Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed MIMO Systems for Nomadic Applications Over a Symmetric Interference Channel

A single source communicates with a single destination via a remote wireless multiple-antenna (multiple-input multiple-output (MIMO)) transceiver. The source has access to each of the transmit antennas through a finite-capacity link, and likewise the destination is connected to the receiving antennas via capacity-constrained channels (e.g., as for wired or time-division multiple access (TDMA) channels). Targeting a nomadic communication scenario, in which the remote MIMO transceiver is designed to serve different standards or services, it is assumed that transmitters and receivers are oblivious to the encoding function shared by source and destination. Assuming a Gaussian symmetric interference network as the channel model (as for regularly placed transmitters and receivers), achievable rates are investigated and compared with an upper bound (that holds also for codebook-dependent operation). Closed-form expressions are derived for large numbers of antennas (and in some cases large signal-to-noise ratios (SNRs)), and asymptotics of the achievable rates are studied with respect to either link capacities or SNR. Overall, the analysis points to effective transmission/reception strategies for the distributed MIMO channel at hand, which are optimal under specified conditions. In particular, it is concluded that in certain asymptotic and nonasymptotic regimes there is no loss of optimality in designing the system for nomadic applications (i.e., assuming oblivious transmitters and receivers). Numerical results validate the analysis.
Combining load forecasts from independent experts

The NPower Forecasting Challenge 2015 invited students and professionals worldwide to predict daily energy usage of a group of customers. The BigDEAL team from the Big Data Energy Analytics Laboratory landed a top 3 place in the final leaderboard. This paper presents a refined methodology based on the implementation during the competition. We first build the individual forecasts using several forecast techniques, such as Multiple Linear Regression (MLR), Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Random Forests (RF). We then select a subset of the individual forecasts based on their performance on a validation period, a.k. a. post-sample. Finally we obtain the final forecast by averaging the selected individual forecasts. The forecast combination on average yields a better result than the forecast from a single technique.
Multilinear spectral regularizatioin for Kernel-based multitask learning

Recent research in machine learning witnessed a renewed interest in tensors. In particular, multilinear algebra has been leveraged to derive structured finite dimensional parametric models [1, 2]. In [3] these ideas have been generalized to reproducing kernel Hilbert spaces. The arising framework comprises existing problem formulations, such as tensor completion [4], as well as novel functional formulations. The approach is based on a class of regularizers for tensor product functions, termed multilinear spectral penalties, that is related to spectral regularization for operator estimation [5]. In this work we outline the main ideas and focus on the implications for (multilinear) multitask learning. Multi-task Learning (MTL) aims at simultaneously finding multiple predictive models, each of which corresponds to a learning task. In many cases of interest MTL has been shown to improve over the case where tasks are learned in isolation, see [6] and references therein. Importantly, the approach allows one to make predictions even in absence of training data for one or more of the tasks. Therefore it is suitable to perform transfer learning [7]. Recently [2] has proposed an extension, termed Multilinear Multi-task Learning (MLMTL), to account for multi-modal interactions between the tasks. This is a departure from classical tensorbased methods, where the multilinear decomposition is performed on the input data.
ClothLens Demo: Simultaneous Multi-User Interaction with Shared Content on a Tabletop

We present ClothLens, a prototype that allows simultaneous interaction of multiple users with a digital map shown on a tabletop display. With our prototype interaction with the content by panning or zooming reduces the conflicts due to interferences. ClothLens utilizes the Focus+Context pattern enabling users to create personal lenses on top of the map. The actual content is organized as a physical cloth object that is bend or stretched according to the interactions.
FPGA-based biophysically-meaningful modeling of olivocerebellar neurons

