Miracle-dz/UniFAR-FTUs huggingface dataset:
A 3-D Self-Calibration Method for Multiple Base Stations in Large Complex Indoor Environment

Base stations deployed for localization are required to be location aware. Traditional techniques for calibrating base stations need to be done manually, which is time-consuming and inconvenient. To address this problem, Semidefinite Programming (SDP) -based approaches have been developed, which can achieve high precision. However, existing approaches mostly focus on the 2-dimensional (2-D) scenarios where base stations must be restricted to be of the same height. In this paper, we propose a 3-dimensional (3-D) SDP-based method to determine the positions of base stations given inaccurate even incomplete distance information and apply it to large indoor environments. The corresponding Cramer-Rao lower bounds (CRLB) are derived to ensure localization quality. We investigate the performance of this method on problems with sensing range. Simulation results verify that our method can achieve superior performance in terms of accuracy and robustness compared with traditional noncooperative localization techniques.
SEMANTIC RULE LABELING DAN SENTENCE INFORMATION DENSITY

Coverage and salient is the main problem to the attention of researchers in peringkasan document. Clustering approach capable of providing good coverage of all the topics but does not have information that can represent other sentences (salience sentence) . Salience can be explored by looking at the relationship from one sentence to another sentence that was built with the approach of graph text position, but position text graph only be able to explore the relationship between a sentence without considering the role of semantic word ("who" did "what" to "whom", "where", "when" and "how") in the sentence being compared. In this paper we propose a new method of election strategy representative sentence sentence cluster approach information density and Semantic Rule labeling. The trial results showed that the method proposed method is able to choose better-sentence summary of the method Sentence Information Density (SID) with an average value of Rouge-1 0.32511.
On Sensitivity of Bias Operation Point In Transistors with Moderate Inversion

The paper analyses sensitivity, with respect to the threshold voltage, of bias operation point in the analog circuits using moderate inversion. It is shown that this regime is highly sensitive with respect to the threshold voltage variation. Such high sensitivity imposes unrealistic requirements on the threshold voltage tolerances, so that it is practically impossible to design an amplifier with moderate inversion in the stages without adaptable or tunable threshold voltage. It is also shown that, as a result of this mode of inversion, some important design metrics (cancelling of the third order distortion) are unrealizable.
Creating Realistic Ground Truth Data for the Evaluation of Calibration Methods for Plenoptic and Conventional Cameras

Camera calibration methods usually consist of capturing images of known calibration patterns and using the detected correspondences to optimize the parameters of the assumed camera model. A meaningful evaluation of these methods relies on the availability of realistic synthetic data. In previous works concerned with conventional cameras the synthetic data was mainly created by rendering perfect images with a pinhole camera and subsequently adding distortions and aberrations to the renderings and correspondences according to the assumed camera model. This method can bias the evaluation since not every camera perfectly complies with an assumed model. Furthermore, in the field of plenoptic camera calibration there is no synthetic ground truth data available at all. We address these problems by proposing a method based on backward ray tracing to create realistic ground truth data that can be used for an unbiased evaluation of calibration methods for both types of cameras.
A Femto/Pico-Watt Feedforward Leakage Self-Suppression Logic Family in 180 nm to 28 nm Technologies

We present a novel logic family, titled feedforward leakage self-suppression logic (FLSL), for nanowatt and sub-nanowatt always-on circuits. It addresses the prohibitively long delay of existing ultra-low leakage logic families without requiring sleep or mode control signals to exercise the leakage-suppression mode. Implemented in 180-nm CMOS, the proposed logic family achieves femto-watt per-gate leakage and a fan-out-of-4 (FO4) delay of 10.2 µs, a remarkable speed enhancement of 150X over the prior art in the same process in the same leakage level. We also investigate the impact of technology scaling by designing the FLSL logic family additionally in 65-nm and 28-nm processes. In a 28-nm process, the FLSL can achieve about 70 ns FO4 delay and 10 picowatts per-gate leakage. Finally, we prototype a finite impulse response filter core for physical sensing systems in a 180 nm. The filter can achieve the power consumption of 109 picowatts for sparse, i.e., predominantly constant or slowly changing, input signals at 1 kHz clock frequency.
One for All and All for One: Scalable Consensus in a Hybrid Communication Model

