Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonfluent/Agrammatic PPA with In-Vivo Cortical Amyloidosis and Pick’s Disease Pathology

The role of biomarkers in predicting pathological findings in the frontotemporal dementia (FTD) clinical spectrum disorders is still being explored. We present comprehensive, prospective longitudinal data for a 66 year old, right-handed female who met current criteria for the nonfluent/agrammatic variant of primary progressive aphasia (nfvPPA). She first presented with a 3-year history of progressive speech and language impairment mainly characterized by severe apraxia of speech. Neuropsychological and general motor functions remained relatively spared throughout the clinical course. Voxel-based morphometry (VBM) showed selective cortical atrophy of the left posterior inferior frontal gyrus (IFG) and underlying insula that worsened over time, extending along the left premotor strip. Five years after her first evaluation, she developed mild memory impairment and underwent PET-FDG and PiB scans that showed left frontal hypometabolism and cortical amyloidosis. Three years later (11 years from first symptom), post-mortem histopathological evaluation revealed Pick's disease, with severe degeneration of left IFG, mid-insula, and precentral gyrus. Alzheimer’s disease (AD) (CERAD frequent/Braak Stage V) was also detected. This patient demonstrates that biomarkers indicating brain amyloidosis should not be considered conclusive evidence that AD pathology accounts for a typical FTD clinical/anatomical syndrome.
Community Core Evolution in Mobile Social Networks

Community detection in social networks attracts a lot of attention in the recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. For example, a casual visit in Facebook cannot be seen as an establishment of friendship. Hence, our question is the following: how to cluster the real friends in mobile social networks? In this paper, we study the problem of detecting the stable community core in mobile social networks. The cumulative stable contact is proposed to depict the relationship among nodes. The whole process is divided into timestamps. Nodes and their connections can be added or removed at each timestamp, and historical contacts are considered when detecting the community core. Also, community cores can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community cores in mobile social networks.
IMPROVING THE EFFECTIVENESS AND EFFICIENCY OF EVIDENCE PRODUCTION FOR HEALTH TECHNOLOGY ASSESSMENT

Objectives: Health Technology Assessment (HTA) needs to address the challenges posed by high cost, effective technologies, expedited regulatory approaches, and the opportunities provided by collaborative real-world evaluation of technologies. The Health Technology Assessment International (HTAi) Policy Forum met to consider these issues and the implications for evidence production to inform HTA. This paper shares their discussion to stimulate further debate. Methods: A background paper, presentations, group discussions, and stakeholder role play at the 2015 HTAi Policy Forum meeting informed this paper. Results: HTA has an important role to play in helping improve evidence production and ensuring that the health service is ready to adopt effective technologies. It needs to move from simply informing health system decisions to also working actively to align stakeholder expectations about realistic evidence requirements. Processes to support dialogue over the health technology life cycle need to be developed that are mindful of limited resources, operate across jurisdictions and learn from past processes. Collaborations between health technology developers and health systems in different countries should be encouraged to develop evidence that will inform decision making. New analytical techniques emerging for real-world data should be harnessed to support modeling for HTA. Conclusions: A paradigm shift (to “Health Innovation System 2.0”) is suggested where HTA adopts a more central, proactive role to support alignment within and amongst stakeholders over the whole life cycle of the technology. This could help ensure that evidence production is better aligned with patient and health system needs and so is more effective and efficient.
3se: a semi-structured search engine for heterogeneous data in graph model

As the ubiquitous interplay of structured, semi-structured and unstructured data from different sources, neither DB-style structured query requiring knowledge of full schema and complex language, nor IR-style keyword search ignoring latent structures, can satisfy users. In this paper, we present a novel Semi-Structured Search Engine (3SE) that provides easy, flexible, precise and rapid access to heterogeneous data represented by a semi-structured graph model. By using an intuitive 3SE Query Language (3SQL), users are able to pose queries on heterogeneous data in a varying degree of structural constraint according to their knowledge of schema. 3SE evaluates 3SQL queries as the top-k answers composed of "logical units" and relationship paths between them, and thus can extract meaningful information even if the query conditions are vague, ambiguous, and inaccurate.
An Advanced Technique for Volumetric Analysis

An accurate segmentation is critical, especially when the tumor morphological changes remain subtle, irregular and difficult to assess by clinical examination. This quantitative measurement depends on the accuracy of the segmentation method used. The undesired partial volume effect, which lies on a boundary between a high intensity region and low intensity region, makes unerring boundary determination a difficult task. A new approach to segmentation is proposed that removes the adverse effect on the boundary, which is unwanted especially from the point of view of volume rendering. This approach gives more accurate boundary detection and holes filling after segmentation. A semi-automatic calculation of volumetric size of brain tumor has been implemented in this approach. A comparative analysis of manual, seeded region growing and this advance approach shows more accurate and better performance for 3D volume measurements. This method is tested by two patients of different tumor type and shape, and better results are reported.
Balancing coarse-grained pipelined architectures using multiple clock domains on platform FPGA

