Miracle-dz/UniFAR-FTUs huggingface dataset:
Interference Cancellation Based Detection for V-BLAST With Diversity Maximizing Channel Partition

Multiple-input multiple-output (MIMO) systems achieve very high bandwidth efficiencies through spatial multiplexing. However, the complexity of optimal detection in such systems motivates the need for more practical alternatives. Recently, a suboptimal lower complexity detection scheme called ¿generalized parallel interference cancellation¿ (GPIC), with close to optimal performance, was introduced. The reported performance of GPIC, however, was assessed by computer simulations only. In this paper, we show that with its original design, GPIC does not always provide close to optimal performance. Based on a diversity analysis of GPIC like techniques, we propose two new improved algorithms, referred to as Sel-MMSE and Sel-MMSE-OSIC, and derive sufficient conditions for achieving optimal performance asymptotically. We also provide a complexity analysis of these two schemes, and show that for large constellation sizes it is lower than the original GPIC. While still more complex than the fixed complexity sphere decoder by a factor in the range of 2-3 (for most configurations), our algorithms are also applicable to undetermined MIMO systems. Simulations results confirm that the new schemes provide maximal diversity gains. Furthermore, Sel-MMSE-OSIC provides a significant gain over Sel-MMSE, making its performance nearly indistinguishable from optimal for all signal-to-noise ratio (SNR) levels.
A Large-Scale Reconfigurable Analog Signal Processor (RASP) IC

The reconfigurable analog signal processor (RASP), one of the first large-scale field-programmable analog arrays (FPAAs), is composed of 56 computational analog blocks (CABs). Each CAB contains various levels of analog computational granularity utilizing over 50,000 programmable analog elements. Bias currents are programmable to within 0.2% from 100 pA to greater than 3 muA. Internal bandwidths are greater than 50 MHz, and the kT/C noise can be adjusted using the drawn capacitances and routing network parasitics. A range of compiled circuits and resulting signal processing systems are presented
Smooth RRT-connect: An extension of RRT-connect for practical use in robots

We propose a new extend function for Rapidly-Exploring Randomized Tree (RRT) algorithms that expands along a curve, obeying velocity and acceleration limits, rather than using straight-line trajectories. This results in smooth, feasible trajectories that can readily be applied in robotics applications. Our main focus is the implementation of such methods on RoboSimian, a quadruped robot competing in the DARPA Robotics Challenge (DRC). Planning in a high-dimensional space is also a large consideration in the evaluation of the techniques discussed in this paper as motion planning for RoboSimian requires a search over a 16-dimensional space. In our experiments, we show that our approach produces results that are comparable to the standard RRT solutions in a two-dimensional space and significantly outperforms the latter in a higher-dimensional setting both in computation time and in algorithm reliability.
An On-Demand Virtual Backbone Broadcast Protocol for VANET

Broadcast mechanisms are widely used in wireless ad-hoc networks, in this paper we proposed a novel on-demand virtual backbone broadcast protocol (OVB-CAST) for VANET. Our OVB-CAST supports safety applications based on WAVE architecture and it provides better adaptability and reliability than some other protocols by its fitness calculator and the on-demand multiple backbones. And we give a comparison between OVB-CAST and other broadcast protocols to show the advantages.
Corn Gluten Hydrolysate Affects the Time-Course of Metabolic Changes Through Appetite Control in High-Fat Diet-Induced Obese Rats

