Miracle-dz/UniFAR-FTUs huggingface dataset:
Reusable garbled circuits and succinct functional encryption

Garbled circuits, introduced by Yao in the mid 80s, allow computing a function f on an input x without leaking anything about f or x besides f(x). Garbled circuits found numerous applications, but every known construction suffers from one limitation: it offers no security if used on multiple inputs x. In this paper, we construct for the first time reusable garbled circuits. The key building block is a new succinct single-key functional encryption scheme. Functional encryption is an ambitious primitive: given an encryption Enc(x) of a value x, and a secret key sk_f for a function f, anyone can compute f(x) without learning any other information about x. We construct, for the first time, a succinct functional encryption scheme for {\em any} polynomial-time function f where succinctness means that the ciphertext size does not grow with the size of the circuit for f, but only with its depth. The security of our construction is based on the intractability of the Learning with Errors (LWE) problem and holds as long as an adversary has access to a single key sk_f (or even an a priori bounded number of keys for different functions). Building on our succinct single-key functional encryption scheme, we show several new applications in addition to reusable garbled circuits, such as a paradigm for general function obfuscation which we call token-based obfuscation, homomorphic encryption for a class of Turing machines where the evaluation runs in input-specific time rather than worst-case time, and a scheme for delegating computation which is publicly verifiable and maintains the privacy of the computation.
Novel Architecture of High Speed Parallel MAC using Carry Select Adder

In this paper, new hardware architecture of multiplier and accumulator (MAC) for high speed arithmetic was designed. The performance was improved by merging multiplication with accumulation and organize a hybrid type carry save adder (CSA). The proposed CSA tree uses 1‟s complement based radix-4 and radix-8 modified booth algorithm(MBA). The CSA propagates the carries to the least significant bits of the partial products and generates the least significant bits in advance to reduce the number of the input bits of the final adder. This MAC add the intermediate results in the form of sum and carry bits instead of the final adder output, which made it possible to optimize the pipeline system to improve the performance. The final addition was carried out by high speed carry select adder (CSLA) with binary to excess convertor using CLA. Based on the theoretical and experimental estimation, we analyzed the results in terms of delay. The design is implemented using VHDL language and simulated using Xilinx ISE10.1 Simulator.
Fingerprint matching based on minutiae phase spectrum

Most fingerprint recognition systems are based on matching the location and orientation attributes of minutia points. In this paper, we propose a localized minutiae phase spectrum representation that encodes the local minutiae structure in the neighborhood of a given minutia point as a fixed-length binary code. Since this representation is invariant to global transformations (e.g., translation and rotation), the correspondences between the minutia points from two different fingerprints can be easily established based on the similarity (Hamming distance) between their phase spectral codes. In addition to determining the local minutiae similarities, a global similarity score can also be computed by aligning the query to the template based on the estimated correspondences and finding the similarity between the global phase spectra of the aligned minutiae sets. While the local similarity scores are robust to nonlinear fingerprint distortion, the global similarity score captures the highly distinctive spatial relationships between all the minutia points. Therefore, a combination of these two similarity measures provides high recognition accuracy. Experiments on the public-domain FVC2002 databases (DB1 and DB2) and FVC2006-DB2 demonstrate that the proposed approach achieves less than 1% Equal Error Rate, which is better than the state-of-the-art fingerprint matchers using only the location and orientation of minutia points.
Label Space Transfer Learning

Small datasets pose a tremendous challenge in machine learning due to the few available training examples compounded with the relative rarity of certain labels which can potentially impede the development of a representative hypothesis. We define "Rare Datasets" as ones with low samples/features ratio and a skewed label distribution. Since a generalized training model can not be theoretically guaranteed, a method to leverage similar data is needed. We propose the first algorithm that utilizes transfer learning for the label space, present theoretical verification of our method and demonstrate the effectiveness of our framework with several real-world experiments. In addition, we formally describe what constitutes a "Rare Dataset" and present a detailed characterization of related methods.
Partial Equalization for MC–CDMA Systems in Non-Ideally Estimated Correlated Fading

Multicarrier code-division multiple access (MC-CDMA) can support high data rates in next-generation multiuser wireless communication systems. Partial equalization (PE) is a low-complexity technique for combining the signals of subcarriers to improve the achievable performance of MC-CDMA systems in terms of their bit error probability (BEP) and bit error outage (BEO) in comparison with maximal ratio combining, orthogonality restoring combining, and equal-gain combining techniques. We analyze the performance of the multiuser MC-CDMA downlink and derive the optimal PE parameter expression, which minimizes the BEP. Realistic imperfect channel estimation and frequency-domain (FD) block-fading channels are considered. More explicitly, the analytical expression of the optimum PE parameter is derived as a function of the number of subcarriers, number of active users (i.e., the system load), mean signal-to-noise ratio (SNR), and variance of the channel-estimation errors for the aforementioned FD block-fading channel. We show that the choice of the optimal PE technique significantly increases the achievable system load for the given target BEP and BEO.
Model-Based Identification of Dominant Congested Links

