Miracle-dz/UniFAR-FTUs huggingface dataset:
An Indoor Positioning Technique Based on Fuzzy Logic

In this paper, a novel fuzzy logic indoor positioning system (FLIPS) is proposed. It measures the distances using received signal strength (RSS) between an object and stations, and then a fuzzy logic inference engine determines coordinate of the object. The proposed fuzzy approach performed better than triangulation technique when the RSS is unstable. The experiments demonstrate the performance of proposed approach within Zigbee sensor network. Moreover, the proposed FLIPS can be easily implemented hardware due to the scheme is low complexity. Index Terms—indoor positioning, wireless sensor network,
Lenalidomide-Induced Immunomodulation in Multiple Myeloma: Impact on Vaccines and Antitumor Responses

Purpose: To show that the immunomodulatory drug lenalidomide can be used in patients with relapsed multiple myeloma to augment vaccine responses. Experimental Design: Early phase clinical trial of patients with multiple myeloma who received at least one prior therapy. Patients were treated with single-agent lenalidomide and randomized to receive two vaccinations with pneumococcal 7-valent conjugate vaccine (PCV) on different schedules. Cohort A received the first PCV vaccination prior to the initiation of lenalidomide and the second vaccination while on lenalidomide. Cohort B received both vaccinations while on lenalidomide. Results: PCV-specific humoral and cellular responses were greater in cohort B than A and were more pronounced in the bone marrow than the blood, suggesting that maximal vaccine efficacy was achieved when both vaccines were administered concomitantly with lenalidomide. Patients with a clinical myeloma response showed evidence of a tumor-specific immune response with increases in myeloma-specific IFN-γ+ T cells and reductions in Th-17 cells. Conclusions: This is the first clinical evidence showing that lenalidomide augments vaccine responses and endogenous antitumor immunity in patients and as such may serve as an adjuvant for cancer and possibly infectious vaccines. Clin Cancer Res; 18(5); 1426–34. ©2012 AACR.
Natural language response generation in mixed-initiative dialogs using task goals and dialog acts

This paper presents our approach towards natural language response generation for mixed-initiative dialogs in the CUHK Restaurants domain. Our experimental corpus consists of about 4000 customer requests and waiter responses. Every request/response utterance is annotated with its task goal (TG) and dialog act (DA). The variable pair {TG, DA} is used to represent the dialog state. Our approach involves a set of corpus-derived dialog state transition rules in the form of {TG, DA}request ∆ {TG, DA}response. These rules encode the communication goal(s) and initiatives of the request/ response. Another set of hand-designed rules associate each response dialog state with one or more text generation templates. Upon testing, our system parses the input customer request for concept categories and from these infers the TG and DA using trained Belief Networks. Application of the dialog state transition rules and text generation templates automatically generates a (virtual) waiter response. Ten subjects were invited to interact with the system. Performance evaluation based on Grice's maxims gave a mean score of 4 on a five-point Likert scale and a task completion rate of at least 90%.
Managing and Analysing Big Audio Data for Environmental Monitoring

Environmental monitoring is becoming critical as human activity and climate change place greater pressures on biodiversity, leading to an increasing need for data to make informed decisions. Acoustic sensors can help collect data across large areas for extended periods making them attractive in environmental monitoring. However, managing and analysing large volumes of environmental acoustic data is a great challenge and is consequently hindering the effective utilization of the big dataset collected. This paper presents an overview of our current techniques for collecting, storing and analysing large volumes of acoustic data efficiently, accurately, and cost-effectively.
Efficient Online Segmentation for Sparse 3 D Laser Scans