This study first investigated the effects of corn gluten hydrolysate (CGH) (1.5 g/day) administration for 7 days on appetite-responsive genes in lean Sprague-Dawley (SD) rats. In a second set of experiments, the metabolic changes occurring at multiple time points over 8 weeks in response to CGH (35.33% wt/wt) were observed in high-fat (HF, 60% of energy as fat) diet-fed SD rats. In lean rats, the hypothalamus neuropeptide-Y and proopiomelanocortin mRNA levels of the CGH group were significantly changed in response to CGH administration. In the second part of the study, CGH treatment was found to reduce body weight and perirenal and epididymal fat weight. CGH also prevented an increase in food intake at 2 weeks and lowered plasma leptin and insulin levels in comparison with the HF group. This reduction in the plasma and hepatic lipid levels was followed by improved insulin resistance, and the beneficial metabolic effects of CGH were also partly related to increases in plasma adiponectin levels. The Homeostasis Model of Assessment - Insulin Resistance (HOMA-IR), an index of insulin resistance, was markedly improved in the HF-CGH group compared with the HF group at 6 weeks. According to the microarray results, adipose tissue mRNA expression related to G-protein coupled receptor protein signaling pathway and sensory perception was significantly improved after 8 weeks of CGH administration. In conclusion, the present findings suggest that dietary CGH may be effective for improving hyperglycemia, dyslipidemia and insulin resistance in diet-induced obese rats as well as appetite control in lean rats.
Chapter 4 Conclusion 4.1 Progression of Single Particle Analysis

To avoid the challenges of crystallization and the size limitations of nuclear magnetic resonance spectroscopy, it has long been hoped that single-particle cryo-electron microscopy would eventually produce atomically interpretable maps. Steady progress towards this goal has been made (Frank 2002), led by reconstructions of large icosahedral viruses, whose 60-fold symmetry, size, and rigid architecture all facilitate precise image alignment. 3D single-particle reconstructions of virus particles from electron micrographs were first accomplished by Fourier synthesis in 1970 (Crowther, Amos et al. 1970). By the turn of the 21 st Century, single particle techniques had already achieved sub-nanometer resolutions (Bottcher, Wynne et al. 1997; Conway, Cheng et al. 1997; Trus, Roden et al. 1997) but were still limited in resolution by various factors (Baker, Olson et al. 1999; van Heel, Gowen et al. 2000). The difficulty in modeling some of these factors led to the lack of accurate predictions about the severity of each of these limits and it was unclear which was the most dominant limit. Thus, when I began my thesis work in 2002, I chose to address two of these problems, namely the lack of computing power for high-resolution reconstructions and the depth of field or, equivalently, the Ewald sphere curvature problem (DeRosier 2000), as they were best suited to my interests and abilities.
A reconfigurable data-driven ALU for Xputers

A reconfigurable data-driven datapath architecture for ALUs is presented which may be used for custom computing machines (CCMs), Xputers (a class of CCMs) and other adaptable computer systems as well as for rapid prototyping of high speed datapaths. Fine grained parallelism is achieved by using simple reconfigurable processing elements which are called datapath units (DPUs). The word-oriented datapath simplifies the mapping of applications onto the architecture. Pipelining is supported by the architecture. The programming environment allows automatic mapping of the operators from high level descriptions. Two implementations, one by FPGAs and one with standard cells are shown. <<ETX>>
Stanford Laptop Orchestra (SLOrk)

In the paper, we chronicle the instantiation and adventures of the Stanford Laptop Orchestra (SLOrk), an ensemble of laptops, humans, hemispherical speaker arrays, interfaces, and, more recently, mobile smart phones. Motivated to deeply explore computer-mediated live performance, SLOrk provides a platform for research, instrument design, sound design, new paradigms for composition, and performance. It also offers a unique classroom combining music, technology, and live performance. Founded in 2008, SLOrk was built from the ground-up by faculty and students at Stanford University's Center for Computer Research in Music and Acoustics (CCRMA). This document describes 1) how SLOrk was built, 2) its initial performances, and 3) the Stanford Laptop Orchestra as a classroom. We chronicle its present, and look to its future.
A Knowledge Base for D. melanogaster Gene Interactions Involved in Pattern Formation

The understanding of pattern formation in Drosophila requires the handling of the many genetic and molecular interactions which occur between developmental genes. For that purpose, a knowledge base (KNIFE) has been developed in order to structure and manipulate the interaction data. KNIFE contains data about interactions published in the literature and gathered from various databases. These data are structured in an object knowledge representation system into various interrelated entities. KNIFE can be browsed through a WWW interface in order to select, classify and examine the objects and their references in other bases. It also provides specialised biological tools such as interaction network manipulation and diagnosis of missing interactions.
Programmable ferroelectric tunnel memristor