In this paper, we propose a model-based approach that uses periodic end-end probes to identify whether a “dominant congested link” exists along an end-end path. Informally, a dominant congested link refers to a link that incurs the most losses and significant queuing delays along the path. We begin by providing a formal yet intuitive definition of dominant congested link and present two simple hypothesis tests to identify whether such a link exists. We then present a novel model-based approach for dominant congested link identification that is based on interpreting probe loss as an unobserved (virtual) delay. We develop parameter inference algorithms for hidden Markov model (HMM) and Markov model with a hidden dimension (MMHD) to infer this virtual delay. Our validation using ns simulation and Internet experiments demonstrate that this approach can correctly identify a dominant congested link with only a small amount of probe data. We further provide an upper bound on the maximum queuing delay of the dominant congested link once we identify that such a link exists.
Clinical versus quantitative vibration assessment: improving clinical performance

Abstract In 3 large cohorts (total of 787 patients), clinical vibration impairment (CVI) of the great toe using a tuning fork was compared with quantitative vibration threshold (QVT). Using a stepwise multivariate analysis, we assessed demographic and anthropomorphic patient characteristics associated with the difference between CVI and QVT for the various cohorts and the chosen QVT ranges of percentile abnormality. We also compared CVI or QVT abnormality with a composite score of nerve conduction abnormality to confirm that QVT is a valid measure of severity of neuropathy.
Presenilin-2 dampens intracellular Ca2+ stores by increasing Ca2+ leakage and reducing Ca2+ uptake

We have previously shown that familial Alzheimer’s disease mutants of presenilin‐2 (PS2) and, to a lesser extent, of presenilin‐1 (PS1) lower the Ca2+ concentration of intracellular stores. We here examined the mechanism by which wild‐type and mutant PS2 affect store Ca2+ handling. By using HeLa, SH‐SY5Y and MEFs as model cells, and recombinant aequorins as Ca2+ probes, we show evidence that transient expression of either wild‐type or mutant PS2 increases the passive Ca2+ leakage: both ryanodine‐ and IP3‐receptors contribute to Ca2+ exit out of the ER, whereas the ribosome translocon complex is not involved. In SH‐SY5Y cells and MEFs, wild‐type and mutant PS2 potently reduce the uptake of Ca2+ inside the stores, an effect that can be counteracted by over‐expression of SERCA‐2B. On this line, in wild‐type MEFs, lowering the endogenous level of PS2 by RNA interference, increases the Ca2+‐loading capability of intracellular stores. Furthermore, we show that in PS double knockout MEFs, reduction of Ca2+ stores is mimicked by the expression of PS2‐D366A, a loss‐of‐function mutant, uncleaved because also devoid of presenilinase activity but not by co‐expression of the two catalytic active fragments of PS2. In summary, both physiological and increased levels of wild‐type and mutant PS2 reduce the Ca2+ uptake by intracellular stores. To exert this newly described function, PS2 needs to be in its full‐length form, even if it can subsequently be cleaved.
Insights into multimodal imaging classification of ADHD

Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD.
Suspected snakebite in children: a study of 156 patients over 10 years

OBJECTIVE To describe the epidemiology and clinical features of children presenting to an emergency department with suspected snakebite. DESIGN A retrospective study of patient records. SETTING An emergency department of a children's teaching hospital (Princess Margaret Hospital) in Perth, Western Australia. PARTICIPANTS All children attending the emergency department from 1984 to 1993 with suspected snakebite. MAIN OUTCOME MEASURE Clinical and laboratory evidence of envenomation. RESULTS Over the decade studied, 156 children (mean age, six years and eight months) presented with suspected snakebite; over two-thirds (68%) were boys. In at least 31% of cases, no appropriate first aid had been applied. Only 14 children were envenomed according to clinical and laboratory criteria: 10 of these had coagulopathy; one of the 10 also had rhabdomyolysis. A Venom Detection Kit was used in 117 children. The test gave a positive result in 21 children (13%). Antivenom was given to 18 children, 14 of whom were definitely envenomed. Four of the envenomed children returned a negative result of Venom Detection Kit testing at all sites tested, and in five patients not clinically envenomed the urine specimen tested positive with the Venom Detection Kit (presumably a false positive result or subclinical envenomation). Of the 156 children, 130 were admitted to hospital, and 26 were discharged directly from the emergency department. All children recovered completely. CONCLUSIONS (i) Many children did not receive appropriate first aid for snakebite; (ii) Most children with suspected snakebite presenting to the emergency department were not envenomed; (iii) Envenomation was best diagnosed by clinical features and laboratory investigations, with the Venom Detection Kit being used to determine the appropriate antivenom; (iv) Discharging children directly from the emergency department is not recommended.
Word Order and Discontinuities in Dependency Grammar

