartment kinetic analysis in the cortical regions of interest and cerebellum (reference region). The test-retest variability and intraclass correlation coefficient were assessed for distribution volume (VT), binding potential relative to plasma concentration (BPP), and BPND of [11C]FLB 457. The test-retest variability of [11C]FLB 457 VT, BPP and BPND were ≤ 17%, 22% and 11% respectively. These results, which are consistent with the published test-retest variability for this ligand measured under baseline conditions demonstrate that the post-amphetamine [11C]FLB 457 BPND is reproducible. These data further support the use [11C]FLB 457 and amphetamine to characterize cortical dopamine transmission in neuropsychiatric disorders.
Please Scroll down for Article Contemporary Physics Uncollapsing the Wavefunction by Undoing Quantum Measurements Uncollapsing the Wavefunction by Undoing Quantum Measurements

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. We review and expand on recent advances in theory and experiments concerning the problem of wavefunction uncollapse: given an unknown state that has been disturbed by a generalised measurement, restore the state to its initial configuration. We describe how this is probabilistically possible with a subsequent measurement that involves erasing the information extracted about the state in the first measurement. The general theory of abstract measurements is discussed, focusing on quantum information aspects of the problem, in addition to investigating a variety of specific physical situations and explicit measurement strategies. Several systems are considered in detail: the quantum double dot charge qubit measured by a quantum point contact (with and without Hamiltonian dynamics), the superconducting phase qubit monitored by a SQUID detector, and an arbitrary number of entangled charge qubits. Furthermore, uncollapse strategies for the quantum dot electron spin qubit, and the optical polarisation qubit are also reviewed. For each of these systems the physics of the continuous measurement process, the strategy required to ideally uncollapse the wavefunction, as well as the statistical features associated with the measurement are discussed. We also summarise the recent experimental realisation of two of these systems, the phase qubit and the polarisation qubit. 1. Introduction The irreversibility of quantum measurement is an axiomatic property of textbook quantum mechanics [1]. In his famous article Law without law, John Wheeler expresses the idea with poetic flare: 'We are dealing with [a quantum] event that makes itself known by an irreversible act of amplification, by an indelible record, an act of registration' [2]. However, it has been gradually recognised that the textbook treatment of an instantaneous wavefunction collapse is really a very special case of what is in general a dynamical process …
Is there supply distortion in the green box? An acreage response approach.

The shift of the farm subsidies toward programs classified as being decoupled income supports in the WTO’s URAA raises the question of their true impact on production and trade. In this study, we measured the acreage effects of the Canadian whole farm programs under uncertainty. Based on the theoretical discussions regarding the role of the insurance effect in acreage decisions, we extend the theoretical restrictions examined by Chavas and Holt (1990)which enables us to include this effect in our model specification. Hence, we modified the expected utility maximization framework (under the hypothesis that farmers are risk averse) developed by Chavas and Holt (1990) and derived three distinct effects: market effects, the wealth effect, and the insurance effect.
When Interference Helps: Increasing Executive Load to Facilitate Deception Detection in the Concealed Information Test

The possibility to enhance the detection efficiency of the Concealed Information Test (CIT) by increasing executive load was investigated, using an interference design. After learning and executing a mock crime scenario, subjects underwent three deception detection tests: an RT-based CIT, an RT-based CIT plus a concurrent memory task (CITMem), and an RT-based CIT plus a concurrent set-shifting task (CITShift). The concealed information effect, consisting in increased RT and lower response accuracy for probe items compared to irrelevant items, was evidenced across all three conditions. The group analyses indicated a larger difference between RTs to probe and irrelevant items in the dual-task conditions, but this difference was not translated in a significantly increased detection efficiency at an individual level. Signal detection parameters based on the comparison with a simulated innocent group showed accurate discrimination for all conditions. Overall response accuracy on the CITMem was highest and the difference between response accuracy to probes and irrelevants was smallest in this condition. Accuracy on the concurrent tasks (Mem and Shift) was high, and responses on these tasks were significantly influenced by CIT stimulus type (probes vs. irrelevants). The findings are interpreted in relation to the cognitive load/dual-task interference literature, generating important insights for research on the involvement of executive functions in deceptive behavior.
Designing an Application-Aware Routing Protocol for Wireless Sensor Networks