We report an analogously programmable memristor based on genuine electronic resistive switching combining ferroelectric switching and electron tunneling. The tunnel current through an 8 unit cell thick epitaxial Pb(Zr[0.2]Ti[0.8])O[3] film sandwiched between La[0.7]Sr[0.3]MnO[3] and cobalt electrodes obeys the Kolmogorov-Avrami-Ishibashi model for bidimensional growth with a characteristic switching time in the order of 10^-7 seconds. The analytical description of switching kinetics allows us to develop a characteristic transfer function that has only one parameter viz. the characteristic switching time and fully predicts the resistive states of this type of memristor.
Optimal number of users in wireless networks: A flat rate pricing

We study revenue-maximizing in a wireless system where interested users share a common spectrum and interfere with each other. Capacity is increased in proportion of the number of users. Our objective is to design a scheme that achieves an optimal solution for the provider with respect to users strategy, bandwidth management and some fairness criteria. We consider a flat rate pricing for users. Two cases are studied: firstly, an approximation to the original problem when the provider has a perfect knowledge about the channel state of each user is proposed. In the second case, a decision theoretic approach based on a POMDP framework is elaborated when the provider has only a partial information on the system state. Additionally, we provide numerical results that illustrate the performance of the proposed solutions.
Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction

This paper describes our participation in SemEval-2016 Task 5 for Subtask 1, Slot 2. The challenge demands to ﬁnd domain spe-ciﬁc target expressions on sentence level that refer to reviewed entities. The detection of target words is achieved by using word vectors and their grammatical dependency relationships to classify each word in a sentence into target or non-target . A heuristic based function then expands the classiﬁed target words to the whole target phrase. Our system achieved an F1 score of 56.816% for this task.
Local Smoothness Enforced Cost Volume Regularization for Fast Stereo Correspondence

A novel cost volume regularization algorithm is proposed. It successfully combines weighted cost aggregation in local stereo methods and smoothness constraints in global stereo methods. By enforcing local smoothness during the weighted cost aggregation on the horizontal tree structure, cost distribution can be well regularized. Accurate disparity results with sharp boundary can be generated while keeping extremely low computational amount. A new post-processing method is also proposed. The whole stereo correspondence algorithm achieves competitive performance on the Middlebury stereo testbed in terms of both accuracy and speed.
Assessing Self-Determined Work Motivation in People with Severe Mental Illness: A Factor-Analytic Approach

Background: Self-determination theory (SDT) has increasingly been used as a theoretical framework for evaluating key elements of the recovery paradigm in rehabilitation and mental health services research and policy-making for people living with severe mental illnesses. Purpose: The purpose of this study was to validate the Behavioral Regulation in Work Questionnaire (BRWQ), an adaptation of the Behavioral Regulation in Exercise Questionnaire-2, as a measure of self-determined work motivation for use in psychiatric rehabilitation settings. Methods: One hundred and twenty-four individuals with severe mental illness were recruited from eight Clubhouse programmes in Hawaii. Factorial validity of the BRWQ was evaluated using exploratory factor analysis. Findings: Principal components analysis of the BRWQ yielded four factors (amotivation, external regulation, introjection and autonomous motivation). The autonomous motivation factor was found to be significantly related to other SDT constructs, including competency, relatedness, outcome expectancy and vocational rehabilitation engagement. Conclusions: The BRWQ is a psychometrically sound SDT measure for assessing self-determined work motivation and could contribute to the use of self-determination as a paradigm for improving recovery and employment outcomes of people with severe mental illness in rehabilitation settings.
Revisiting TV coverage estimation with measurement-based statistical interpolation