The ability to extract individual objects in the scene is key for a large number of autonomous navigation systems such as mobile robots or autonomous cars. Such systems navigating in dynamic environments need to be aware of objects that may change or move. In most perception cues, a pre-segmentation of the current image or laser scan into individual objects is the first processing step before a further analysis is performed. In this paper, we present an effective method that first removes the ground from the scan and then segments the 3D data in a range image representation into different objects. A key focus of our work is a fast execution with several hundred Hertz. Our implementation has small computational demands so that it can run online on most mobile systems. We explicitly avoid the computation of the 3D point cloud and operate directly on a 2.5D range image, which enables a fast segmentation for each 3D scan. This approach can furthermore handle sparse 3D data well, which is important for scanners such as the new Velodyne VLP-16 scanner. We implemented our approach in C++ and ROS, thoroughly tested it using different 3D scanners, and will release the source code of our implementation. Our method can operate at frame rates that are substantially higher than those of the sensors while using only a single core of a mobile CPU and producing high quality segmentation results.
Pharmacological fractionation of tetrodotoxin‐sensitive sodium currents in rat dorsal root ganglion neurons by μ‐conotoxins

Adult rat dorsal root ganglion (DRG) neurons normally express transcripts for five isoforms of the α‐subunit of voltage‐gated sodium channels: NaV1.1, 1.6, 1.7, 1.8 and 1.9. Tetrodotoxin (TTX) readily blocks all but NaV1.8 and 1.9, and pharmacological agents that discriminate among the TTX‐sensitive NaV1‐isoforms are scarce. Recently, we used the activity profile of a panel of μ‐conotoxins in blocking cloned rodent NaV1‐isoforms expressed in Xenopus laevis oocytes to conclude that action potentials of A‐ and C‐fibres in rat sciatic nerve were, respectively, mediated primarily by NaV1.6 and NaV1.7.
Lossless Compression Based on the Sequence Memoizer

In this work we describe a sequence compression method based on combining a Bayesian nonparametric sequence model with entropy encoding. The model, a hierarchy of Pitman-Yor processes of unbounded depth previously proposed by Wood et al. [16] in the context of language modelling, allows modelling of long-range dependencies by allowing conditioning contexts of unbounded length. We show that incremental approximate inference can be performed in this model, thereby allowing it to be used in a text compression setting. The resulting compressor reliably outperforms several PPM variants on many types of data, but is particularly effective in compressing data that exhibits power law properties.
The Edge Weight Computation with MapReduce for Extracting Weighted Graphs

Automated weighted graph construction from massive data is essential to weighted graph theory based data mining processes, where the edge weight computation is time consuming or even fails to complete on a single machine when necessary resources are exhausted. In addition, existing work lacks of the measurement on the accuracy of the edge weights, which represents the graph accuracy and affects the following data mining results. This paper describes the classification, implementation and evaluation of edge weight computation algorithms with MapReduce Framework, which is a powerful parallel and distributed processing model. First, a classification of the edge weight computation algorithms is developed and how they can be applied on MapReduce is also discussed. Then we propose comprehensive measurements on the edge weight accuracy in terms of the number of edges, strength distribution, community structure, Hop-plot and effective diameters. Finally, a performance study has been conducted to evaluate these algorithms in terms of memory and disk usage, execution time and accuracy using a real massive social network application dataset. The results are presented and discussed. Our comparison results can help find out the most effective parallel and distributed edge weight computation algorithm for constructing a weighted graph for a given massive dataset.
Global revision in summarisation : generating novel sentences with Prim's algorithm

In abstract-like summarisation, extracted sentences containing key content are often revised to improve the coherence of the overall summary. In this work, we consider the task of Global Revision, in which a key sentence is revised and supplemented with additional content from the original document. Specifically, this task comprises two subtasks: selecting content; and grammatically ordering content, the focus of this paper. Using statistical dependency models, we search for a Maximal Spanning (Dependency) Tree that structures recycled words and phrases to form a novel sentence. Combining a modified version of Prim’s algorithm with a four-gram language model, we evaluated our system on a sentence regeneration task obtaining Bleu scores of . 30, a statistically significant improvement above the baseline.
Proportional QoS over WDM networks: blocking probability

