Miracle-dz/UniFAR-FTUs huggingface dataset:
Time Interference Alignment via Delay Offset for Long Delay Networks

Time Interference Alignment is a flavor of Interference Alignment that increases the network capacity by suitably staggering the transmission delays of the senders. In this work the analysis of the existing literature is generalized and the focus is on the computation of the dof for networks with randomly placed users in a n-dimensional Euclidean space. In the basic case without coordination among the transmitters analytical expressions of the sum dof can be derived. If the transmit delays are coordinated, in 20% of the cases time Interference Alignment yields additional dof with respect to orthogonal access schemes. The potential capacity improvements for satellite networks are also investigated.
Knowledge-enabled decision making for robotic system utilizing ambient service components

Robotic and ambient intelligent environments are much more demanding regarding the knowledge a robot needs to have. A skilled robot performs human-scale manipulation tasks, interacts with a variety of objects, understands instructions given by humans and most importantly, requires the capability of interpreting ubiquitous resources and assembling them into a complex plan. In this paper, a novel Knowledge-enable Decision Making Framework KDMF is proposed for Component-Based Robotic System CBRS. We exploit that the use of domain knowledge is pivotal to endow robots with higher degrees of autonomy and intelligence in CBRS. Ontology knowledge about classes, properties, relations is organized in OWL based conceptual map, which allows automated inference to derive new pieces of information. Knowledge about tasks is specified in a tree data structure, and knowledge about components' functions is formulated by a specific type of service specification profile. Using the knowledge representation, a task-oriented decision making method is proposed that integrates knowledge inference and service components utilization. In practical applications of ambient and robotic assisted living, robot's plans generated by the decision making software are based on the knowledge of components, rather than particular device instances, which improves the reusability and flexibility of the system. Experimental results validate the effectiveness of the proposed method.
Interplay of Insulin-Like Growth Factor-II, Insulin-Like Growth Factor-I, Insulin-Like Growth Factor-I Receptor, COX-2, and Matrix Metalloproteinase-7, Play Key Roles in the Early Stage of Colorectal Carcinogenesis

Purpose: The aim of this study was to characterize the relationship of insulin-like growth factor (IGF)-II expression with IGF-I, IGF-I receptor (IGF-IR), cyclooxygenase-2 (COX-2), and matrix metalloproteinase (MMP)-7 in early colorectal carcinogenesis. Experimental Design: With the semiquantitative reverse transcriptase-PCR, 90 human colorectal tumor tissues (63 adenomas and 27 submucosal pT1 cancers) were analyzed for IGF-II, IGF-IR, IGF-I, COX-2, and MMP-7 expression. Ninety-nine adenoma tissues and 60 pT1 cancer tissues were also analyzed immunohistochemically for IGF-II expression. Loss of imprinting of the IGF-II gene was analyzed. Paired carcinoma and adenoma tissues obtained from a carcinoma in adenoma lesion was analyzed by a cDNA array. Results: IGF-II mRNA expression was detected in 37.8% of the 90 colorectal tumor tissues. The frequency of IGF-II mRNA expression was significantly higher in pT1 cancer (70.4%) than in adenoma (23.8%). Immunohistochemical IGF-II expression was also more frequently detected in pT1 cancer (58.3%) than in adenoma (25.3%). Loss of imprinting of the IGF-II gene was observed in 15 (44.1%) of the 34 colorectal tumors in which IGF-II was overexpressed. IGF-II expression was positively correlated with the expression of IGF-IR and IGF-I. COX-2 and MMP-7 mRNA expression was detected in 42.2% and 77.8% of the tumor tissues, respectively, and both were positively correlated with IGF-I, IGF-II, and IGF-IR expression. IGF-II was the most differentially expressed gene between carcinoma and adenoma lesions. Conclusions: IGF-II, in conjunction with IGF-IR, IGF-I, COX-2, and MMP-7, seems to play a key role in the early stage of colorectal carcinogenesis.
Flat combining and the synchronization-parallelism tradeoff

Traditional data structure designs, whether lock-based or lock-free, provide parallelism via fine grained synchronization among threads. We introduce a new synchronization paradigm based on coarse locking, which we call flat combining. The cost of synchronization in flat combining is so low, that having a single thread holding a lock perform the combined access requests of all others, delivers, up to a certain non-negligible concurrency level, better performance than the most effective parallel finely synchronized implementations. We use flat-combining to devise, among other structures, new linearizable stack, queue, and priority queue algorithms that greatly outperform all prior algorithms.
Lineage tracing of human B cells reveals the in vivo landscape of human antibody class switching

