Miracle-dz/UniFAR-FTUs huggingface dataset:
The Accurate Estimation of Disparity Maps from Cross-Scale Reference-Based Light Field

This paper addresses the problem of disparity map accurate estimation in the cross-scale reference-based light field, which consists several low-quality images arranged around one central high-resolution (HR) image. In the framework, we use a HR image-guidance CNN (HRIG-CNN) for estimating the disparity map in the HR level. Specifically, we first calculate the coarse disparity map using our cross-pattern strategy, which can blend the multiple disparity maps. And then, we refine this coarse disparity map using HRIG-CNN for obtaining high-quality disparity map, which contains detail information and preserve edge information. With the HR image guidance, our HRIG-CNN achieves state-of-the-art for obtaining disparity map in such hybrid light field condition. In the end, we provide both quantitative and qualitative evaluations on different methods, and demonstrate the high performance and robustness of the proposed framework compared with the state-of-the-arts algorithms.
Optimum Overflow Thresholds in Variable-Length Source Coding Allowing Non-Vanishing Error Probability

The variable-length source coding problem allowing the error probability up to some constant is considered for general sources. In this problem, the optimum mean codeword length of variable-length codes has already been determined. On the other hand, in this paper, we focus on the overflow (or excess codeword length) probability instead of the mean codeword length. The infimum of overflow thresholds under the constraint that both of the error probability and the overflow probability are smaller than or equal to some constant is called the optimum overflow threshold. In this paper, we first derive finite-length upper and lower bounds on these probabilities so as to analyze the optimum overflow thresholds. Then, by using these bounds, we determine the general formula of the optimum overflow thresholds in both of the first-order and second-order forms. Next, we consider another expression of the derived general formula so as to reveal the relationship with the optimum coding rate in the fixed-length source coding problem. Finally, we apply the general formula derived in this paper to the case of stationary memoryless sources.
Two-Layer Recommendation-Based Real Time Bidding (RTB)

Real-time bidding (RTB) has recently become the predominant technique in online advertising. Although, RTB is very effective, compared to classical approaches, a lot can be done to improve the accuracy of display advertising. In fact, a major drawback of existing RTB systems is the use of the bidding price and the current user profile as the primary features to display advertising. However, in doing so, the user may not always get the appropriate ad, and the same ad may be presented several times to the same user, which leads to its frustration. To overcome this limitation, we propose in this paper, an approach to serve the right ad to the right user at the right time. Our approach consists of incorporating the notion of recommender systems into the RTB architecture. Specifically, we design a two-layer approach. The first layer implements the item-based collaboratif filtering technique, while the second layer implements the factorization machines model. This allows to capture and use the information of other users (more specifically, those who are similar to the current user) to enhance the accuracy of display advertising. We show how these two layers collaborate to reach our goal, and validate our approach through an experimental study.
Power Grid Optimal Operation in Presence of Distributed Generation Resources with Constant and Variable Production

Fast growth of loads in power systems and the large gap between small and large periodic loads, has become the unit generation scheduling and unit commitment as a vital issue in the time horizons of operation. In the meantime, the presence of distributed generation units of various types and the increase in the participation rate of these units has become a critical issue in power systems analysis. In this paper, a new method for planning the constrained commitment of power plants production in networks based on wind farms and other renewable energy sources has been addressed in the form of a nonlinear optimization problem. In the way that, the problem of production commitment is solved in the main problem without the presence of wind power.
Sonodynamic Therapy: Advances and Challenges in Clinical Translation

Sonodynamic therapy (SDT) consists of the synergetic interaction between ultrasound and a chemical agent. In SDT, the cytotoxicity is triggered by ultrasonic stimuli, notably through cavitation. The unique features of SDT are relevant in the clinical context more than ever: the need for efficacy, accuracy, and safety while being noninvasive and preserving the patient's quality of life. However, despite the promising results of this technique, only a few clinical reports describe the use of SDT. The objective of this article is to provide an extensive overview of the clinical and preclinical research conducted in vivo on SDT, to identify the limitations, and to detail the developed strategies to overcome them.
Hip and Knee Kinetics During a Back Squat and Deadlift.