This paper addresses consensus in an asynchronous model where the processes are partitioned into clusters. Inside each cluster, processes can communicate through a shared memory, which favors efficiency. Moreover, any pair of processes can also communicate through a message-passing communication system, which favors scalability. In such a "hybrid communication" context, the paper presents two simple binary consensus algorithms (one based on local coins, the other one based on a common coin). These algorithms are straightforward extensions of existing message-passing randomized round-based consensus algorithms. At each round, the processes of each cluster first agree on the same value (using an underlying shared memory consensus algorithm), and then use a message-passing algorithm to converge on the same decided value. The algorithms are such that, if all except one processes of a cluster crash, the surviving process acts as if all the processes of its cluster were alive (hence the motto "one for all and all for one"). As a consequence, the hybrid communication model allows us to obtain simple, efficient, and scalable fault-tolerant consensus algorithms. As an important side effect, according to the size of each cluster, consensus can be obtained even if a majority of processes crash.
Cortical representations of auditory and tactile perceptual decisions

Perceptual decision making is the process that makes a rich environment manageable by compartmentalizing stimuli into various categories. Parietal cortex is involved in many tasks that require perceptual decisions. While much work in both the human and monkey domains has investigated processes related to visual decision making in the frontal and parietal lobes in a predominantly unimodal fashion, relatively little research has explored auditory and tactile perceptual decisions. As such, we wanted to know whether these regions also play a role in auditory and tactile decision making and to what extent information therein may be represented supramodally. Using functional magnetic resonance imaging and a paradigm requiring human participants to categorize auditory and tactile frequency-modulated sweeps, we sought to disentangle motor confounds and minimize linguistic processing from the perceptual decisions that participants made. We ran a series of experiments that utilized whole-brain multivariate pattern analysis implemented via linear discriminant analysis classification in order to detect cortical representations of such potentially supramodal information. Ultimately, we showed that learned categories were best decoded within the right parietal lobe and the medial frontal gyrus, supramodal representations of “up- vs. down-sweeps” were localized to the left parietal-temporal-occipital junction, and, most consequentially, cross-modality decoding of category membership was strongest in the left posterior insula and precuneus. Given our choice of paradigm, such results appear to demonstrate that the information representations in the posterior insula and precuneus are independent of motor and language processing and instead reflect supramodal or modality-free mechanisms that underlie the categorization process.
F3C: Fog-enabled Joint Computation, Communication and Caching Resource Sharing for Energy-Efficient IoT Data Stream Processing

Fog/edge computing has been recently regarded as a promising approach for supporting emerging mission-critical Internet of Things (IoT) applications on capacity and battery constrained devices. By harvesting and collaborating a massive crowd of devices in close proximity for computation, communication and caching resource sharing (i.e., 3C resources), it enables great potentials in low-latency and energy-efficient IoT task execution. To efficiently exploit 3C resources of fog devices in proximity, we propose F3C, a fog-enabled 3C resource sharing framework for energy-efficient IoT data stream processing by solving an energy cost minimization problem under 3C constraints. Nevertheless, the minimization problem proves to be NP-hard via reduction to a Generalized Assignment Problem (GAP). To cope with such challenge, we propose an efficient F3C algorithm based on an iterative task team formation mechanism which regards each task's 3C resource sharing as a subproblem solved by the elaborated min cost flow transformation. Via utility improving iterations, the proposed F3C algorithm is shown to converge to a stable system point. Extensive performance evaluations demonstrate that our F3C algorithm can achieve superior performance in energy saving compared to various benchmarks.
A long noncoding RNA signature for ulcerative colitis identifies IFNG-AS 1 as an enhancer of inflammation A long noncoding RNA signature in Ulcerative Colitis