Antibody class switching is a feature of the adaptive immune system which enables diversification of the effector properties of antibodies. Even though class switching is essential for mounting a protective response to pathogens, the in vivo patterns and lineage characteristics of antibody class switching have remained uncharacterized in living humans. Here we comprehensively measured the landscape of antibody class switching in human adult twins using antibody repertoire sequencing. The map identifies how antibodies of every class are created and delineates a two-tiered hierarchy of class switch pathways. Using somatic hypermutations as a molecular clock, we discovered that closely related B cells often switch to the same class, but lose coherence as somatic mutations accumulate. Such correlations between closely related cells exist when purified B cells class switch in vitro, suggesting that class switch recombination is directed toward specific isotypes by a cell-autonomous imprinted state. DOI: http://dx.doi. org/10.7554/eLife.16578. 001
Effects of Prolonged Waking-Auditory Stimulation on Electroencephalogram Synchronization and Cortical Coherence during Subsequent Slow-Wave Sleep

Evidence suggests that sleep homeostasis is not only dependent on duration of previous wakefulness but also on experience- and/or use-dependent processes. Such homeostatic mechanisms are reflected by selective increases in the duration of a sleep stage, modifications to electrophysiological–metabolic brain patterns in specific sleep states, and/or reactivation to neuronal ensembles in subsequent sleep periods. Use-dependent sleep changes, apparently different from those changes caused by memory consolidation processes, are thought to reflect neuronal restoration processes after the sustained exposure to stimulation during the preceding wakefulness. In the present study, we investigated changes in the brain electrical activity pattern during human sleep after 6 hr of continuous auditory stimulation during previous wakefulness. Poststimulation nights showed a widespread increase of spectral power within the α (8–12 Hz) and sleep spindle (12–15 Hz) frequency range during slow-wave sleep (SWS) compared with the baseline night. This effect was mainly attributable to an enhanced EEG amplitude rather than an increase of oscillations, except for temporal (within α and sleep spindles) and parietal regions (within sleep spindles) in which both parameters contributed equally to the increase of spectral energy. Power increments were accompanied by a strengthening of the coherence between fronto-temporal cortical regions within a broad frequency range during SWS but to the detriment of the coherence between temporal and parieto-occipital areas, suggesting underlying compensatory mechanisms between temporal and other cortical regions. In both cases, coherence was built up progressively across the night, although no changes were observed within each SWS period. No electrophysiological changes were found in rapid eye movement sleep. These results point to SWS as a critical brain period for correcting the cortical synaptic imbalance produced by the predominant use of specific neuronal populations during the preceding wakefulness, as well as for synaptic reorganization after prolonged exposure to a novel sensory experience.
Creating a Systemic Functional Grammar Corpus from the Penn Treebank

The lack of a large annotated systemic functional grammar (SFG) corpus has posed a significant challenge for the development of the theory. Automating SFG annotation is challenging because the theory uses a minimal constituency model, allocating as much of the work as possible to a set of hierarchically organised features. In this paper we show that despite the unorthodox organisation of SFG, adapting existing resources remains the most practical way to create an annotated corpus. We present and analyse SFGBank, an automated conversion of the Penn Treebank into systemic functional grammar. The corpus is comparable to those available for other linguistic theories, offering many opportunities for new research.
A Hierarchical Human-Robot Interaction-Planning Framework for Task Allocation in Collaborative Industrial Assembly Processes

In this letter, we propose a framework for task allocation in human-robot collaborative assembly planning. Our framework distinguishes between two main layers of abstraction and allocation. In the higher layer, we use an abstract world model, incorporating a multiagent human-robot team approach in order to describe the collaborative assembly planning problem. From this, nominal co-ordinated skill sequences for every agent are produced. In order to be able to treat humans and robots as agents of the same form, we move relevant differences/peculiarities into distinct cost functions. The layer beneath handles the concrete skill execution. On atomic level, skills are composed of complex hierarchical and concurrent hybrid state machines, which in turn co-ordinate the real-time behavior of the robot. Their careful design allows to cope with unpredictable events also on decisional level without having to explicitly plan for them, instead one may rely also on manually designed skills. Such events are likely to happen in dynamic and potentially partially known environments, which is especially true in case of human presence.
User Relation Prediction Based on Matrix Factorization and Hybrid Particle Swarm Optimization

Many real-world domains are relational in nature, consisting of a set of objects related to each other in complex ways. Matrix factorization is an effective method in relationship prediction, However, traditional matrix factorization link prediction methods can only be used for non-negative matrix. In this paper, a generalized framework, itelliPrediction, is presented that is able to deal with positive and negative matrix. The novel itelliPrediction framework is domain independent and with high precision. We validate our approach using two different data sources, an open data sets and a real-word dataset, the result demonstrated that the quality of our approach is comparable to, if not better than, exiting state of the art relation predication framework.
Adaptive Kanerva-based function approximation for multi-agent systems