Per FCC rules, secondary users in TV White Spaces must operate only within spectrum subject to a no-harmful-interference condition to existing primary receivers. In effect, this translates into a protection region around every TV transmitter, wherein secondary nodes must not transmit (on the same channel). This is implemented by requiring secondary users to consult a database prior to any channel access so as to obtain the local prohibited channels/spatial zones (or equivalently the free channels or White Spaces). Construction of such protection regions for a transmitter within a database has been done, mainly based on empirical propagation models that estimate the received signal strength at a location. Clearly, such model-based prediction is always of limited accuracy and should be supplemented by measurement based approaches that help validate and improve the database predictions. In this work, we present results based on applying spatial interpolation techniques (Kriging) to measurement data obtained in Seattle, WA. Our results have shown that empirical DBA models tend to over-estimate received signal strengths by not explicitly accounting for local obstructions, while measurement-based Kriging achieves consistently good performance. Furthermore, boundary estimation via Kriging achieves a type I error rate 46.1% lower than comparable DBA approach while keeping type II error rate under a low limit (5%) for a given service threshold (i.e., -84 dBm/6 MHz); this is also an improvement over a method using k-Nearest Neighbor for such estimation.
Consideration set generation in commerce search

In commerce search, the set of products returned by a search engine often forms the basis for all user interactions leading up to a potential transaction on the web. Such a set of products is known as the consideration set. In this study, we consider the problem of generating consideration set of products in commerce search so as to maximize user satisfaction. One of the key features of commerce search that we exploit in our study is the association of a set of important attributes with the products and a set of specified attributes with the user queries. Those important attributes not used in the query are treated as unspecified. The attribute space admits a natural definition of user satisfaction via user preferences on the attributes and their values, viz. require that the surfaced products be close to the specified attribute values in the query, and diverse with respect to the unspecified attributes. We model this as a general Max-Sum Dispersion problem wherein we are given a set of n nodes in a metric space and the objective is to select a subset of nodes with total cost at most a given budget, and maximize the sum of the pairwise distances between the selected nodes. In our setting, each node denotes a product, the cost of a node being inversely proportional to its relevance with respect to specified attributes. The distance between two nodes quantifies the diversity with respect to the unspecified attributes. The problem is NP-hard and a 2-approximation was previously known only when all the nodes have unit cost. In our setting, we do not make any assumptions on the cost. We label this problem as the General Max-Sum Dispersion problem. We give the first constant factor approximation algorithm for this problem, achieving an approximation ratio of 2. Further, we perform extensive empirical analysis on real-world data to show the effectiveness of our algorithm.
A Relationship Between Quantization and Watermarking Rates in the Presence of Gaussian Attacks

A system which embeds watermarks in n-dimensional i.i. d. Gaussian images and distributes them in compressed form is studied. The performance of the system in the presence of Gaussian attacks is considered, and the region of achievable watermarking and quantization rates is established under constraints on image distortion and watermark detectability. The performance of related schemes is also discussed.
Foraging for carotenoids: do colorful male hihi target carotenoid-rich foods in the wild?

Lay Summary Birds that color their feathers with dietary carotenoid pigments are expected to seek out these pigments when they are molting. We show that molting male hihi, who express carotenoid-based plumage, seek out naturally occurring foods that are rich in carotenoid pigments. Female hihi, who do not express carotenoid-based plumage, do not seek out carotenoid-rich foods. This lends strength to the idea that carotenoid-based plumage reveals an individual’s foraging ability.
Chapter 2 Game Analytics – The Basics