Choe, KH, Coburn, JW, Costa, PB, and Pamukoff, DN. Hip and knee kinetics during a back-squat and deadlift. J Strength Cond Res XX(X): 000-000, 2018-The back-squat and deadlift are performed to improve hip and knee extensor function. The purpose of this study was to compare lower extremity joint kinetics (peak net joint moments [NJMs] and positive joint work [PJW]) between the back-squat and deadlift. Twenty-eight resistance-trained subjects (17 men: 23.7 ± 4.3 years, 1.76 ± 0.09 m, 78.11 ± 10.91 kg; 11 women: 23.0 ± 1.9 years, 1.66 ± 0.06 m, 65.36 ± 7.84 kg) were recruited. One repetition maximum (1RM) testing and biomechanical analyses occurred on separate days. Three-dimensional biomechanics of the back-squat and deadlift were recorded at 70 and 85% 1RM for each exercise. The deadlift demonstrated larger hip extensor NJM than the back-squat {3.59 (95% confidence interval [CI]: 3.30-3.88) vs. 2.98 (95% CI: 2.72-3.23) Nm·kg, d = 0.81, p < 0.001}. However, the back-squat had a larger knee extensor NJM compared with the deadlift (2.14 [95% CI: 1.88-2.40] vs. 1.18 [95% CI: 0.99-1.37] Nm·kg, d = 1.44 p < 0.001). More knee PJW was performed during the back-squat compared with the deadlift (1.85 [95% CI: 1.60-2.09] vs. 0.46 [95% CI: 0.35-0.58] J·kg, d = 2.10, p < 0.001). However, there was more hip PJW during the deadlift compared with the back-squat (3.22 [95% CI: 2.97-3.47] vs. 2.37 [95% CI: 2.21-2.54] J·kg, d = 1.30, p < 0.001). Larger hip extensor NJM and PJW during the deadlift suggest that individuals targeting their hip extensors may yield greater benefit from the deadlift compared with the back-squat. However, larger knee extensor NJM and PJW during the back-squat suggest that individuals targeting their knee extensor muscles may benefit from incorporating the back-squat compared with the deadlift.
On the Progression of Situation Calculus Universal Theories with Constants

The progression of action theories is an important problem in knowledge representation. Progression is second-order definable and known to be first-order definable and effectively computable for restricted classes of theories. Motivated by the fact that universal theories with constants (UTCs) are expressive and natural theories whose satisfiability is decidable, in this paper we provide a thorough study of the progression of situation calculus UTCs. First, we prove that progression of a (possibly infinite) UTC is always first-order definable and results in a UTC. Though first-order definable, we show that the progression of a UTC may be infeasible, that is, it may result in an infinite UTC that is not equivalent to any finite set of first-order sentences. We then show that deciding whether there is a feasible progression of a UTC is undecidable. Moreover, we show that deciding whether a sentence (in an expressive fragment of first-order logic) is in the progression of a UTC is CONEXPTIME-complete, and that there exists a family of UTCs for which the size of every feasible progression grows exponentially. Finally, we discuss resolutionbased approaches to compute the progression of a UTC. This comprehensive analysis contributes to a better understanding of progression in action theories, both in terms of feasibility
Intelligent Fuzzy Controller for Human-Aware Robot Navigation

This paper presents a framework of mobile robot system for human-aware robot navigation. Recently, we have experienced advanced researches and developments on mobile service robots for cleaning and guide in public places. Autonomous robot navigation in dynamic environments where a robot is surrounded by a lot of pedestrians is one of the major challenges in intelligentrobot systems. In this study, we develop a fuzzy controller to realize a multi objective task planning in crowds. Furthermore, we show an experimental example and discuss the applicabilityof the proposed method from the experimental results.
How Do Accelerators Select Startups? Shifting Decision Criteria Across Stages

