Miracle-dz/UniFAR-FTUs huggingface dataset:
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction

Inductive learning systems have been successfully applied in a number of medical domains. It is generally accepted that the highest accuracy results that an inductive learning system can achieve depend on the quality of data and on the appropriate selection of a learning algorithm for the data. In this paper we analyze the effect of class noise on supervised learning in medical domains. We review the related work on learning from noisy data and propose to use feature extraction as a pre-processing step to diminish the effect of class noise on the learning process. Our experiments with 8 medical datasets show that feature extraction indeed helps to deal with class noise. It clearly results in higher classification accuracy of learnt models without the separate explicit elimination of noisy instances
Information Propagation in Social Networks with Overlapping Community Structure

Many real networks exhibit overlapping community structures. Recent studies have been performed that analyze the impact of overlapping community structure on information propagation, but few of them concerned with individual behaviors. From this point of view, we propose a Markov process model to evaluate the performance of information propagation in social networks with overlapping community structures. In addition, many individual social behaviors are combined in the model. For example, individuals may exhibit selfish behaviors, such as individual and social selfishness, and people may discard the information after they have used it. The accuracy of the model is verified by simulation. Furthermore, the numerical results show that both overlapping community structure of the network and individual behaviors have a significant impact on the outbreak size and propagation speed of the information. Additionally, the overlapping community structure of the social network can reduce the impact of selfishness on information propagation.
Intermittent calcitriol therapy in secondary hyperparathyroidism: a comparison between oral and intraperitoneal administration.

BACKGROUND Intermittent oral or intravenous doses of calcitriol given two or three times per week are commonly used to treat secondary hyperparathyroidism (secondary HPT). This study was undertaken to compare the biochemical and skeletal responses to thrice weekly intraperitoneal (i.p.) versus oral doses of calcitriol in children with secondary HPT undergoing peritoneal dialysis (CCPD). METHODS Forty-six patients aged 12.5+/-4.8 years on CCPD for 22+/-25 months were randomly assigned to treatment with oral (p.o.) or i.p. calcitriol for 12 months; 17 subjects given p.o. calcitriol and 16 subjects given i.p. calcitriol completed the study. Bone biopsies were performed at the beginning and at the end of the study, while determinations of serum and total ionized calcium, phosphorus, alkaline phosphatase, parathyroid hormone (PTH) and calcitriol levels were done monthly. RESULTS Serum total and ionized calcium levels were higher in subjects treated with i.p. calcitriol, P < 0.0001, whereas serum phosphorus levels were higher in those given p.o. calcitriol, P < 0.0001. For the i.p. group, serum PTH levels decreased from pre-treatment values of 648+/-125 pg/ml to a nadir of 169+/-57 pg/ml after nine months. In contrast, serum PTH levels did not change from baseline values of 670+/-97 pg/ml in subjects given p.o. calcitriol, P < 0.0001 by multiple regression analysis. Serum alkaline phosphatase levels were also lower in patients treated with i.p. calcitriol, P < 0.0001, but there was no difference between groups in the average dose of calcitriol given thrice weekly. The skeletal lesions of secondary HPT improved in both groups, 33% of patients developed adynamic bone lesion. CONCLUSION Differences in the bioavailability of calcitriol and/or in phosphorus metabolism may account for the divergent biochemical response to p.o. and i.p. calcitriol.
Social networks and labor market inequality between ethnicities and races

Abstract This paper analyzes the relationship between unexplained racial/ethnic wage differentials on the one hand and social network segregation, as measured by inbreeding homophily, on the other. Our analysis is based on both the US and Estonian surveys, supplemented with the Estonian telephone communication data. In the case of Estonia we consider the regional variation in economic performance of the Russian minority, and in the US case we consider the regional variation in black--white differentials. Our analysis finds a strong relationship between the size of the wage differential and network segregation: Regions with more segregated social networks exhibit larger unexplained wage gaps.
Immune Checkpoint Blockade Biology in Mouse Models of Glioblastoma

Glioblastoma Multiforme (GBM) is a highly malignant primary brain cancer that is associated with abysmal prognosis. The median survival of GBM patients is ∼15 months and there have not been any significant advance in therapies in over a decade, leaving treatment options limited. There is clearly an unmet need for GBM treatment. Immunotherapies are treatments based on usurping the power of the host's immune system to recognize and eliminate cancer cells. They have recently proven to be a successful strategy for combating a variety of cancers. Of the various types of immunotherapies, checkpoint blockade approaches have thus far produced significant clinical responses in several cancers including melanoma, non small‐cell lung cancer, renal cancer, and prostate cancer. This review focuses on the biological rationale for using checkpoint blockade immunotherapeutic approaches in primary brain cancer and an up‐to‐date summary of current and ongoing checkpoint inhibitors‐based clinical trials for malignant glioma. In addition, we expand on new concepts for further improving checkpoint blockade treatments, with a particular focus on the advantages of using genetically engineered mouse models for studies of immunotherapies in GBM. J. Cell. Biochem. 118: 2516–2527, 2017. © 2017 Wiley Periodicals, Inc.
A provably secure code‐based short signature scheme and its nontransferable variant