Take Away Points:  Overview of important key terms in game analytics  Introduction to game telemetry as a source of business intelligence.  In-depth description and discussion of user-derived telemetry and metrics.  Introduction to feature selection in game analytics  Introduction to the knowledge discovery process in game analytics  References to essential further reading. 1 Analytics – a new industry paradigm Developing a profitable game in today's market is a challenging endeavor. Thousands of commercial titles are published yearly, across a number of hardware platforms and distribution channels, all competing for players' time and attention, and the game industry is decidedly competitive. In order to effectively develop games, a variety of tools and techniques from e.g. business practices, project management to user testing have been developed in the game industry, or adopted and adapted from other IT sectors. One of these methods is analytics, which in recent years has decidedly impacted the game industry and game research environment. Analytics is the process of discovering and communicating patterns in data, towards solving problems in business or conversely predictions for supporting enterprise decision management, driving action and/or improving performance. The meth-odological foundations for analytics are statistics, data mining, mathematics, programming and operations research, as well as data visualization in order to communicate insights learned to the relevant stakeholders. Analytics is not just the querying and reporting of BI data, but rests on actual analysis, e.g. statistical analysis, predic-tive modeling, optimization, forecasting, etc. (Davenport and Harris, 2007). Analytics typically relies on computational modeling. There are several branches or domains of analytics, e.g. marketing analytics, risk analytics, web analytics – and game analytics. Importantly, analytics is not the same thing as data analysis. Analytics is an umbrella term, covering the entire methodology of finding and communicating patterns in data, whereas analysis is used for individual applied instances, e.g. running
Geo-spatial analytics using the dynamic ST-SNN Approach

This work has been supported by FCT, Fundacao para a Ciencia e Tecnologia, within the Project Scope UID/CEC/00319/2013 and by Novabase Business Solutions with a co-funded QREN project (24822). Also, we would like to thank the Maritime Research Institute in the Netherlands, for making the data available for analysis under the MOVE EU Cost Action IC0903 (Knowledge Discovery from Moving Objects).
Perfect simulation and non-monotone Markovian systems

Perfect simulation, or coupling from the past, is an efficient technique for sampling the steady state of monotone discrete time Markov chains. Indeed, one only needs to consider two trajectories corresponding to minimal and maximal state in the system. We show here that even for non-monotone systems one only needs to compute two trajectories: an infimum and supremum envelope. Since the sequence of states obtained by taking infimum (resp. supremum) at each time step does not correspond to a feasible trajectory of the system, the envelopes might not couple or the coupling time might be larger. We show that the envelope approach is efficient for some classes of non-monotone queuing networks, such as networks of queues with batch arrivals, queues with fork and join nodes and/or with negative customers.
PIK3CA mutations in non-small cell lung cancer (NSCLC): Genetic heterogeneity, prognostic impact and incidence of prior malignancies

Background: Somatic mutations of the PIK3CA gene have been described in non-small cell lung cancer (NSCLC), but limited data is available on their biological relevance. This study was performed to characterize PIK3CA-mutated NSCLC clinically and genetically. Patients and methods: Tumor tissue collected consecutively from 1144 NSCLC patients within a molecular screening network between March 2010 and March 2012 was analyzed for PIK3CA mutations using dideoxy-sequencing and next-generation sequencing (NGS). Clinical, pathological, and genetic characteristics of PIK3CA-mutated patients are described and compared with a control group of PIK3CA-wildtype patients. Results: Among the total cohort of 1144 patients we identified 42 (3.7%) patients with PIK3CA mutations in exon 9 and exon 20. These mutations were found with a higher frequency in sqamous cell carcinoma (8.9%) compared to adenocarcinoma (2.9%, p<0.001). The most common PIK3CA mutation was exon 9 E545K. The majority of patients (57.1%) had additional oncogenic driver aberrations. With the exception of EGFR-mutated patients, non of the genetically defined subgroups in this cohort had a significantly better median overall survival. Further, PIK3CA-mutated patients had a significantly higher incidence of malignancy prior to lung cancer (p<0.001). Conclusion: PIK3CA-mutated NSCLC represents a clinically and genetically heterogeneous subgroup in adenocarcinomas as well as in squamous cell carcinomas with a higher prevalence of these mutations in sqamous cell carcinoma. PIK3CA mutations have no negative impact on survival after surgery or systemic therapy. However, PIK3CA mutated lung cancer frequently develops in patients with prior malignancies.