High-throughput technologies revealed new categories of genes, including the long noncoding RNAs (lncRNAs), involved in the pathogenesis of human disease; however, the role of lncRNAs in the ulcerative colitis (UC) has not been evaluated. Gene expression profiling was used to develop lncRNA signatures in UC samples. Jurkat T cells were activated by PMA/ionomycin subsequently interferon-γ (IFNG) and tumor necrosis factor (TNF)-α protein levels were assessed by ELISA. Antisense molecules were designed to block IFNG-AS1 expression. A unique set of lncRNAs was differentially expressed between UC and control samples. Of these, IFNG-AS1 was among the highest statistically significant lncRNAs (fold change: 5.27, P value: 7.07E-06). Bioinformatic analysis showed that IFNG-AS1 was associated with the IBD susceptibility loci SNP rs7134599 and its genomic location is adjacent to the inflammatory cytokine IFNG. In mouse models of colitis, active colitis samples had increased colonic expression of this lncRNA. Utilizing the Jurkat T cell model, we found IFNG-AS1 to positively regulate IFNG expression. Novel lncRNA signatures differentiate UC patients with active disease, patients in remission, and control subjects. A subset of these lncRNAs was found to be associated with the clinically validated IBD susceptibility loci. IFNG-AS1 was one of these differentially expressed lncRNAs in UC patients and found to regulate the key inflammatory cytokine, IFNG, in CD4 T cells. Taking these findings together, our study revealed novel lncRNA signatures deregulated in UC and identified IFNG-AS1 as a novel regulator of IFNG inflammatory responses, suggesting the potential importance of noncoding RNA mechanisms on regulation of inflammatory bowel disease-related inflammatory responses.
Distant supervision of relation extraction in sparse data

To extract structured knowledge from unstructured text sources we need to understand the semantic relationships between entities. State-of-the-art relation extraction techniques take advantage of the abundance of data on the web. However, in domains with sparse data such as social networks which have limited occurrences of entities and relationship patterns, bootstrapping techniques and pattern detection methods are inefﬁcient and inaccurate. In this paper, we introduce REDS, a Relation Extraction approach based on Distant Supervision. REDS extracts the named entities from text documents and assigns a ﬁngerprint to each potential relationship among the named entities. Then, it queries a knowledge repository for similar matches to each ﬁngerprint. An assessor uses the query results and the data statistics to measure the validity of the relationships corresponding to the queried ﬁngerprints, and labels each potential relationship with the predicted type. In addition to handling the relation extraction in presence of data sparsity, REDS uses an information retrieval framework that makes it scalable and capable of dealing with noisy data. We implement and test REDS on a non-English historical archive consisting of unstructured notarial acts and structured civil registers; By means of manual evaluations REDS achieves precision of 0.90.
A METHOD OF TRACKING OPTIMUM EFFICIENCY FOR FOUR-COIL WIRELESS POWER TRANSFER SYSTEM

Magnetic resonant wireless power transfer (WPT) is an emerging technology that may create new applications for wireless power charging. However, low efficiency resulting from the change of the transfer distance is a main obstructing factor for promoting this technology. In this paper, a method of fast tracking optimum efficiency is proposed. The input impedance value is obtained by measuring the input current. Then the transfer distance is estimated by the input impedance value. The optimum load resistor is obtained under a given transfer distance. In addition, the extended Lmatching network is proposed in order to automatically adjust the load resistor. The key parameters of the matching network are also given. The optimum efficiency can be fast tracked by the proposed method as the transfer distance varies. The WPT system and the extended L-matching network are designed. Simulated and experimental results validating the proposed method are given.
HiCAN: Hierarchical Convolutional Attention Network for Sequence Modeling