The provision of scalable quality-of-service (QoS) guarantees on wavelength-division-multiplexing (WDM) networks is an important and challenging issue for the next generation Internet. One of the important performance metrics in a QoS-capable WDM network is the call blocking probability. Previously, a proportional differentiation model has been proposed as an effective method for scalable differentiated services provision. This model provides the network operators the ability of quantitatively adjusting the quality differentiation between service classes. We introduce this model into WDM networks with the aim of providing proportionally differentiated blocking probability to various traffic classes. An intentional blocking algorithm is proposed to implement this model at the wavelength level. In order to solve the link utilization degradation in this algorithm, we propose another intentional termination algorithm. Since the performance requirement from the network operator might be various, a hybrid algorithm is also given as a balance between the above two. These three algorithms are also suitable to TDM over WDM, where one connection only take part of the transmission capacity of one wavelength. Extensive simulation results demonstrate that our algorithms provide accurate and controllable differentiation on blocking probability between various traffic classes even in a bursty traffic situation. The infeasibility problem in proportional blocking probability provision is also discussed.
Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP

Common spatial patterns (CSP) is a commonly used technique for classifying imagined movement type brain-computer interface (BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback of CSP is that the signal processing pipeline contains two supervised learning stages: the first in which class-relevant spatial filters are learned and a second in which a classifier is used to classify the filtered variances. This may lead to potential overfitting issues, which are generally avoided by limiting CSP to only a few filters. This work argues for an alternative approach where only a single supervised learning stage is needed. The key step in this approach is to use whitened spatial covariance matrices as features and to use a linear classifier to simultaneously learn the spatial filters and the classifier weights. Unfortunately, this approach can also lead to overfitting problems. We show how these problems can be addressed by appropriately regularizing the whitening computation. Ridge regularized covariance classification outperforms whitened spatial covariance, CSP, and two regularized CSP classification methods on an imagined movement dataset indicating the usefulness of this regularization method for BCI. Trace norm regularization can help with the interpretability of the results. Index Terms-Brain-computer interface (BCI), classification,
A Semi-Blind Channel Estimation Algorithm for Massive MIMO Systems

In this letter, we propose a semi-blind iterative space-alternating generalized expectation maximization (SAGE)-based channel estimator for massive MIMO systems. The method updates the pilot-based minimum mean square error (MMSE) estimate iteratively with the help of the SAGE algorithm. Pilot-based estimators require additional pilot symbols for enhancing accuracy, whereas the proposed estimator uses data symbols. Several of the existing estimators assume complete information on large scale fading coefficients of the interfering cells which requires heavy overhead. However, the proposed estimator solves this problem by an estimate obtained from the received samples. Further, we show that the proposed estimator converges almost in one iteration, and achieves appreciable improvement over the existing pilot-based and data-aided estimators in a pilot contaminated scenario.
Token Coherence: decoupling performance and correctness

Many future shared memory multiprocessor servers will both target commercial workloads and use highly integrated "glueless" designs. Implementing low latency cache coherence in these systems is difficult, because traditional approaches either add indirection for common cache to cache misses (directory protocols) or require a totally ordered interconnect (traditional snooping protocols). Unfortunately, totally ordered interconnects are difficult to implement in glueless designs. An ideal coherence protocol would avoid indirections and interconnect ordering; however, such an approach introduces numerous protocol races that are difficult to resolve. We propose a new coherence framework to enable such protocols by separating performance from correctness. A performance protocol can optimize for the common case (i.e., absence of races) and rely on the underlying correctness substrate to resolve races, provide safety, and prevent starvation. We call the combination Token Coherence, since it explicitly exchanges and counts tokens to control coherence permissions. We develop TokenB, a specific Token Coherence performance protocol that allows a glueless multiprocessor to both exploit a low latency unordered interconnect (like directory protocols) and avoid indirection (like snooping protocols). Simulations using commercial workloads show that our new protocol can significantly outperform traditional snooping and directory protocols.
MARSA