In this paper, we show how adaptive prototype optimization can be used to improve the performance of function approximation based on Kanerva Coding when solving largescale instances of classic multi-agent problems. We apply our techniques to the predator-prey pursuit problem. We first demonstrate that Kanerva Coding applied within a reinforcement learner does not give good results. We then describe our new adaptive Kanerva-based function approximation algorithm, based on prototype deletion and generation. We show that probabilistic prototype deletion with random prototype generation increases the fraction of test instances that are solved from 45% to 90%, and that prototype splitting increases that fraction to 94%. We also show that optimizing prototypes reduces the number of prototypes, and therefore the number of features, needed to achieve a 90% solution rate by up to 87%. These results demonstrate that our approach can dramatically improve the quality of the results obtained and reduce the number of prototypes required. We conclude that adaptive prototype optimization can greatly improve a Kanerva-based reinforcement learner's ability to solve large-scale multi-agent problems.
Double-Linear Approximation Algorithm to Achieve Maximum-Power-Point Tracking for Photovoltaic Arrays

In this paper, a double-linear approximation algorithm (DLAA) to achieve maximum-power-point tracking (MPPT) for photovoltaic (PV) arrays is proposed. The DLAA is based on the approximation that the maximum power point varies linearly with irradiation and temperature. With the DLAA, a maximum power point can be determined instantaneously. Moreover, complicated calculations and perturbations about an optimal point can be avoided. The paper also proposes a corresponding circuit to realize the DLAA. The configuration of the DLAA circuit is simple such that it is cost-effective and can be embedded into PV arrays easily. An example of implementation of a PV power supply system with the proposed MPPT is designed and the DLAA is compared with the perturb-and-observe method. Simulated and experimental results have demonstrated the feasibility of the PV power system and verified the advantages of the proposed DLAA.
Chlorophyll as Photosensitizer in Dye-Sensitized Solar Cells

Chlorophyll, being the most abundant pigment that commonly found in plants, bacteria, bryophytes and algae, plays a vital role in photosynthesis. Chlorophylls are natural pigments and therefore safe, environmental friendly, easily available and cheap. Chlorophyll has been experimented to function as a photosensitizer in dye-sensitized solar cells (DSSCs) as DSSCs mimic the photosynthesis process in green plants. DSSC was first developed by Gratzel in 1991 and since then has gained tremendous attention as its fabrication is cheap and easy. A DSSC basically comprises a semiconductor that has been soaked in sensitizing dye (chlorophyll), a counter electrode, and an electrolyte containing a redox mediator. The dye absorbs light, which is transformed into electricity. Chlorophyll can be extracted from the leaves of pomegranate, bougainvillea, papaya, Pandanus amaryllifolius, spinach, green grasses, seaweeds, algae and bryophytes. Chlorophyll from these sources has been studied as possible photosensitizers for DSSCs. Most researches done in chlorophyll DSSC use the extracted natural pigments. The type of solvent and pH of the dye solution will also affect the stability of chlorophyll and subsequently the performance of the DSSCs. This chapter will present an inexhaustive overview on DSSCs using chlorophyll as dye.
Generalized criteria for blind multivariate signal equalization

We consider the problem of blind multivariate signal equalization. Assuming that the input signals are i.i. d. and statistically mutually independent, we propose both a generalization of some available equalization criteria and a generalization of some source separation criteria to the convolutive case. Hence, we obtain a new generalized class of objective function for blind equalization.
More than Words: Syntactic Packaging and Implicit Sentiment

Work on sentiment analysis often focuses on the words and phrases that people use in overtly opinionated text. In this paper, we introduce a new approach to the problem that focuses not on lexical indicators, but on the syntactic "packaging" of ideas, which is well suited to investigating the identification of implicit sentiment, or perspective. We establish a strong predictive connection between linguistically well motivated features and implicit sentiment, and then show how computational approximations of these features can be used to improve on existing state-of-the-art sentiment classification results.
Knee and ankle kinematics in different walking conditions

To investigate the relationship between knee and ankle as a reference to the design of intelligent low limb prosthesis, this paper made experiments to describe the knee and ankle kinematics of normal gait on level ground, stairs and ramps of different slopes. 8 subjects participated in this study and gaits were measured on the level ground, stairs and ramps of different slopes using a motion capture system. Changes of knee and ankle angles with respect to walking conditions are described. Knee flexion and ankle dorsiflexion at heel strike increased with the angle of ramps walking up. While walking down ramps, Knee flexion increased at toe off with the angle. Walking up/down stairs share the similar characteristics with walking up/down ramps, respectively. Coupling relationship of knee and ankle differed significantly between subjects and conditions, but there are linear relationships between knee angle and ankle angle. Taking the gait characteristics and relationship of knee and ankle analyzed in this paper as a reference, we can divide the gait into several parts and use a simple and linear way to approximate human gait, which can help us design and control intelligent low limb prosthesis that function well in all conditions in the future.
Implementation of 64-Point Reconfigurable FFT Processor for ASIP Architecture