Convolutional neural networks (CNN) are widely used on sequential data since it can capture local context dependencies and temporal order information inside sequences. Attention (ATT) mechanisms have also attracted enormous interests due to its capability of capturing the important parts of a sequence. These two neural networks can extract different features from sequences. In order to combine the advantages of CNN and ATT, we propose a convolutional attention network (CAN), which merges the structure of CNN and ATT into a single neural network and can serve as a new basic module in complex neural networks. Based on CAN, we then build a sequence encoding model with hierarchical structure, "hierarchical convolutional attention network (HiCAN)", to tackle sequence modeling problems. It can explicitly capture both the local and global context dependencies and temporal order information in sequences. Extensive experiments conducted on session-based recommendation (Recommender Systems) demonstrate that HiCAN is able to outperform state-of-the-art methods and show higher computational efficiency. Furthermore, we conduct extended experiments on text classification (Natural Language Processing). The results show that our model can also achieve competitive performance on NLP tasks.
Osteology of a North American Goniopholidid (Eutretauranosuchus delfsi) and Palate Evolution in Neosuchia

ABSTRACT The goniopholidid Eutretauranosuchus delfsi is currently known from two skulls from the Late Jurassic Morrison Formation of Colorado. Here we present a detailed osteology for a third Eutretauranosuchus delfsi specimen (AMNH FARB 570), consisting of a skull and fragmentary postcranium from Bone Cabin Quarry, Wyoming (Morrison Formation). Additional preparation, computed tomography (CT), and three-dimensional processing of CT images reveal matrix-covered structures on AMNH FARB 570, provide new information on goniopholidid cranial anatomy, and allow for the first description of an articulated North American goniopholidid braincase. These new data, along with restudy of the palatal anatomy in Eutretauranosuchus, provide valuable insight into the phylogenetic history of goniopholidid crocodyliforms. The skull of AMNH FARB 570 is extremely similar to other specimens of E. delfsi and Amphicotylus spp. , especially in details of the architecture of the palate. In these Morrison Formation goniopholidids the secondary palate is incomplete such that the nasopharyngeal passage has no ventral floor. Additionally, there is a complex septum, constructed from the vomers and pterygoid, which extends ventrally near to the plane of the secondary palate. Characters diagnosing Eutretauranosuchus delfsi include: (1) an elongate, platyrostral skull; (2) a prominent depression on the posterolateral surface of the alveolar process of the maxilla; (3) minimal lateral undulation of the maxillary tooth row; (4) broad- ening of the nasals anterior to the prefrontals; (5) nasals that do not contact external nares; (6) a broad, flat interfenestral bar with raised rims along the supratemporal fenestrae; (7) a nasopharyngeal septum formed from diverging vomeral processes anteriorly and a midline pterygoid process posteriorly (also in Amphicotylus lucasii, AMNH FARB 5782); and (8) a vomeral septal complex that forms an X shape in cross section. Based on a revised phylogenetic analysis of 88 crocodylomorph taxa, a novel phylogenetic hypothesis is proposed in which a clade of Calsoyasuchus + Sunosuchus is sister to a clade of Goniopholis baryglyphaeus + (Goniopholis simus + (Amphicotylus lucasii + Eutretauranosuchus delfsi)). Examination of secondary palate characters in Goniopholididae suggests that this group “experimented” with changes to the bony secondary palate a number of times, resulting in morphologies not seen other mesoeucrocodylian clades.
DMRA: A Decentralized Resource Allocation Scheme for Multi-SP Mobile Edge Computing

Mobile Edge Computing (MEC) is a burgeoning paradigm that pushes data and services away from remote clouds to distributed Base Stations (BSs) equipped with MEC servers, which are deployed by Service Providers (SPs) at the edge of cellular networks. Normally, a SP prefers to use its own BSs, instead of those deployed by other SPs, to provide data and storage services. This can not only improve the quality of user experience but also increase its own revenue. In a densely deployed MEC network where a User Equipment (UE) tends to be covered by multiple BSs from varied SPs, how to allocate the resources in the BSs to provide the best service is a challenging problem. In this paper, we propose a novel resource allocation scheme, Decentralized Multi-SP Resource Allocation (DMRA), for densely-deployed MEC networks in order to maximize the total profit of all SPs and provide high-quality services. Our experimental results indicate that the proposed scheme outperforms the existing resource allocation algorithms for MEC.
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction

Improving the performance of click-through rate (CTR) prediction remains one of the core tasks in online advertising systems. With the rise of deep learning, CTR prediction models with deep networks remarkably enhance model capacities. In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests. As existing CTR prediction works neglect the importance of the temporal signals when embed users' historical clicking records, we propose a time-aware attention model which explicitly uses absolute temporal signals for expressing the users' periodic behaviors and relative temporal signals for expressing the temporal relation between items. Besides, we propose a regularized adversarial sampling strategy for negative sampling which eases the classification imbalance of CTR data and can make use of the strong guidance provided by the observed negative CTR samples. The adversarial sampling strategy significantly improves the training efficiency, and can be co-trained with the time-aware attention model seamlessly. Experiments are conducted on real-world CTR datasets from both in-station and out-station advertising places.
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering

Non-extractive commonsense QA remains a challenging AI task, as it requires systems to reason about, synthesize, and gather disparate pieces of information, in order to generate responses to queries. Recent approaches on such tasks show increased performance, only when models are either pre-trained with additional information or when domain-specific heuristics are used, without any special consideration regarding the knowledge resource type. In this paper, we perform a survey of recent commonsense QA methods and we provide a systematic analysis of popular knowledge resources and knowledge-integration methods, across benchmarks from multiple commonsense datasets. Our results and analysis show that attention-based injection seems to be a preferable choice for knowledge integration and that the degree of domain overlap, between knowledge bases and datasets, plays a crucial role in determining model success.
Development of event-related potentials (ERPS) in infants and preschool children with and without familial risk for dyslexia

The maturation of auditory event-related potentials (ERPs) is complex. Different ERP components have different developmental time schedules regarding to amplitudes and latencies. Decreased amplitude of the negative deflection mismatch negativity (MMN) has been connected to the language learning deficits. In the present study 52 infants aged 6 months and the same subjects in childhood aged 6,5 years were studied by using an oddball paradigm. The purpose was to find out the developmental courses of the ERP peaks and the differences in the detection of temporal changes of speech between the groups of control and at-risk for developmental dyslexia. The standard stimulus was a pseudoword /ata/ and the temporally different deviant stimulus /atta/ was a longer version of the same pseudoword. It was found that latencies of the deflections Pi190/c110 and Ni320/c225 shortened with age. Amplitudes decreased for Pi190/c110 and increased for Ni320/c225 with age. Pi190/c110 was thought to be a precursor of an adult P1 and Ni320/c225 of an adult N2. In childhood there probably was some sign of N1. The groups differed at the both ages in their automatic detection of temporal changes of speech. This shows that individuals at-risk for developmental dyslexia have deficient perception of temporal features of speech. However, this deficit had somewhat eased from infancy to childhood.
A machine-learning approach to Detect users' suspicious behaviour through the Facebook wall

Facebook represents the current de-facto choice for social media, changing the nature of social relationships. The increasing amount of personal information that runs through this platform publicly exposes user behaviour and social trends, allowing aggregation of data through conventional intelligence collection techniques such as OSINT (Open Source Intelligence). In this paper, we propose a new method to detect and diagnose variations in overall Facebook user psychology through Open Source Intelligence (OSINT) and machine learning techniques. We are aggregating the spectrum of user sentiments and views by using N-Games charts, which exhibit noticeable variations over time, validated through long term collection. We postulate that the proposed approach can be used by security organisations to understand and evaluate the user psychology, then use the information to predict insider threats or prevent insider attacks.