Competitive accelerators can help startups overcome resource constraints and the liability of newness, but they are also highly selective when choosing startups. However, little is known about how a top accelerator selects startups and what the decision criteria are in that process. Here, we analyze a unique dataset of the real profiles of the startups that applied to the first seed accelerator in Southeast Asia and the accelerator's decisions to uncover the selection process and latent criteria. We used a scoreboard of 30 criteria based on the real-win-worth (Is it real? Can it win? Is it worth doing? ) framework to compare the profiles of the selected versus the rejected startups. Our analyses revealed that accelerator managers’ implicit decision criteria shifted from eight real or win criteria in the initial screening of many startups to another four win or worth criteria in the final selection of a small number of startups. These critical criteria were further used to build regression models that predict screening and selection results. Understanding the shifting decision criteria may inform accelerator managers of their own subconscious preferences to improve the decision process, and as a result also increase entrepreneurs’ empathy toward accelerator managers to sharpen their applications.
Effects of Initial Alloy on Microstructure and Magnetic Properties During Hot Deformation of Nd–Fe–B HDDR Powder

The effect of an initial alloy on microstructure and magnetic properties of Nd–Fe–B hydrogenation–disproportionation–desorption–recombination (HDDR) magnets during hot-deformation process have been studied with strip-casting alloy of composition Nd12.5FebalCo0. 3Nb0.2B6. 4 which have different microstructures. HDDR powders were produced by HDDR process with strip-casting alloy after pre-annealing or no pre-annealing. Obtained HDDR powders were subjected to hot-pressing at 700 °C under 400 MPa in vacuum and subsequently to die-upsetting at 800 °C with deformation conditions such as strain rate of 0.01 s−1 and height reduction of 77%. The microstructure of the initial alloys showed a large difference depending on the presence or absence of pre-annealing. The coercivity of HDDR powders using alloy after pre-annealing and no pre-annealing was different about 2 kOe. In addition, the remanence of hot-deformed magnet using HDDR powder produced with no pre-annealing alloy was higher than that with pre-annealing alloy although the hot deformation was conducted under the same condition and the remanence of two kinds of starting HDDR powder was almost the same. It was confirmed that the microstructure and magnetic properties of hot-deformed magnet depend on the microstructure of the initial alloy.
Spatial Intelligence toward Trustworthy Vehicular IoT

Spatial challenges for the vehicular Internet of Things come from mobility, high density, sparse connectivity, and heterogeneity. In this article, we propose two techniques, namely decentralized moving edge and multi-tier multi-access edge clustering, to handle these challenges. The "vehicle as an edge" concept of the decentralized moving edge provides a more suitable solution to meet the throughput and latency performance requirements by conducting distributed communication, data caching, and computing tasks at vehicles. Multi-tier multi-access edge clustering generates different levels of clusters for more efficient integration of different types of access technologies including licensed/unlicensed long-range low-throughput communications and unlicensed short-range high-throughput communications. We employ fuzzy logic to jointly consider multiple inherently contradictory metrics and use Q-learning to achieve a self-evolving capability. Realistic computer simulations are conducted to show the advantage of the proposed protocols over alternatives, an
Fast Road Detection Methods on a Large Scale Dataset for Assisting Robot Navigation Using Kernel Principal Component Analysis and Deep Learning

A large database needs a heavy computation when the analysis is needed. The heavy computation leads to decrease the autonomous system performance. In our previous work, a complete vision based dirvable road detection method was proposed using Deep Belief Neural Network(DBNN). However, the previous method is unable to perform in real time for a large scale database. Due to solve this problem, in this paper, two fast drivable road detection approaches have been proposed using Kernel Principal Component Analysis-Deep Belief Neural Network (KPCA-DBNN) and Dimensionality Reduction Deep Belief Neural Network (DRDBNN) to reduce heavy computation for a large database. In the KPCA-DBNN, KPCA is used for dimensionality reduction and DBNN is used for classification. In the DRDBNN, two DBNNs are used. One DBNN is used for dimensionality reduction, and other DBNN is used for classification. The performance of the two approaches is demonstrated by the experimental results. From the experimental results, we see that the KPCA-DBNN and DRDBNN approaches reduce the processing time as compared to the conventional DBNN method. In addition, the results indicate that DRDBNN performed better than KPCA-DBNN in terms of detection accuracy on a large road database.
The role of geospatial hotspots in the spatial spread of tuberculosis in rural Ethiopia: a mathematical model