Signatures with partially message recovery in which some parts of messages are not transmitted with signatures to make them shorter are helpful where bandwidth is one of the critical concern. This primitive is especially used for signing short messages in applications such as time stamping, certified email services, and identity‐based cryptosystems. In this paper, to have quantum‐attack‐resistant short signatures, the first signature scheme with partially message recovery based on coding theory is presented. Next, it is shown that the proposal is secure under Goppa Parametrized Bounded Decoding and the Goppa Code Distinguishing assumptions in the random oracle model. Relying on the partially message recovery property, the proposal is shorter than Dallot signature scheme, the only provably secure and practical code‐based signature scheme, while it preserves Dallot signature efficiency. We should highlight that our scheme can be used as a building block to construct short code‐based signature schemes with special properties. To show this, we present a provably secure short designated verifier signature scheme, a nontransferable form of short signatures, which is used in electronic voting and deniable authentication protocols.
Seasonally Varying Predation Behavior and Climate Shifts Are Predicted to Affect Predator-Prey Cycles

The functional response of some predator species changes from a pattern characteristic for a generalist to that for a specialist according to seasonally varying prey availability. Current theory does not address the dynamic consequences of this phenomenon. Since season length correlates strongly with altitude and latitude and is predicted to change under future climate scenarios, including this phenomenon in theoretical models seems essential for correct prediction of future ecosystem dynamics. We develop and analyze a two-season model for the great horned owl (Bubo virginialis) and snowshoe hare (Lepus americanus). These species form a predator-prey system in which the generalist to specialist shift in predation pattern has been documented empirically. We study the qualitative behavior of this predator-prey model community as summer season length changes. We find that relatively small changes in summer season length can have a profound impact on the system. In particular, when the predator has sufficient alternative resources available during the summer season, it can drive the prey to extinction, there can be coexisting stable states, and there can be stable large-amplitude limit cycles coexisting with a stable steady state. Our results illustrate that the impacts of global change on local ecosystems can be driven by internal system dynamics and can potentially have catastrophic consequences.
I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators

Deep learning has become the de-facto computational paradigm for various kinds of perception problems, including many privacy-sensitive applications such as online medical image analysis. No doubt to say, the data privacy of these deep learning systems is a serious concern. Different from previous research focusing on exploiting privacy leakage from deep learning models, in this paper, we present the first attack on the implementation of deep learning models. To be specific, we perform the attack on an FPGA-based convolutional neural network accelerator and we manage to recover the input image from the collected power traces without knowing the detailed parameters in the neural network. For the MNIST dataset, our power side-channel attack is able to achieve up to 89% recognition accuracy.
HMC-MAC: Processing-in Memory Architecture for Multiply-Accumulate Operations with Hybrid Memory Cube

Many studies focus on implementing processing-in memory (PIM) on the logic die of the hybrid memory cube (HMC) architecture. The multiply-accumulate (MAC) operation is heavily used in digital signal processing (DSP) systems. In this paper, a novel PIM architecture called HMC-MAC that implements the MAC operation in the HMC is proposed. The vault controllers of the conventional HMC are working independently to maximize the parallelism, and HMC-MAC is based on the conventional HMC without modifying the architecture much. Therefore, a large number of MAC operations can be processed in parallel. In HMC-MAC, the MAC operation can be carried out simultaneously with as much as 128 KB data. The correctness on HMC-MAC is verified by simulations, and its performance is better than the conventional CPU-based MAC operation when the MAC operation is consecutively executed at least six times
IL-23 plays a key role in Helicobacter hepaticus–induced T cell–dependent colitis

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract that is caused in part by a dysregulated immune response to the intestinal flora. The common interleukin (IL)-12/IL-23p40 subunit is thought to be critical for the pathogenesis of IBD. We have analyzed the role of IL-12 versus IL-23 in two models of Helicobacter hepaticus–triggered T cell–dependent colitis, one involving anti–IL-10R monoclonal antibody treatment of infected T cell–sufficient hosts, and the other involving CD4+ T cell transfer into infected Rag−/− recipients. Our data demonstrate that IL-23 and not IL-12 is essential for the development of maximal intestinal disease. Although IL-23 has been implicated in the differentiation of IL-17–producing CD4+ T cells that alone are sufficient to induce autoimmune tissue reactivity, our results instead support a model in which IL-23 drives both interferon γ and IL-17 responses that together synergize to trigger severe intestinal inflammation.
Autonomous cooperative driving using V2X communications in off-road environment