The Inferior-Olivary nucleus (ION) is a well-charted region of the brain, heavily associated with sensorimotor control of the body. It comprises ION cells with unique properties which facilitate sensory processing and motor-learning skills. Various simulation models of ION-cell networks have been written in an attempt to unravel their mysteries. However, simulations become rapidly intractable when biophysically plausible models and meaningful network sizes (>=100 cells) are modeled. To overcome this problem, in this work we port a highly detailed ION cell network model, originally coded in Matlab, onto an FPGA chip. It was first converted to ANSI C code and extensively profiled. It was, then, translated to HLS C code for the Xilinx Vivado toolflow and various algorithmic and arithmetic optimizations were applied. The design was implemented in a Virtex 7 (XC7VX485T) device and can simulate a 96-cell network at real-time speed, yielding a speedup of x700 compared to the original Matlab code and x12.5 compared to the reference C implementation running on a Intel Xeon 2.66GHz machine with 20GB RAM. For a 1,056-cell network (non-real-time), an FPGA speedup of x45 against the C code can be achieved, demonstrating the design's usefulness in accelerating neuroscience research. Limited by the available on-chip memory, the FPGA can maximally support a 14,400-cell network (non-real-time) with online parameter configurability for cell state and network size. The maximum throughput of the FPGA ION-network accelerator can reach 2.13 GFLOPS.
Human-wildlife conflict: proximate predictors of aggression between humans and rhesus macaques in India.

Macaques live in close contact with humans across South and Southeast Asia, and direct interaction is frequent. Aggressive contact is a concern in many locations, particularly among populations of rhesus and longtail macaques that co-inhabit urbanized cities and towns with humans. We investigated the proximate factors influencing the occurrence of macaque aggression toward humans as well as human aggression toward macaques to determine the extent to which human behavior elicits macaque aggression and vice versa. We conducted a 3-month study of four free-ranging populations of rhesus macaques in Dehradun, India from October-December 2012, using event sampling to record all instances of human-macaque interaction (N = 3120). Our results show that while human aggression was predicted by the potential for economic losses or damage, macaque aggression was influenced by aggressive or intimidating behavior by humans as well as recent rates of conspecific aggression. Further, adult female macaques participated in aggression more frequently than expected, whereas adult and subadult males participated as frequently as expected. Our analyses demonstrate that neither human nor macaque aggression is unprovoked. Rather, both humans and macaques are responding to one another's behavior. Mitigation of human-primate conflict, and indeed other types of human-wildlife conflict in such coupled systems, will require a holistic investigation of the ways in which each participant is responding to, and consequently altering, the behavior of the other.
Separate-and-conquer Regression

In this paper a rule learning algorithm for the prediction of a numerical target variable is presented. It is based on the separate-and-conquer strategy and the classification phase is done by a decision list. A new splitpoint generation method is introduced for the efficient handling of numerical attributes. It is shown that the algorithm performs comparable to other regression algorithms where some of them are based on rules and some are not. Additionally a novel heuristic for evaluating the trade-off between consistency and generality of regression rules is introduced. This heuristic features a parameter to directly trade off the rule’s consistency and its generality. We present an optimal setting for this parameter based on an optimization on several data sets.
Effect of SLCO1B3 Haplotype on Testosterone Transport and Clinical Outcome in Caucasian Patients with Androgen-Independent Prostatic Cancer

Purpose: The organic anion transporter OATP1B3, encoded by SLCO1B3, is involved in the transport of steroid hormones. However, its role in testosterone uptake and clinical outcome of prostatic cancer is unknown. This study examined (a) the SLCO1B3 genotype in cancer cells as well as the uptake of testosterone by cells transfected with genetic variants of SLCO1B3; (b) the expression of OATP1B3 in normal prostate, benign prostatic hyperplasia, and prostatic cancer; and (c) the role of SLCO1B3 haplotype on clinical outcome of Caucasian patients with androgen-independent prostatic cancer. Experimental Design:SLCO1B3 genotype was assessed in the NCI-60 panel of tumor cells by sequencing, whereas testosterone transport was analyzed in Cos-7 cells transfected with WT, 334G, and 699A SLCO1B3 variants. OATP1B3 expression in prostatic tissues was examined by fluorescence microscopy, and the relationship between SLCO1B3 haplotypes and survival was examined in patients. Results: Cells transfected with wild-type (334T/699G) SLCO1B3, or with a vector containing either the 334G or 699A variants, actively transported testosterone, whereas its uptake was impaired in cells transfected with a gene carrying both 334G and 699A single nucleotide polymorphisms. Prostatic cancer overexpresses OATP1B3 compared with normal or benign hyperplastic tissue; patients with SLCO1B3 334GG/699AA haplotype showed longer median survival (8.5 versus 6.4 years; P = 0.020) and improved survival probability at 10 years (42% versus 23%; P < 0.023) than patients carrying TT/AA and TG/GA haplotypes. Conclusions: The common SLCO1B3 GG/AA haplotype is associated with impaired testosterone transport and improved survival in patients with prostatic cancer.
Safe and Stable Formation Control with Distributed Multi-Agents Using Adaptive Control and Control Barrier Functions