Pipelining is a fundamental technique used in computing machines to enhance throughput by exploiting concurrency between different stages of computation. Due to the inter-play between many aspects of the algorithm and the ability of either the tool or the designer to leverage architectural features of modern day platform FPGAs, building efficient pipelined architecture for a given algorithm on FPGA still remains an art. A particular aspect of coarse-grained pipelined architecture that has a large impact on performance as well as resource usage is the balancing of individual stages in terms of the delay of each stage. In this paper we present a technique that uses the on-chip clocking resources on platform FPGAs to derive multiple clocks to achieve better balancing of coarse-grained pipelined architectures. In addition, we show that our technique is orthogonal to existing compiler scheduling techniques and can be deployed with minor changes to the top-level design and its associated constraints. Our experiments on an IP-router application show that this technique can be used to either improve the throughput or used to reduce the internal buffering required between stages due to the mis-match in delay of different stages of the pipelined architecture. In particular, our implementation on a Xilinx Virtex-5 device shows that we are able to reduce the LUT usage by 10% and improve the throughput by 20% using our technique for a given target frequency.
Computing throughput capacity for realistic wireless multihop networks

Capacity is an important property for QoS support in Mobile Ad Hoc Networks (MANETs) and has been extensively studied. However, most approaches rely on simplified models (isotropic radio propagation, unidirectional links, perfect scheduling, perfect routing, etc.) and either provide asymptotic bounds or are based on integer linear programming solvers. In this paper we present a probabilistic approach to capacity calculation by linking the normalized throughput of a communication pair in an ad hoc network to the connection probability of the two nodes in a so called schedule graph GT(N,E). The effective throughput of a random network is modelled as a random variable and expected values of it are computed using Monte-Carlo methods. A schedule graph GT(N,E) for a given network directly emerges from the physical properties of the network, like node distribution, radio propagation or channel assignment. The modularity of the approach allows for capacity analysis under more realistic network models. In the paper throughput capacity is computed for various forms of network configurations and the results are compared to simulation results obtained with ns-2.
Electromagnetic, complex image model of a large area RF resonant antenna as inductive plasma source

A large area antenna generates a plasma by both inductive and capacitive coupling; it is an electromagnetically coupled plasma source. In this work, experiments on a large area planar RF antenna source are interpreted in terms of a multi-conductor transmission line coupled to the plasma. This electromagnetic treatment includes mutual inductive coupling using the complex image method, and capacitive matrix coupling between all elements of the resonant network and the plasma. The model reproduces antenna input impedance measurements, with and without plasma, on a 1.2 × 1.2 m2 antenna used for large area plasma processing. Analytic expressions are given, and results are obtained by computation of the matrix solution. This method could be used to design planar inductive sources in general, by applying the termination impedances appropriate to each antenna type.
HermesE: A 96-Channel Full Data Rate Direct Neural Interface in 0.13 $\mu$ m CMOS

A power and area efficient sensor interface consumes 6.4 mW from 1.2 V while occupying 5 mm × 5 mm in 0.13 μm CMOS. The interface offers simultaneous access to 96 channels of broadband neural data acquired from cortical microelectrodes as part of a head-mounted wireless recording system, enabling basic neuroscience as well as neuroprosthetics research. Signals are conditioned with a front-end achieving 2.2 μVrms input-referred noise in a 10 kHz bandwidth before conversion at 31.25 kSa/s by 10-bit SAR ADCs with 60.3 dB SNDR and 42 fJ/conv-step. Switched-capacitor filtering provides a well-controlled frequency response and utilizes windowed integrator sampling to mitigate noise aliasing, enhancing noise/power efficiency.
Scandinavian Approaches to Participatory Design

What is distinctive about Scandinavian participatory design approaches? What can we learn from Scandinavian participatory design approaches that we can take into our own design practices, collaborations in design, and design pedagogy? The discussion argues that three principles distinguish Scandinavian approaches to participatory design: striving for democracy and demo-cratisation; explicit discussions of values in design and imagined futures; and ways that conflicts and contradictions are regarded as resources in design. The author draws on recent experiences in Norway, in multi-disciplinary and international collaborations in health informatics. Background on Scandinavian approaches to participatory design is provided to give a sense of their distinctive history and critiques reflecting on problems and limits encountered. An instance of information systems interface design is presented in order to talk concretely about Scandinavian participatory design principles in contrast to mainstream systems design traditions in the United States.
Fast Depth Map Upsampling using Edge Information