Routing protocols that can facilitate application- specific service guarantee in wireless sensor networks (WSN) constitute one of the key design objectives of current WSN research. Since energy-efficient routing protocols in literature do not offer a complete framework for service differentiation, a newer approach is warranted. Formulating such routing approach requires the adoption of different cost metrics that can parameterize the application-specific requirements. To this effect, this paper proposes an application-aware routing protocol (AARP) that considers battery power, data transaction reliability and end-to-end delay for service differentiation. Two mathematical models namely analytical hierarchical process (AHP) and grey relational analysis (GRA) are incorporated for intermediate node selection (ranking and subsequent selection based on local weight calculation) for data transaction purposes. As demonstrated by the simulation results, the proposed routing approach offers service configurability across a range of applications.
Towards tactile expressions of emotion through mediated touch

In this paper we investigate the expression of emotions through mediated touch. Participants used the Tactile Sleeve for Social Touch (TaSST), a wearable sleeve that consists of a pressure sensitive input layer, and a vibration motor output layer, to record a number of expressions of discrete emotions. The aim was to investigate if these participants could make meaningful distinctions in their tactile expressions of the emotions.
Rules and Restraint: Government Spending and the Design of Institutions

Government spending has increased dramatically in the United States since World War II despite the many rules intended to rein in the insatiable appetite for tax revenue most politicians seem to share. Drawing on examples from the federal and state governments, "Rules and Restraint" explains in lucid, nontechnical prose why these budget rules tend to fail, and proposes original alternatives for imposing much-needed fiscal discipline on our legislators. One reason budget rules are ineffective, David M. Primo shows, is that politicians often create and preserve loopholes to protect programs that benefit their constituents. Another reason is that legislators must enforce their own provisions, an arrangement that is seriously compromised by their unwillingness to abide by rules that demand short-term sacrifices for the sake of long-term gain. Convinced that budget rules enacted through such a flawed legislative process are unlikely to work, Primo ultimately calls for a careful debate over the advantages and drawbacks of a constitutional convention initiated by the states - a radical step that would bypass Congress to create a path toward change.
Metric Spaces Admitting Low-distortion Embeddings into All n-dimensional Banach Spaces

Abstract For a fixed $K\,\gg \,1$ and $n\,\in \,\mathbb{N}$ , $n\,\gg \,1$ we study metric spaces which admit embeddings with distortion $\le \,K$ into each $n$ -dimensional Banach space. Classical examples include spaces embeddable into log $n$ -dimensional Euclidean spaces, and equilateral spaces. We prove that good embeddability properties are preserved under the operation of metric composition of metric spaces. In particular, we prove that $n$ -point ultrametrics can be embedded with uniformly bounded distortions into arbitrary Banach spaces of dimension $\log \,n$ . The main result of the paper is a new example of a family of finite metric spaces which are not metric compositions of classical examples and which do embed with uniformly bounded distortion into any Banach space of dimension $n$ . This partially answers a question of G. Schechtman.
Task decomposition and module combination based on class relations: a modular neural network for pattern classification

In this paper, we propose a new method for decomposing pattern classification problems based on the class relations among training data. By using this method, we can divide a K-class classification problem into a series of ((2)(K)) two-class problems. These two-class problems are to discriminate class Ci from class Cj for i=1, . , K and j = i+1, while the existence of the training data belonging to the other K-2 classes is ignored. If the two-class problem of discriminating class Ci from class Cj is still hard to be learned, we can further break down it into a set of two-class subproblems as small as we expect. Since each of the two-class problems can be treated as a completely separate classification problem with the proposed learning framework, all of the two-class problems can be learned in parallel. We also propose two module combination principles which give practical guidelines in integrating individual trained network modules. After learning of each of the two-class problems with a network module, we can easily integrate all of the trained modules into a min-max modular (M3) network according to the module combination principles and obtain a solution to the original problem. Consequently, a large-scale and complex K-class classification problem can be solved effortlessly and efficiently by learning a series of smaller and simpler two-class problems in parallel.
Workpiece Posture Measurement and Intelligent Robot Grasping Based on Monocular Vision