This manuscript considers the problem of ensuring stability and safety during formation control with distributed multi-agent systems in the presence of parametric uncertainty in the dynamics and limited communication. We propose an integrative approach that combines Control Barrier Functions, Adaptive Control, and connected graphs. A reference model is designed so as to ensure a safe and stable formation control strategy. This is combined with a provably correct adaptive control design that includes a use of a CBF-based safety filter that suitably generates safe reference commands, and employs error-based relaxation (EBR) of Nagumo's Invariance Theorem. Together, it is shown to lead to a guarantee of boundedness, formation control, and forward invariance. Numerical examples are provided to support the theoretical derivations.
The Creativity Styles Questionnaire--Revised

This study describes the development of the Creativity Styles Questionnaire-Revised (CSQ-R; Kumar & Holman, 1989). This questionnaire measures beliefs about and strategies for going about being creative. Participants were 273 freshmen students in a psychology course. Cronbach alphas for the 7 subscales of the CSQ-R ranged between . 45 and . 81, with a median reliability of . 74. Students were divided into high- and low-creativity groups based on 3 self-report measures of creativity capacity. Those in the high-creativity group were more inclined than the other students to report (a) having a stronger belief in unconscious processes, (b) using more techniques to be creative, and (c) being less concerned about developing a final product.
Mood, Self-Esteem, and Counterfactuals: Externally Attributed Moods Limit Self-Enhancement Strategies

Two studies demonstrated that moods can influence counterfactual direction, and that self-esteem and external mood attributions can moderate such influences. This was shown for hypothetical situations (Study 1) and for performances after a laboratory task (Study 2). Although both high self-esteem (HSE) and low self-esteem (LSE) persons generated more downward than upward counterfactuals when in positive moods (Study 1), they diverged in their reactions to negative moods (Studies 1 & 2): HSE persons generated more downward than upward counterfactuals, whereas LSE persons generated more upward than downward counterfactuals. HSE persons also felt better after generating downward counterfactuals when in negative moods (Study 2), suggesting a mood repair strategy. Importantly, in both studies external attributions for moods moderated this pattern. Mood repair strategies were not used when moods were externally attributed, implicating the self and the informational value of moods in this process.
Single Layer Dual band G-shaped patch antenna

Microstrip (MSA) antenna is known for its conformal nature and easy implementation. MSA has the limitation of narrow bandwidth due to its resonance behavior. The concern in the design of MSA is increasing the bandwidth either by Single or by multiband resonance without affecting conformal nature of the antenna. Several concepts were referred for the enhancement of bandwidth and the simple way to enhance the bandwidth is changing the shape of the patch. Creating slots on the patch can change the shape of the antenna. Because of increased discontinuity the radiation increases and hence the bandwidth also increases. This is done by putting slot on the patch so that we can get different shape of the patches. In this paper we get G-shape. The antenna parameters such as gain, return loss etc., for simplicity purpose here we mention some of few parameters that is gain, return loss and bandwidth. There are two bands can be obtained. The centre frequencies for these two bands are 3GHz, 3.8GHz,. The gain for these frequencies is 7.5dBi, 2.4dBi respectively. The return loss is -17dBi and -15dBi respectively and the percentage of the bandwidth is. HFSS the Finite Element Method based analysis software is used to design the Patch Antenna and to simulate the results.
The X-factor: visualizing undisturbed root architecture in soils using X-ray computed tomography.