Geospatial tuberculosis (TB) hotspots are hubs of TB transmission both within and across community groups. We aimed to quantify the extent to which these hotspots account for the spatial spread of TB in a high-burden setting. We developed spatially coupled models to quantify the spread of TB from geographical hotspots to distant regions in rural Ethiopia. The population was divided into three ‘patches’ based on their proximity to transmission hotspots, namely hotspots, adjacent regions and remote regions. The models were fitted to 5-year notification data aggregated by the metapopulation structure. Model fitting was achieved with a Metropolis–Hastings algorithm using a Poisson likelihood to compare model-estimated notification rate with observed notification rates. A cross-coupled metapopulation model with assortative mixing by region closely fit to notification data as assessed by the deviance information criterion. We estimated 45 hotspot-to-adjacent regions transmission events and 2 hotspot-to-remote regions transmission events occurred for every 1000 hotspot-to-hotspot transmission events. Although the degree of spatial coupling was weak, the proportion of infections in the adjacent region that resulted from mixing with hotspots was high due to the high prevalence of TB cases in a hotspot region, with approximately 75% of infections attributable to hotspot contact. Our results suggest that the role of hotspots in the geospatial spread of TB in rural Ethiopia is limited, implying that TB transmission is primarily locally driven.
Study of Cascaded H-Bridge Inverter Switching on Small Scale PV System Under Partial Shading Condition

Various factors can cause nonuniform operation problem in photovoltaic (PV) systems such as partial shading (nonuniform insolation), nonuniform ageing, unequal material quality, dust, etc. Nonuniform illuminations are common occurences be faced by PV systems. These cause disproportionate impact on PV system performance. This paper studies switching angles determination of cascaded H-bridge (CHB) inverter under nonuniform conditions to obtain low total harmonic distortions (THDs). So that, no need a bulky filter to meet grid interconnection requirements of PV system integration. The proposed method exploits nonuniform PV voltages by composing voltage levels to produce output voltage waveforms nearly sinusoidal. To realize this is applied CHB inverter topology, selective harmonics elimination (SHE), and Newton Raphson iteration method. By applying generated switching angles, the simulations show the generated output voltages are nearly sinusoidal which characterized by a desired rms fundamental voltages satisfaction, and low THDs as well. To verify the simulations results, the laboratory experimentals were carried out and presented in this paper.
An Image Compression-Encryption Algorithm Based on Cellular Neural Network and Compressive Sensing

In this paper, an image encryption algorithm on the basis of cellular neural networks (CNN) and compressive sensing (CS) is proposed. Firstly, four CNN with hyper chaotic behavior is introduced to generate chaotic sequence. Then, the index of the sorted chaotic sequence is used to control the generation of measurement matrix in CS procedure. Moreover, Lissajous map is served to produce asymptotic deterministic random measurement matrix instead of the common random measurement matrix. In addition, the chaotic sequence is normalized to 8-bit integer to diffuse the result after applying CS operation on the plain image, and the image after compression and encryption is obtained. The simulation results and analysis verify the proposed algorithm owns good security and ideal performance.
Recognition of facial expression and identity in part reflects a common ability, independent of general intelligence and visual short-term memory

ABSTRACT Recognising identity and emotion conveyed by the face is important for successful social interactions and has thus been the focus of considerable research. Debate has surrounded the extent to which the mechanisms underpinning face emotion and face identity recognition are distinct or share common processes. Here we use an individual differences approach to address this issue. In a well-powered (N = 605) and age-diverse sample we used structural equation modelling to assess the association between face emotion recognition and face identity recognition ability. We also sought to assess whether this association (if present) reflected visual short-term memory and/or general intelligence (g). We observed a strong positive correlation (r = .52) between face emotion recognition ability and face identity recognition ability. This association was reduced in magnitude but still moderate in size (r = .28) and highly significant when controlling for measures of g and visual short-term memory. These results indicate that face emotion and face identity recognition abilities in part share a common processing mechanism. We suggest that face processing ability involves multiple functional components and that modelling the sources of individual differences can offer an important perspective on the relationship between these components.
Liver Transplantation Trends and Outcomes for Hereditary Hemorrhagic Telangiectasia in the United States.