Industrial robots have been widely applied in grasping static workpieces, but the measurement method for the three-dimensional position of known specifications and random location of online workpieces still cannot meet the application requirements of the industrial field. In this paper, system structure and installation design of hardware were introduced firstly. Then, image pre-processing and feature extraction were established, composing of image enhancement, image smooth, edge detection, corner detection and angle calculation. Finally, a monocular vision-guided robot grasping device platform was established as well as corresponding software control platform. Experimental results indicated that the monocular vision system could quickly identify the workpiece pose with high measurement accuracy and that the robot could successfully grasp random location workpieces.
Cantonal Differences in Health Care Premium Subsidies in Switzerland

We study health care premium subsidies in the Swiss cantons in order to understand the reasons behind the substantial cross-cantonal variation in households' premium load, i.e., the share of disposable income that is spent on premiums after the subsidy. Cantons' financial situation is of particular interest in this regard, because the premium subsidies aim at reducing the premium load for lower income groups in order to ensure universal access to health care at affordable costs. Thus, variation in premium load is meant to reflect underlying differences between cantons in health care and overall living costs, or different preferences of the electorate with regards to social policy, but not budgetary considerations of cantons. We develop a premium subsidy calculation model based on cantonal regulations and apply it to households in the Swiss Household Panel to assess the effect of cantonal budget tightness on households' premium load from 2004-2012. Our analysis is based on panel regression methods and a difference-in- differences model in order to take into account unobserved heterogeneity and simultaneity concerns. The results indicate that there is a significant and negative relationship between the budget of a canton and the premium load of households
Efficiency of Asset Markets with Asymmetric Information

We examine the welfare effects of costly information acquistion in a version of the Grossman-Stiglitz (1980) exchange economy in which all traders are fully rational. We find, as emphasized by Hirschleifer, that information gathering leads the suboptimal risk sharing. Furthermore, information gathering worsens the equilibrium risk-return tradeoff faced by investors. We show that all investors would be better off if the information were not available and that the welfare costs of information gathering are greater when (i) the cost of acquiring information is lower; (ii) the prior return variance of the risky asset is higher; and (iii) the quality of the information is higher. We also show that the relative welfare costs to investors depends on the combination of their endowment and preferences.
Identifying Ragas in Indian Music

In this work, we propose a method to identify the ragas of an Indian Carnatic music signal. This has several interesting applications in digital music indexing, recommendation and retrieval. However, this problem is hard due to (i) the absence of a fixed frequency for a note (ii) relative scale of notes (iii) oscillations around a note, and (iv) improvisations. In this work, we attempt the raga classification problem in a non-linear SVM framework using a combination of two kernels that represent the similarities of a music signal using two different features-pitch-class profile and n-gram distribution of notes. This differs from the previous pitch-class profile based approaches where the temporal information of notes is ignored. We evaluated the proposed approach on our own raga dataset and Comp Music dataset and show an improvement of 10.19% by combining the information from two features relevant to Indian Carnatic music.
Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