Although roots play a crucial role in plant growth and development through their acquisition and delivery of water and nutrients to the above-ground organs, our understanding of how they interact with their immediate soil environment largely remains a mystery as the opaque nature of soil has prevented undisturbed in situ root visualization (Perret et al., 2007). However, new developments in non-invasive techniques such as X-ray computed tomography (CT) provide, for the first time, an exciting opportunity to examine detailed root architecture in three dimensions (3-D) in undisturbed soil cores (Fig. 1). Although other non-invasive 3-D visualization procedures exist, X-ray CT is viewed as the most appropriate technique for studies of soil:root interactions as the presence of iron and manganese ions may provide interference when alternative techniques such as Nuclear Magnetic Resonance (NMR) are used (Heeraman et al., 1997). Detailed understanding of interactions between roots and their immediate soil environment is vital when considering issues such as land degradation as soil structure is a primary factor determining the availability of edaphic resources such as water and nutrients (Lynch, 1995), and is extrinsically linked to plant productivity (Moran et al., 2000). In view of the rapidly increasing human global population and the threat posed by climate change, maximizing crop yields and developing sustainable soil management strategies are vital for food security. X-ray CT overcomes some of the limitations associated with previous methodologies for studying roots by providing Fig. 1. X-ray CT image of roots of a 3-week-old Zea mays (L.) plant grown in a soil column (loamy sand, Newport series). 1 pixel1⁄444 lm.
Boolean Analyzer - An Algorithm That Uses A Probabilistic Interestingness Measure to find Dependency/Association Rules In A Head Trauma Data

A new, binary-based technique is presented for finding dependency/association rules called the Boolean Analyzer (BA). With initial guidance from a domain user or domain expert, BA is given one or more metrics to partition the entire data set. This leads to analyzing the implicit domain knowledge and creating weighted rules in the form of boolean expressions. To augment the analysis of the rules produced, we can additionally apply a probabilistic interestingness measure (PIM) to order the generated rules based on event dependency, where events are combinations of primed and unprimed variables. Following our discussion of the basic BA algorithm, our paper will present a case study on clinical head trauma data. BA able to find rules, where the most significant rules were those that had a high PIM. We believe that BA has broad applicability in the medical domain, and hope that our presentation here can stimulate other creative applications of the technique.
Asymptotics of predictive distributions driven by sample means and variances

Let $\alpha_n(\cdot)=P\bigl(X_{n+1}\in\cdot\mid X_1,\ldots,X_n\bigr)$ be the predictive distributions of a sequence $(X_1,X_2,\ldots)$ of $p$-variate random variables. Suppose $$\alpha_n=\mathcal{N}_p(M_n,Q_n)$$ where $M_n=\frac{1}{n}\sum_{i=1}^nX_i$ and $Q_n=\frac{1}{n}\sum_{i=1}^n(X_i-M_n)(X_i-M_n)^t$. Then, there is a random probability measure $\alpha$ on $\mathbb{R}^p$ such that $\alpha_n\rightarrow\alpha$ weakly a.s. If $p\in\{1,2\}$, one also obtains $\lVert\alpha_n-\alpha\rVert\overset{a.s. }\longrightarrow 0$ where $\lVert\cdot\rVert$ is total variation distance. Moreover, the convergence rate of $\lVert\alpha_n-\alpha\rVert$ is arbitrarily close to $n^{-1/2}$. These results (apart from the one regarding the convergence rate) still apply even if $\alpha_n=\mathcal{L}_p(M_n,Q_n)$, where $\mathcal{L}_p$ belongs to a class of distributions much larger than the normal. Finally, the asymptotic behavior of copula-based predictive distributions (introduced in [13]) is investigated and a numerical experiment is performed.
Studying the archives of the Hellenic Society of Psychoanalytic Psychotherapy: An outline of its historical course