BACKGROUND Liver arteriovenous malformations (AVM) in hereditary hemorrhagic telangiectasia (HHT) can necessitate liver transplantation. There is limited data on HHT patients undergoing liver transplantation (LT) in the United States. METHODS Two sources of data were used: (1) Scientific Registry of Transplant Recipients (SRTR) database (1998-2016) (2) Single center liver transplant database (Mayo Clinic Rochester, MN). The aims of this study were (1) to determine trends in LT for HHT-related liver involvement in the United States using the SRTR database; (2) to identify clinical characteristics, indications, and outcomes for LT in HHT. RESULTS Thirty-nine HHT patients were listed for LT in the SRTR database from 1998-2016 to 1998-2001 (n = 1); 2002-2005 (n = 4); 2006-2010 (n = 10), and 2011-2016 (n = 24). Twenty-four underwent LT at a median age of 47.5 years (interquartile range, 37.0-58.5 years). Median calculated MELD score at time of LT was 8.0 (interquartile range, 7.0-9.5), and 75% received an exception MELD score. Two status-1 patients died during transplant surgery. Nineteen (86%) patients were alive after a median post-LT follow-up of 48 months, whereas 2 patients were lost to follow-up. Five of the aforementioned HHT patients underwent LT at Mayo Clinic, 4 with high output cardiac failure, and 1 with biliary ischemia. All 5 were alive at the time of last follow-up with good graft function and resolution of heart failure. CONCLUSIONS Outcomes after LT for HHT patients are excellent with 86% survival after a median follow-up of 48 months and resolution of heart failure. LT listing for HHT has increased in substantially in more recent eras.
A Unique Nonsaccharide Mimetic of Heparin Hexasaccharide Inhibits Colon Cancer Stem Cells via p38 MAP Kinase Activation

Targeting of cancer stem cells (CSC) is expected to be a paradigm-shifting approach for the treatment of cancers. Cell surface proteoglycans bearing sulfated glycosaminoglycan (GAG) chains are known to play a critical role in the regulation of stem cell fate. Here, we show for the first time that G2.2, a sulfated nonsaccharide GAG mimetic (NSGM) of heparin hexasaccharide, selectively inhibits colonic CSCs in vivo. G2.2-reduced CSCs (CD133+/CXCR4+, Dual hi) induced HT-29 and HCT 116 colon xenografts’ growth in a dose-dependent fashion. G2.2 also significantly delayed the growth of colon xenograft further enriched in CSCs following oxaliplatin and 5-fluorouracil treatment compared with vehicle-treated xenograft controls. In fact, G2.2 robustly inhibited CSCs’ abundance (measured by levels of CSC markers, e.g., CD133, DCMLK1, LGR5, and LRIG1) and self-renewal (quaternary spheroids) in colon cancer xenografts. Intriguingly, G2.2 selectively induced apoptosis in the Dual hi CSCs in vivo eluding to its CSC targeting effects. More importantly, G2.2 displayed none to minimal toxicity as observed through morphologic and biochemical studies of vital organ functions, blood coagulation profile, and ex vivo analyses of normal intestinal (and bone marrow) progenitor cell growth. Through extensive in vitro, in vivo, and ex vivo mechanistic studies, we showed that G2.2′s inhibition of CSC self-renewal was mediated through activation of p38α, uncovering important signaling that can be targeted to deplete CSCs selectively while minimizing host toxicity. Hence, G2.2 represents a first-in-class (NSGM) anticancer agent to reduce colorectal CSCs.