During the last decade, a great deal of attention is received for the idea of designing autonomous vehicles, with intelligent, cooperative and coordinated capabilities to reach an overall optimal performance and robustness. Almost all intelligent vehicles are currently equipped with computing modules, communication modules and multiple sensors for environment perception. Accordingly, it is a matter of utilizing these sensors and modules to enhance and improve the transportation safety and efficiency. In this paper, autonomous cooperative driving using various communications schemes in the off-road environment is presented. Moreover, it highlights the importance of Vehicle-To-Vehicle (V2V) communication and shows the advantages of Vehicle-To-Pedestrian (V2P) and Pedestrian-To-Vehicle (P2V) communications when added to the system environment perception. Several experiments have been performed for real-life scenarios to verify the results, and evaluate the performance of the proposed approach. The experiments outcomes prove the high performance of the proposed approach in real-world application and emphasize the importance of V2X communications in autonomous vehicles.
An Efficient Local Search for the Maximum Edge Weighted Clique Problem

The maximum edge weighted clique problem (MEWCP), an extension of the classical maximum clique problem, is an important NP-hard combinatorial optimization problem. The problem has been widely used in various areas. The objective of this paper is to design an efficient local search algorithm to solve the MEWCP. First, the proposed scoring strategy is used to evaluate the benefit of adding and swapping operators. Second, the vertex weighting strategy is used to increase the diversity of solutions and the configuration checking strategy is used to avoid the cycling problem. By combining these three strategies, we propose multiple rules to select the added vertex or the swapped vertex pair. Based on the multiple rules, an efficient local search algorithm, namely, local search based on multiple rules (LSMR), is proposed. LSMR is compared with several representative algorithms on massive graph instances. The experimental results indicate that LSMR is superior to competitors in terms of solution quality and computational efficiency in most instances.
Modulation in the Air: Backscatter Communication Over Ambient OFDM Carrier

Ambient backscatter communication (AmBC) enables radio-frequency (RF) powered backscatter devices (BDs) (e.g., sensors and tags) to modulate their information bits over ambient RF carriers in an over-the-air manner. This technology, also called “modulation in the air,” has emerged as a promising solution to achieve green communication for future Internet of Things. This paper studies an AmBC system by leveraging the ambient orthogonal frequency division multiplexing (OFDM) modulated signals in the air. We first model such AmBC system from a spread-spectrum communication perspective, upon which a novel joint design for BD waveform and receiver detector is proposed. The BD symbol period is designed as an integer multiplication of the OFDM symbol period, and the waveform for BD bit “0” maintains the same state within the BD symbol period, while the waveform for BD bit “1” has a state transition in the middle of each OFDM symbol period within the BD symbol period. In the receiver detector design, we construct the test statistic that cancels out the direct-link interference by exploiting the repeating structure of the ambient OFDM signals due to the use of cyclic prefix. For the system with a single-antenna receiver, the maximum-likelihood detector is proposed to recover the BD bits, for which the optimal threshold is obtained in closed-form expression. For the system with a multi-antenna receiver, we propose a new test statistic which is a linear combination of the per-antenna test statistics and derive the corresponding optimal detector. The proposed optimal detectors require only knowing the strength of the backscatter channel, thus simplifying their implementation. Moreover, practical timing synchronization algorithms are proposed for the designed AmBC system, and we also analyze the effect of various system parameters on the transmission rate and detection performance. Finally, extensive numerical results are provided to verify that the proposed transceiver design can improve the system bit-error-rate performance and the operating range significantly and achieve much higher data rate, as compared with the conventional design.
Improved tone recognition by normalizing for coarticulation and intonation effects

We have previously demonstrated that tone modeling improved speech recognition on a digit corpus [7]. In this work, we further improve tone recognition by normalizing for both tone coartic-ulation and intonation effects. The tone classiﬁcation errors on continuous digit strings were reduced by 26.1% from the base-line, when the effects of F0 downdrift, phrase boundary and tone coarticulation were normalized. We also applied the same approach to conversational speech from the YINHE domain [6], and obtained similar improvements. The word error rate on sponta-neous YINHE data was reduced by 16.5% when a simple four-tone model was applied to resort recognizer 10 -best outputs.
Pupil Dilation May Affect Retinal Vessel Caliber Measures