Natural languages are always difficult to parse. Two phenomena that constantly pose problems for different formalisms are word order--what part of a sentence has to be placed where--and discontinuities--words that belong together but are not placed into the same phrase. Dependency grammar, a linguistic formalism based on binary relations between words, is very adequate for handling both problems. A parser for dependency grammar together with its grammar writing formalism is described in this paper. Word order and discontinuities in Hungarian are handled based on this formalism.
Ieee Transactions on Image Processing 1 Sparse Coding for Alpha Matting

—Existing color sampling based alpha matting methods use the compositing equation to estimate alpha at a pixel from pairs of foreground (F) and background (B) samples. The quality of the matte depends on the selected (F,B) pairs. In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives the estimate of the alpha matte from a set of unpaired F and B samples. A non-parametric probabilistic segmentation provides a certainty measure on the pixel belonging to foreground or background, based on which a dictionary is formed for use in sparse coding. By removing the restriction to conform to (F,B) pairs, this method allows for better alpha estimation from multiple F and B samples. The same framework is extended to videos, where the requirement of temporal coherence is handled effectively. Here, the dictionary is formed by samples from multiple frames. A multi-frame graph model, as opposed to a single image as for image matting, is proposed that can be solved efficiently in closed form. Quantitative and qualitative evaluations on a benchmark dataset are provided to show that the proposed method outperforms current state-of-the-art in image and video matting.
[Angiography with 64-channel CT upon suspicion of stable coronary disease].

BACKGROUND Invasive coronary angiography is the gold standard for diagnosing coronary artery disease. CT angiography (CTA) is a non-invasive alternative that is more available and less expensive. Previous Norwegian experience with 16-channel CT has been less successful than that reported from other countries. Improved image resolution has increased expectations of a better diagnostic accuracy, but a new local assessment of the method's usefulness is needed before routine usage is implemented. MATERIAL AND METHODS Patients with suspected stabile coronary disease, referred to invasive coronary angiography, were first assessed with 64-channel CT angiography. Patients with atrial fibrillation or previous bypass operation were not included. All patients who fulfilled the eligibility criteria were included in the study (104), but 13 who had an Agatston calcium score > or = 800 and three for whom the examinations were technically unsuccessful were excluded; the study material therefore consisted of 88 patients. We assessed the method's ability (diagnostic accuracy) to detect diameter stenoses > or = 50% in coronary segments > or = 2 mm (without stent). RESULTS When 4% non-interpretable segments were interpreted as positive (stenotic), the sensitivity was 97%, the specificity 78% and the positive and negative predictive values were 77 and 98% on a patient level. For 50 patients who did not have significant stenosis, CTA correctly identified 39, but overestimated the degree of stenosis in 11 patients. INTERPRETATION 64-channel CTA is best at ruling out obstructive coronary artery disease in patients with intermediate risk of such disease. Few patients with significant lesions were missed due to false negative CTA evaluations, but false positive findings are a problem in low risk populations.
Low-energy and multi-hop enhancement in wireless smart utility networks

This paper proposes an effective and low-energy multi-hop data collection scheme using a superframe division technique with frame aggregation in the wireless smart utility networks (SUNs). In the proposed scheme, MAC layer profile deploying IEEE 802.15. 4/4e-compliant low-energy (LE) superframe-based multi-hop transmission mitigates the bottleneck problem by controlling the radio link numbers in a superframe, thereby preventing the data collection throughput degradation expected in high-traffic situations such as emergencies. Computer simulation results confirm that the proposed superframe division multi-hop data collection scheme with frame aggregation achieves over a 95% frame success probability under the high-traffic situation in which 1,000 meter nodes generate frames at a 10-minute interval.
Outer-Inner Membrane Vesicles Naturally Secreted by Gram-Negative Pathogenic Bacteria