In this paper a novel architecture of ASIP for a reconfigurable FFT is proposed. The proposed design implements a reconfigurable 64 points FFT processor for unsigned real numbers. By changing the value of integer constant we can design 2, 4, 8, 16, 32 and 64 point FFT which incorporates a high speed ASIP. In OFDMA system there is a need of alterable point FFT processor. Hence, the design meets the requirement of OFDMA system. DIFFFT algorithm is implemented using VHDL language and Xilinx 9.1i is used for simulation results. The clock frequency is limited to 272.769 MHz for design stability and real time processing.
Experiments with Lazy Evaluation of Classification Decision Trees Made with Genetic Programming

In this paper, we present a lazy evaluation approach of classification decision trees with genetic programming. We describe and experiment with the lazy evaluation that does not evaluate the whole population but evaluates only the individuals that are chosen to participate in the tournament selection method. Further on, we used dynamic weights for the classification instances, that are linked to the chance of that instance getting picked for the evaluation process. These weights change based on the misclassification rate of the instance. We test our lazy evaluation approach on 10 standard classification benchmark datasets and show that not only lazy evaluation approach uses less time to evolve the good solution, but can even produce better solution due to changing instance weights and thus preventing the overfitting of the solutions.
Robust and Accurate Object Tracking Under Various Types of Occlusions

We propose a complete solution to robust and accurate object tracking in face of various types of occlusions which pose many challenges to correct judgment of occlusion situation and proper update of target template. In order to tackle those challenges, we first propose a content-adaptive progressive occlusion analysis (CAPOA) algorithm. By combining the information provided by spatiotemporal context, reference target, and motion constraints together, the algorithm makes a clear distinction between the target and outliers. Accurate tracking of an occluded target is achieved by rectifying the target location using the variant-mask template matching (VMTM). In order to deal with template drift in the process of template update, we propose a drift-inhibitive masked Kalman appearance filter (DIMKAF) which accurately evaluates the influence of template drift when updating the masked template. Finally, we devise a local best match authentication (LBMA) algorithm to handle complete occlusions, so that we can achieve a much more trustworthy detection of the end of an arbitrarily long complete occlusion. Experimental results show that our proposed solution tracks targets reliably and accurately no matter when they are under: short-term, long-term, partial or complete occlusions.
Serotonin 5‐HT1B receptor gene and attention deficit hyperactivity disorder in Chinese Han subjects

Serotonin is an endogenous neurotransmitter that regulates aggressive and impulsive behavior and may be involved in the development of attention deficit hyperactivity disorder (ADHD). 5‐HT1B knockout mice display hyperactivity, increased exploratory activity and aggression, reduced anxiety, increased vulnerability to cocaine self‐administration, and elevated alcohol consumption. Many of these same behaviors are seen in patients with ADHD. Prior studies reported excess transmission of the 861G allele of 5‐HT1B to ADHD offspring. We used the transmission disequilibrium test (TDT) and haplotype analysis to investigate the A‐161T and G861C polymorphisms in the 5‐HT1B receptor gene in ADHD trios from the Chinese Han population. We found no association with ADHD but did find a tendency for excess transmission of the 861G allele (χ2 = 3.766, P = 0.052) and the G/A haplotype (χ2 = 2.925, df = 1, P = 0.087), and under‐transmission of C/A haplotype (χ2 = 3.707, df = 1, P = 0.054) to offspring with inattentive ADHD. © 2004 Wiley‐Liss, Inc.
Journal of Geophysical Research : Space Physics Classification of Solar WindWith Machine Learning

We present a four-category classification algorithm for the solar wind, based on Gaussian Process. The four categories are the ones previously adopted in Xu and Borovsky (2015): ejecta, coronal hole origin plasma, streamer belt origin plasma, and sector reversal origin plasma. The algorithm is trained and tested on a labeled portion of the OMNI data set. It uses seven inputs: the solar wind speed Vsw, the temperature standard deviation σT , the sunspot number R, the F10.7 index, the Alfven speed vA, the proton specific entropy Sp, and the proton temperature Tp compared to a velocity-dependent expected temperature. The output of the Gaussian Process classifier is a four-element vector containing the probabilities that an event (one reading from the hourly averaged OMNI database) belongs to each category. The probabilistic nature of the prediction allows for a more informative and flexible interpretation of the results, for instance, being able to classify events as “undecided. ” The new method has a median accuracy larger than 90% for all categories, even using a small set of data for training. The Receiver Operating Characteristic curve and the reliability diagram also demonstrate the excellent quality of this new method. Finally, we use the algorithm to classify a large portion of the OMNI data set, and we present for the first time transition probabilities between different solar wind categories. Such probabilities represent the “climatological” statistics that determine the solar wind baseline.