Abstract The authors attempt to outline the historical course of the Hellenic Society of Psychoanalytic Psychotherapy (HSPP). They put forward several hypotheses concerning the dynamics of its foundation and evolution. The HSPP was founded in 1977 by five Greek psychoanalysts and psychoanalytic psychotherapists trained abroad, after three decades of fruitless attempts at establishing psychoanalysis in Greece. The authors sustain that the foundation of the HSPP addressed the complex problems of Greek society in the 1970s. The request for the founding of an institution for psychoanalytic therapy and training can be linked to a search for new orientations in thought that would enhance the working-through of traumas that had marked Greek society in the previous decades; these traumas played a role in the insurmountable problems in establishing psychoanalysis in Greece and in the difficulties met by the emancipation of Greek psychiatry from asylum-centered practice. The HSPP remained the only psychoanalytic institution in Greece until the foundation of the Hellenic Psychoanalytic Society in 1984. One of the main traits of its historical course has been the effort to shape a psychoanalytic organization directed towards the clinical reality of Greek society.
Comparative study of central decision makers versus groups of evolved agents trading in equity markets

This paper investigates the process of deriving a single decision solely based on the decisions made by a population of experts. Four different amalgamation processes are studied and compared among one another, collectively referred to as central decision makers. The expert, also referred to as reference, population is trained using a simple genetic algorithm using crossover, elitism and immigration using historical equity market data to make trading decisions. Performance of the trained agent populationâ€™s elite, as determined by results from testing in an out-of-sample data set, is also compared to that of the centralized decision makers to determine which displays the better performance. Performance was measured as the area under their total assets graph over the out-of-sample testing period to avoid biasing results to the cut off date using the more traditional measure of profit. Results showed that none of the implemented methods of deriving a centralized decision in this investigation outperformed the evolved and optimized agent population. Further, no difference in performance was found between the four central decision makers
BIM-Integrated Construction Operation Simulation for Just-In-Time Production Management

Traditional construction planning, which depends on historical data and heuristic modification, prevents the integration of managerial details such as productivity dynamics. Specifically, the distance between planning and execution brings cost overruns and duration extensions. To minimize variations, this research presents a Building Information Modeling (BIM)-integrated simulation framework for predicting productivity dynamics at the construction planning phase. To develop this framework, we examined critical factors affecting productivity at the operational level, and then forecast the productivity dynamics. The resulting plan includes specific commands for retrieving the required information from BIM and executing operation simulations. It consists of the following steps: (1) preparing a BIM model to produce input data; (2) composing a construction simulation at the operational level; and (3) obtaining productivity dynamics from the BIM-integrated simulation. To validate our framework, we applied it to a structural steel model; this was due to the significance of steel erections. By integrating BIM with construction operation simulations, we were able to create reliable construction plans that adapted to project changes. Our results show that the developed framework facilitates the reliable prediction of productivity dynamics, and can contribute to improved schedule reliability, optimized resource allocation, cost savings associated with buffers, and reduced material waste.
Data Assimilation by Conditioning of Driving Noise on Future Observations

Conventional recursive filtering approaches, designed for quantifying the state of an evolving stochastic dynamical system with intermittent observations, use a sequence of i) an uncertainty propagation step followed by ii) a step where the associated data is assimilated using Bayes' rule. Alternatively, the order of the steps can be switched to i) one step ahead data assimilation followed by ii) uncertainty propagation. In this paper, we apply this smoothing-based sequential filter to systems driven by random noise, however with the conditioning on future observation not only to the system variable but to the driving noise. Our research reveals that, for the nonlinear filtering problem, the conditioned driving noise is biased by a nonzero mean and in turn pushes forward the filtering solution in time closer to the true state when it drives the system. As a result our proposed method can yield a more accurate approximate solution for the state estimation problem.