Outer-inner membrane vesicles (O-IMVs) were recently described as a new type of membrane vesicle secreted by the Antarctic bacterium Shewanella vesiculosa M7T. Their formation is characterized by the protrusion of both outer and plasma membranes, which pulls cytoplasmic components into the vesicles. To demonstrate that this is not a singular phenomenon in a bacterium occurring in an extreme environment, the identification of O-IMVs in pathogenic bacteria was undertaken. With this aim, a structural study by Transmission Electron Microscopy (TEM) and Cryo-transmission electron microscopy (Cryo-TEM) was carried out, confirming that O-IMVs are also secreted by Gram-negative pathogenic bacteria such as Neisseria gonorrhoeae, Pseudomonas aeruginosa PAO1 and Acinetobacter baumannii AB41, in which they represent between 0.23% and 1.2% of total vesicles produced. DNA and ATP, which are components solely found in the cell cytoplasm, were identified within membrane vesicles of these strains. The presence of DNA inside the O-IMVs produced by N. gonorrhoeae was confirmed by gold DNA immunolabeling with a specific monoclonal IgM against double-stranded DNA. A proteomic analysis of N. gonorrhoeae-derived membrane vesicles identified proteins from the cytoplasm and plasma membrane. This confirmation of O-IMV extends the hitherto uniform definition of membrane vesicles in Gram-negative bacteria and explains the presence of components in membrane vesicles such as DNA, cytoplasmic and inner membrane proteins, as well as ATP, detected for the first time. The production of these O-IMVs by pathogenic Gram-negative bacteria opens up new areas of study related to their involvement in lateral gene transfer, the transfer of cytoplasmic proteins, as well as the functionality and role of ATP detected in these new vesicles.
Motif discovery in infant- and adult-directed speech

Infant-directed speech (IDS) is thought to play a key role in determining infant language acquisition. It is thus important to describe how computational models of infant language acquisition behave when given an input of IDS, as compared to adult-directed speech (ADS). In this paper, we explore how an acoustic motif discovery algorithm fares when presented with speech from both registers. Results show small but significant differences in performance, with lower recall and lower cluster collocation in IDS than ADS, but a higher cluster purity in IDS. Overall, these results are inconsistent with a view suggesting that IDS is acoustically clearer than ADS in a way that systematically facilitates lexical recognition. Similarities and differences with human infants’ word segmentation are discussed.
Provably Sublinear Point Multiplication on Koblitz Curves and Its Hardware Implementation

We describe algorithms for point multiplication on Koblitz curves using multiple-base expansions of the form k = Sigmaplusmntaua(tau-1)b and k = Sigmaplusmntaua(tau - mu)b(tau2 - mutau - 1)c. We prove that the number of terms in the second type is sublinear in the bit length of k, which leads to the first provably sublinear point multiplication algorithm on Koblitz curves. For the first type, we conjecture that the number of terms is sublinear and provide numerical evidence demonstrating that the number of terms is significantly less than that of tau-adic nonadjacent form expansions. We present details of an innovative FPGA implementation of our algorithm and performance data demonstrating the efficiency of our method. We also show that implementations with very low computation latency are possible with the proposed method because parallel processing can be exploited efficiently.
A Method for Edge Detection in Hyperspectral Images Based on Gradient Clustering

Edge detection in hyperspectral images is an intrinsic difficult problem as the gray value intensity images related to single spectral bands may show different edges. The few existing approaches are either based on a straight forward combining of these individual edge images, or on finding the outliers in a region segmentation. We propose as an alternative a clustering of all image pixels in a feature space constructed by the spatial gradients in the spectral bands. An initial comparative study of various hyperspectral datasets shows the differences and properties of these approaches and makes clear that the proposal has interesting properties that may be studied further.
To index or not to index: time-space trade-offs in search engines with positional ranking functions

Positional ranking functions, widely used in Web search engines, improve result quality by exploiting the positions of the query terms within documents. However, it is well known that positional indexes demand large amounts of extra space, typically about three times the space of a basic nonpositional index. Textual data, on the other hand, is needed to produce text snippets. In this paper, we study time-space trade-offs for search engines with positional ranking functions and text snippet generation. We consider both index-based and non-index based alternatives for positional data. We aim to answer the question of whether one should index positional data or not. We show that there is a wide range of practical time-space trade-offs. Moreover, we show that both position and textual data can be stored using about 71% of the space used by traditional positional indexes, with a minor increase in query time. This yields considerable space savings and outperforms, both in space and time, recent alternatives from the literature. We also propose several efficient compressed text representations for snippet generation, which are able to use about half of the space of current state-of-the-art alternatives with little impact in query processing time.