The Global Precipitation Mission (GPM) Core Observatory that was launched on 27 February 2014 ushered in a new era for estimating precipitation from satellites. Based on their high spatial–temporal resolution and near global coverage, satellite-based precipitation products have been applied in many research fields. The goal of this study was to quantitatively compare two of the latest GPM-era satellite precipitation products (GPM IMERG and GSMap-Gauge Ver. 6) with a network of 840 precipitation gauges over the Chinese mainland. Direct comparisons of satellite-based precipitation products with rain gauge observations over a 20 month period from April 2014 to November 2015 at 0.1° and daily/monthly resolutions showed the following results: Both of the products were capable of capturing the overall spatial pattern of the 20 month mean daily precipitation, which was characterized by a decreasing trend from the southeast to the northwest. GPM IMERG overestimated precipitation by approximately 0.09 mm/day while GSMap-Gauge Ver. 6 underestimated precipitation by −0.04 mm/day. The two satellite-based precipitation products performed better over wet southern regions than over dry northern regions. They also showed better performance in summer than in winter. In terms of mean error, root mean square error, correlation coefficient, and probability of detection, GSMap-Gauge was better able to estimate precipitation and had more stable quality results than GPM IMERG on both daily and monthly scales. GPM IMERG was more sensitive to conditions of no rain or light rainfall and demonstrated good capability of capturing the behavior of extreme precipitation events. Overall, the results revealed some limitations of these two latest satellite-based precipitation products when used over the Chinese mainland, helping to characterize some of the error features in these datasets for potential users.
Descending commands to an insect leg controller network cause smooth behavioral transitions

Biological inspiration has long been pursued as a key to more efficient, agile and elegant control in robotics. It has been a successful strategy in the design and control of robots with both biologically abstracted and biomimetic designs. Behavioral studies have resulted in a good understanding of the mechanics of certain animals. However, without a better understanding of their nervous systems, the biologically-inspired observation-based approach was limited. The findings of Hess and Büschges, and Ekeberg et al. describing the neural mechanisms of stick insect intra-leg joint coordination have made it possible to control models of insect legs with a network of neural pathways they found in the animal's thoracic ganglia. Our work with this model, further informed by cockroach neurobiological studies performed in the Ritzmann lab, has led to LegConNet (Leg Controller Network). In this paper we show that LegConNet controls the forward stepping motion of a robotic leg. With hypothesized additional pathways, some later confirmed by neurobiology, it can smoothly transition the leg from forward stepping to turning movements. We hypothesize that commands descending from a higher center in the nervous system inhibit or excite appropriate local neural pathways and change thresholds, which, in turn, create a cascade of reflexes resulting in behavioral transitions.
0 1 / 2 0 0 5 Behaviour of the UMDA c algorithm with truncation selection on monotone functions

Of late, much progress has been made in developing Estimation of Distribution Algorithms (EDA), algorithms that use probabilistic modelling of high quality solutions to guide their search. While experimental results on EDA behaviour are widely available, theoretical results are still rare. This is especially the case for continuous EDA. In this article, we develop theory that predicts the behaviour of the Univariate Marginal Distribution Algorithm in the continuous domain (UMDAc) with truncation selection on monotonous fitness functions. Monotonous functions are commonly used to model the algorithm behaviour far from the optimum. Our result includes formulae to predict population statistics in a specific generation as well as population statistics after convergence. We find that population statistics develop identically for monotonous functions. We show that if assuming monotonous fitness functions, the distance that UMDAc travels across the search space is bounded and solely relies on the percentage of selected individuals and not on the structure of the fitness landscape. This can be problematic if this distance is too small for the algorithm to find the optimum. Also, by wrongly setting the selection intensity, one might not be able to explore the whole search space.
Bayesian Inference for Genomic Data Integration Reduces Misclassification Rate in Predicting Protein-Protein Interactions

Protein-protein interactions (PPIs) are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL), to lower the misclassification rate (both false positives and negatives) through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased sources. Extensive studies indicate that NBEL is significantly more robust than the classic naïve Bayes to unreliable, error-prone and contaminated data. On a large human data set our NBEL approach predicts many more PPIs than naïve Bayes. This suggests that previous studies may have large numbers of not only false positives but also false negatives. The validation on two human PPIs datasets having high quality supports our observations. Our experiments demonstrate that it is feasible to predict high-throughput PPIs computationally with substantially reduced false positives and false negatives. The ability of predicting large numbers of PPIs both reliably and automatically may inspire people to use computational approaches to correct data errors in general, and may speed up PPIs prediction with high quality. Such a reliable prediction may provide a solid platform to other studies such as protein functions prediction and roles of PPIs in disease susceptibility.