ABSTRACT Purpose: To assess the effect of pupil dilation on measures of retinal vessel caliber. Design: Observational study with self-comparisons Methods: Retinal photographs were taken for both eyes of 25 subjects before and after pupil dilation. Three photographic graders, masked to pupil dilation status, measured the same set of images using a computer-assisted, semi-automatic method. We compared means (standard deviations) of retinal arteriolar caliber equivalent (CRAE) and retinal venular caliber equivalent (CRVE) of the same eyes between pre- and post-dilation images. We assessed concordance correlation coefficients (CC), Bland Altman limits-of-agreements, and used linear mixed models to assess CRAE and CRVE measures associated with pupil dilation (influencing image quality), graders (observers) and right-left eye variation. Results: We found high CCs for CRAE (0.82-0.94) and CRVE (0.87-0.94) between pre- and post-dilation images of the same eyes across the graders. Bland Altman plots showed that mean differences ranged from 0.55-3. 42μm for CRAE and 1.56-2. 29μm for CRVE. After adjusting for right-left eye random variation, a significant fixed effect of dilation was evident in mean CRAE in two of the three graders. There was no significant fixed effect of dilation in mean CRVE across all graders. In models including data of both eyes’ measures from pre- and post-dilation images by three graders, the fixed effect for dilation status contributed significantly to CRAE and CRVE variances whereas random effects for graders and dilation status contributed minimally. Conclusions: Contrary to our hypothesis, we found a systematic effect of pupil dilation on retinal vessel caliber measures.
Nonuniformity Correction Based on Adaptive Sparse Representation Using Joint Local and Global Constraints Based Learning Rate

The infrared focal plane array (IRFPA) suffers from the problem of non-uniform photoelectric responsivity of infrared detectors, namely nonuniformity. This paper aims at removing the fixed pattern noise (FPN) brought by nonuniformity in IRFPA through scene-based nonuniformity correction (SBNUC). Different from traditional neural network-based methods where spatial filters are adopted, a new approach of acquiring the desired image (the target of optimization task) is proposed, which is based on the sparse representation theory. The sparse representation of the image corrected by the current correction coefficients is used as the desired image. During sparse coding, the error tolerance is computed from an adaptive criterion, which is negatively correlated to the root-mean-square error between the input image and the corrected image. The resulted desired image is more accurate than its counterparts in traditional filter-based methods, as the edges can be preserved without heavily blurring image details, while the elements of FPN are omitted as redundant when coding the representation coefficients. To suit this desired image, a new adaptive learning rate based on joint local and global constraints is proposed. Different from traditional local spatial standard deviation-based learning rate, the constraints are calculated based on the variations among the input image, the corrected image, and the desired image. A new motion detection rule is also incorporated to this learning rate, which could be maximized to accelerate convergence only if the scene motion is sufficient both globally and locally. Experiments are conducted on both public infrared image sets (the Video Verification of Identity program data set and the Automonous Systems Lab data set) and a private infrared sequence. Experimental results demonstrate that, the proposed method achieves better performance of FPN removal on infrared sequences with both simulated and real nonuniformity, compared with SBNUC methods based on spatial filter (including Gaussian filter, guided filter, and bilateral filter).
An Augmented Lagrangian Based Compressed Sensing Reconstruction for Non-Cartesian Magnetic Resonance Imaging without Gridding and Regridding at Every Iteration

Background Non-Cartesian trajectories are used in a variety of fast imaging applications, due to the incoherent image domain artifacts they create when undersampled. While the gridding technique is commonly utilized for reconstruction, the incoherent artifacts may be further removed using compressed sensing (CS). CS reconstruction is typically done using conjugate-gradient (CG) type algorithms, which require gridding and regridding to be performed at every iteration. This leads to a large computational overhead that hinders its applicability. Methods We sought to develop an alternative method for CS reconstruction that only requires two gridding and one regridding operation in total, irrespective of the number of iterations. This proposed technique is evaluated on phantom images and whole-heart coronary MRI acquired using 3D radial trajectories, and compared to conventional CS reconstruction using CG algorithms in terms of quantitative vessel sharpness, vessel length, computation time, and convergence rate. Results Both CS reconstructions result in similar vessel length (P = 0.30) and vessel sharpness (P = 0.62). The per-iteration complexity of the proposed technique is approximately 3-fold lower than the conventional CS reconstruction (17.55 vs. 52.48 seconds in C++). Furthermore, for in-vivo datasets, the convergence rate of the proposed technique is faster (60±13 vs. 455±320 iterations) leading to a ∼23-fold reduction in reconstruction time. Conclusions The proposed reconstruction provides images of similar quality to the conventional CS technique in terms of removing artifacts, but at a much lower computational complexity.