This article introduces a dynamic cloud-based marketplace of near-realtime human sensing data (MARSA) for different stakeholders to sell and buy near-realtime data. MARSA is designed for environments where information technology (IT) infrastructures are not well developed but the need to gather and sell near-realtime data is great. To this end, we present techniques for selecting data types and managing data contracts based on different cost models, quality of data, and data rights. We design our MARSA platform by leveraging different data transferring solutions to enable an open and scalable communication mechanism between sellers (data providers) and buyers (data consumers). To evaluate MARSA, we carry out several experiments with the near-realtime transportation data provided by people in Ho Chi Minh City, Vietnam, and simulated scenarios in multicloud environments.
Structure, Melting and Transport Properties of Binary Liquid Pd-Si Metal Alloys: Molecular Dynamics Simulations

Quantum Sutton-Chen (Q-SC) potentials for molecular dynamic (MD) simulation were derived for the Pd-Si system, which were then used to obtain an atomistic description of melting and transport properties for palladium metal, metallic silicon and their alloys. Melting and structural properties were investigated by analysing the radial distribution function, enthalpy, density, and diffusion coefficient as a function of temperature. The agreement between the MD/Q-SC results and experimental values for the estimated melting points and structural properties was excellent for both pure elements: Pd and metallic Si, while melting of alloys was shown to be complicated by chemical association between the interacting constituents, which led to difficulty in the dissociation of long-range order and thus significant overshoot in calculated melting points owing to high heating rate for achievable MD execution.
Short-Term Congestion Forecasting in Wholesale Power Markets

Short-term congestion forecasting is highly important for market participants in wholesale power markets that use locational marginal prices (LMPs) to manage congestion. Accurate congestion forecasting facilitates market traders in bidding and trading activities and assists market operators in system planning. This study proposes a new short-term forecasting algorithm for congestion, LMPs, and other power system variables based on the concept of system patterns - combinations of status flags for generating units and transmission lines. The advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce forecast error. The advantage relative to previously proposed structural forecasting methods is that data requirements are substantially reduced. Forecasting results based on a NYISO case study demonstrate the feasibility and accuracy of the proposed algorithm.
Assigning a Single Figure of Merit (SFOM) to a specified path in a LTE network

System level simulators of mobile systems without realistic modelling of node mobility may not correlate well with network operations in a real world deployment. In this work, we present a new method of assigning a Single Figure of Merit (SFOM) to a particular path to be traversed by mobile user equipments in a LTE network. This single figure of merit is an indicator of the user's perceived quality of the network during the course of a journey along that particular path. Our model can be used with an open-source MATLAB based LTE System Level Simulator. Results show that the proposed method of calculating a Single Figure of Merit (SFOM) is a reliable predictor of a mobile user's satisfaction.
Implementation of a real-time 3-D positioning sound synthesis algorithm for a handheld device

This paper describes an implementation of a positioning sound synthesis algorithm on an ARM 926 EJ-S based mobile device. To achieve a minimal complexity, sound sources are synthesized with a corresponding pair of approximated minimum phase filter and inter-aural time delay (ITD). The resulting complexity is measured as 13.48 million-instruction per second (MIPS). The quality of positioned sources is verified with the MOS test. Eventually, the system proposed has ample capability to reproduce a positioning sound source along with three-dimensional graphic displays, i.e. games, user-interface
Nitroxide side-chain dynamics in a spin-labeled helix-forming peptide revealed by high-frequency (139.5-GHz) EPR spectroscopy.