In this paper, we propose a new depth map upsampling method to increase the depth image resolution using edge information. Although the joint bilateral upsampling (JBU) method expands the resolution of the depth map using two weighting functions, the complexity of JBU is relatively high. In the proposed upsampling method, we reduce the complexity of depth map upsmapling operation using a color weighting function chosen by the guide information. We use the modified mean absolute difference value with the Gaussian weighting function to obtain the guide information. Experimental results show that the proposed upsampler outperforms JBU in terms of the computational complexity. Keywords—joint bilateral filter; depth map upsampling
Typical performance of generalised vector channels

We introduce a general matrix formulation for multiuser channels and analyse the special cases of Multiple-Input Multiple-Output channels, channels with interference and relay arrays under LDPC coding using methods developed for the statistical mechanics of disordered systems. We use the replica method to provide results for the typical overlaps of the original and recovered messages and discuss their implications. The results obtained are consistent with belief propagation and density evolution results but also complement them giving additional insights into the information dynamics of these channels with unexpected effects in some cases.
Breadth First Search on Massive Graphs

We consider the problem of Breadth First Search (BFS) traversal on massive sparse undirected graphs. Despite the existence of simple linear time algorithms in the RAM model, it was considered non-viable for massive graphs because of the I/O cost it incurs. Munagala and Ranade [29] and later Mehlhorn and Meyer [27] gave efficient algorithms (refered to as MR BFS and MM BFS, respectively) for computing BFS level decompositions in an external memory model. Ajwani et al. [3] implemented MR BFS and the randomized variant of MM BFS using the external memory library STXXL and gave a comparative study of the two algorithms on various graph classes. In this paper, we review and extend that result demonstrating the effectiveness and viability of the BFS implementations on various other synthetic and real world benchmarks. Furthermore, we present the implementation of the deterministic variant of MM BFS and show that in most cases, it outperforms the randomized variant.
Partially occluded face recognition using subface hidden Markov models

An improved subface Hidden Markov model based on local texture with Discrete Cosine Transform (DCT) is proposed to recognize partially occluded faces. After LPQ (Local Phase Quantization) or LBP (Local Binary Pattern) features are extracted, the DCT is applied to generate the subface HMM's observed vectors. All local subface HMMs of a face are then combined to a global HMM. Each partial occlusion is estimated by Haar features and is assigned with relevant weight automatically. The experiments on AR database show that local facial texture features transformed by DCT as HMM's observed vectors are appropriate for partially occluded face recognition.
Data Fusion in WSNs: Architecture, Taxonomy, Evaluation of Techniques, and Challenges

In WSNs, the most critical issue is energy consumption as sensor nodes have limited resources. The sensors collect data from the environment where they can fail due to variations in pressure, temperature, and electromagnetic noise. All these can result in misleading readings and measurements where a lot of energy is consumed. Therefore, data fusion is used to overcome these challenges as it assures the accuracy and the efficiency of gathered data, and eliminates data redundancy which results in saving power, thus improving the overall network performance. This paper provides a survey of research related to the data fusion domain to explore many aspects of data fusion in terms of architecture, taxonomy, and techniques and methods. It also evaluates and compares these techniques as it investigates the advantages and the drawbacks of each, and emphasizes the applicability of these techniques in the WSN domain. Finally, it presents the data fusion challenges in WSNs.
Wide-w-NAF Method for Scalar Multiplication on Koblitz Curves

At CRYPTO 1991, Koblitz proposed the anomalous binary curves for speeding up scalar multiplication in elliptic curve cryptosystem. At CRYPTO 1997, Solinas proposed the tau-NAF method on Koblitz curves and reduced the Hamming weight of the scalar to n/3 over the field F2n. At PKC 2004, Avanzi et al combined the tau-NAF with one point halving and reduced the Hamming weight of the scalar to 2n/7. Recently, Avanzi et al improved this method by introducing the wide-double-NAF whose Hamming weight is n/4. In this paper, we propose the wide-w-NAF, which is an extension of Avanzi's wide-double-NAF, and reduce the Hamming weight to n/(w + 1). When n > 144, our method is at least 43%-56% faster than Solinas's tau-NAF method and 21%-39% faster than Avanzi's wide-double-NAF method without additional memory requirements.
Biological Synthesis of Silver Nanoparticles and their Functional Properties

A nanoparticle is defined as a small object between 1 and 100 nanometer in size and has a large surface to volume ratio. Silver nanoparticles (AgNPs) could be synthesized using various chemical and physical processes. However, these methods lead to hazardous by-products. In the recent past, AgNPs are produced by biological means. The size, shape and composition of AgNPs have significant effect on their biological applications. Aqueous solution of AgNP is not stable and rapidly undergoes agglomeration which is prevented by electrostatic or steric stabilization techniques with the help of capping or protective agents. The biologically synthesized nanoparticles are now favoured because it is a green alternative, mild, and does not need toxic chemicals and solvents. The scope of this review is to provide an overview of the various biological means researched for the synthesis of AgNPs, different techniques and chemicals used to develop stable solution, various techniques for their characterization, and their biological. The future research directions in this subject area are also dicussed.
Optimal Coding in Biological and Artificial Neural Networks