High-frequency electron paramagnetic resonance (EPR) spectroscopy has been performed on a nitroxide spin-labeled peptide in fluid aqueous solution. The peptide, which follows the single letter sequence, was reacted with the methanethiosulfonate spin label at the cysteine sulfur. The spin sensitivity of high-frequency EPR is excellent with less than 20 pmol of sample required to obtain spectra with good signal-to-noise ratios. Simulation of the temperature-dependent spectral lineshapes reveals the existence of local anisotropic motion about the nitroxide N-O bond with a motional anisotropy tau( perpendicular)/tau( parallel) ( identical with N) approaching 2.6 at 306 K. Comparison with previous work on rigidly labeled peptides suggests that the spin label is reorienting about its side-chain tether. This study demonstrates the feasibility of performing 140-GHz EPR on biological samples in fluid aqueous solution.
Duration of Thyroid Dysfunction Correlates with All-Cause Mortality. The OPENTHYRO Register Cohort

Introduction and Aim The association between thyroid dysfunction and mortality is controversial. Moreover, the impact of duration of thyroid dysfunction is unclarified. Our aim was to investigate the correlation between biochemically assessed thyroid function as well as dysfunction duration and mortality. Methods Register-based follow-up study of 239,768 individuals with a serum TSH measurement from hospitals and/or general practice in Funen, Denmark. Measurements were performed at a single laboratory from January 1st 1995 to January 1st 2011. Cox regression was used for mortality analyses and Charlson Comorbidity Index (CCI) was used as comorbidity score. Results Hazard ratios (HR) with 95% confidence intervals (CI) for mortality with decreased (<0.3 mIU/L) or elevated (>4.0 mIU/L) levels of TSH were 2.22; 2.14–2.30; P<0.0001 and 1.28; 1.22–1.35; P<0.0001, respectively. Adjusting for age, gender, CCI and diagnostic setting attenuated the risk estimates (HR 1.23; 95% CI: 1.19–1.28; P<0.0001, mean follow-up time 7.7 years, and HR 1.07; 95% CI: 1.02–1.13; P = 0.004, mean follow-up time 7.2 years) for decreased and elevated values of TSH, respectively. Mortality risk increased by a factor 1.09; 95% CI: 1.08–1.10; P<0.0001 or by a factor 1.03; 95% CI: 1.02–1.04; P<0.0001 for each six months a patient suffered from decreased or elevated TSH, respectively. Subdividing according to degree of thyroid dysfunction, overt hyperthyroidism (HRovert 1.12; 95% CI: 1.06–1.19; P<0.0001), subclinical hyperthyroidism (HRsubclinical 1.09; 95% CI: 1.02–1.17; P = 0.02) and overt hypothyroidism (HRovert 1.57; 95% CI: 1.34–1.83; P<0.0001), but not subclinical hypothyroidism (HRsubclinical 1.03; 95% CI: 0.97–1.09; P = 0.4) were associated with increased mortality. Conclusions and Relevance In a large-scale, population-based cohort with long-term follow-up (median 7.4 years), overt and subclinical hyperthyroidism and overt but not subclinical hypothyroidism were associated with increased mortality. Excess mortality with increasing duration of decreased or elevated serum TSH suggests the importance of timely intervention in individuals with thyroid dysfunction.
Mobile Minirobot with Hybrid Locomotion

In this paper the authors present a mobile minirobot with a hybrid locomotion system. The proposed locomotion system combines the advantages of legged locomotion with the advantages of wheeled locomotion. Due to the proposed structure, the minirobot allows three ways of locomotion: legged, wheeled and combined. The paper describes the experimental prototype, the electronic components needed for controlling the minirobot and it analyses locomotion possibilities by using wheels and legs. The end of the paper presents the kinematic model for a single leg of the minirobot.
Selfish content replication on graphs

Replication games are a model of the problem of content placement in computer and communication systems, when the participating nodes make their decisions such as to maximize their individual utilities. In this paper we consider replication games played over arbitrary social graphs; the social graph models limited interaction between the players due to, e.g., the network topology. We show that in replication games there is an equilibrium object placement for arbitrary social graphs. Nevertheless, if all nodes follow a myopic strategy to update their object placements then they might cycle arbitrarily long before reaching an equilibrium if the social graph is non-complete. We give sufficient conditions under which such cycles do not exist, and propose an efficient distributed algorithm to reach an equilibrium over a non-complete social graph.