Feature representations in both, biological neural networks in the primate ventral stream and artificial convolutional neural networks trained on object recognition, incresase in complexity and receptive field size with layer depth. Somewhat strikingly, empirical evidence indicates that this analogy extends to the specific representations learned in each layer. This suggests that biological and artificial neural networks share a fundamental organising principle. We shed light on this principle in the framework of optimal coding. Specifically, we first investigate which properties of a code render it robust to transmission over noisy channels and formally prove that for equientropic channels an upper bound on the expected minimum decoding error is attained for codes with maximum marginal entropy. We then show that the pairwise correlation of units in a deep layer of a neural network, that has been trained on an object recognition task, increases when perturbing the distribution of input images, i. e. , that the network exhibits properties of an optimally coding system. By analogy, this suggests that the layer-wise similarity of feature representations in biological and artificial neural networks is a result of optimal coding that enables robust transmission of object information over noisy channels. Because we find that in equientropic channels the upper bound on the expected minimum decoding error is independent of the class-conditional entropy, our work further provides a plausible explanation why optimal codes can be learned in unsupervised settings.
Source camera attribution using stabilized video

Although PRNU (Photo Response Non-Uniformity)-based methods have been proposed to verify the source camera of a non-stabilized video, these methods may not be adequate for stabilized videos. The use of video stabilization has been increasing in recent years with the development of novel stabilization software and the availability of stabilization in smart-phone cameras. This paper presents a PRNU-based source camera attribution method for out-of-camera stabilized video (i.e., stabilization applied after the video is captured). The scheme can (i) automatically determine if a given video is stabilized, (ii) calculate the fingerprint from a stabilized video, and (iii) effectively correlate the fingerprint computed from a stabilized video (i.e., anonymous video) with a fingerprint computed from another stabilized or non-stabilized video (i.e., a known video). Furthermore, experimental results show that the source camera of an anonymous non-stabilized video can be verified using a fingerprint computed from a set of images.
Augmenting tandem language learning with the TandemTable

In this paper, we present our computer-assisted language learning system called TandemTable. It is designed for a multi-touch tabletop and is meant to aid co-located tandem language learners during their learning sessions. By suggesting topics of discussion, and presenting learners with a variety of conversation-focused collaborative activities with shared digital artifacts, the system helps to inspire conversations and help them flow.
A permutations-based algorithm for fast alignment of long paired-end reads

random-permutations-based algorithm is presented for alignment of long paired-end reads. An implementation of the algorithm is shown to process a million pairs of 100 − 500bp-long reads in 3-10 minutes on a single CPU, correctly aligning more reads than popular fast aligners, 5-100 times faster. Approved for public release: distribution is unlimited.
Complexity and Approximation of the Fuzzy K-Means Problem

The fuzzy $K$-means problem is a generalization of the classical $K$-means problem to soft clusterings, i.e. clusterings where each points belongs to each cluster to some degree. Although popular in practice, prior to this work the fuzzy $K$-means problem has not been studied from a complexity theoretic or algorithmic perspective. We show that optimal solutions for fuzzy $K$-means cannot, in general, be expressed by radicals over the input points. Surprisingly, this already holds for very simple inputs in one-dimensional space. Hence, one cannot expect to compute optimal solutions exactly. We give the first $(1+\epsilon)$-approximation algorithms for the fuzzy $K$-means problem. First, we present a deterministic approximation algorithm whose runtime is polynomial in $N$ and linear in the dimension $D$ of the input set, given that $K$ is constant, i.e. a polynomial time approximation algorithm given a fixed $K$. We achieve this result by showing that for each soft clustering there exists a hard clustering with comparable properties. Second, by using techniques known from coreset constructions for the $K$-means problem, we develop a deterministic approximation algorithm that runs in time almost linear in $N$ but exponential in the dimension $D$. We complement these results with a randomized algorithm which imposes some natural restrictions on the input set and whose runtime is comparable to some of the most efficient approximation algorithms for $K$-means, i.e. linear in the number of points and the dimension, but exponential in the number of clusters.
Improving ℒ2 Gain Performance of Linear Systems by Reset Control

Abstract In this paper, new Lyapunov-based reset rules are constructed to improve ℒ 2 gain performance of linear-time-invariant (LTI) systems. By using the hybrid system framework, sufficient conditions for exponential and finite gain ℒ 2 stability are presented. It is shown that the ℒ 2 gain of the closed loop system with resets can be improved compared with the base system. Numerical example supports our results.