Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Condition to Create Femoral Tunnel Considering Combined Influence of Knee Flexion and Transverse Drill Angle in Anatomical Single-Bundle ACL Reconstruction Using Medial Portal Technique: 3D Simulation Study

There has been no previous study using three-dimensional (3D) measurement on femoral tunnel characteristics according to the combined influence of various flexion angles of knee and transverse drill angles in single-bundle ACL reconstruction with transportal technique. The purpose of this study was to determine optimal condition of knee flexion angle and transverse drill angle to create secure femoral tunnel in single-bundle ACL reconstruction with transportal technique considering tunnel length, tunnel wall breakage, and graft bending angle. This study was conducted using simulation of 3D computed tomography of thirty subjects. Three variables of femoral tunnel changed according to combined influence of four flexion angles of knee and three transverse drill angles were measured: tunnel length, wall breakage, and graft bending angle. There was no case of short femoral tunnel less than 25 mm at 120° and 130° of flexion. There was no case of breakage of femoral tunnel at 120° of flexion with maximum transverse drill angle (MTA) and MTA-10° and at 130° of flexion. Considering effect on graft bending angle, decrease of flexion angle and transverse drill angle could be appropriate in creating femoral tunnel. Increased flexion angle and transverse drill angle secured femoral tunnel having sufficiently long length without wall breakage. However, avoiding excessive flexion angle and maximum transverse drill angle could be recommended because they tended to cause more acute graft bending angle.
A new insight into the evolution and functional divergence of FRK genes in Pyrus bretschneideri

In plants, plant fructokinases (FRKs) are considered to be the main gateway of fructose metabolism as they can phosphorylate fructose to fructose-6-phosphate. Chinese white pears (Pyrus bretschneideri) are one of the popular fruits in the world market; sugar content is an important factor affecting the quality of the fruit. We identified 49 FRKs from four Rosaceae species; 20 of these sequences were from Chinese white pear. Subsequently, phylogenic relationship, gene structure and micro-collinearity were analysed. Phylogenetic and exon–intron analysis classified these FRKs into 10 subfamilies, and it was aimed to further reveal the variation of the gene structure and the evolutionary relationship of this gene family. Remarkably, gene expression patterns in different tissues or different development stages of the pear fruit suggested functional redundancy for PbFRKs derived from segmental duplication or genome-wide duplication and sub-functionalization for some of them. Additionally, PbFRK11, PbFRK13 and PbFRK16 were found to play important roles in regulating the sugar content in the fruit. Overall, this study provided important insights into the evolution of the FRK gene family in four Rosaceae species, and highlighted its roles in both pear tissue and fruits. Results presented here provide the appropriate candidate of PbFRKs that might contribute to fructose efflux in the pear fruit.
BlockStore: A Secure Decentralized Storage Framework on Blockchain

In order to ensure faster audits, higher transparency and security, many applications are being designed using blockchains. We propose BlockStore, a secure decentralized storage framework using blockchain technology. The primary motivation is efficient utilization of storage resources of users. Users often have un-utilized or underutilized storage in their devices. They can choose to host their storage resources when they are not in use. Users rent storage from the host for a fee for a fixed period of time and release back after the time expires. BlockStore keeps track of un-utilized storage of hosts in Space Wallet, a structure that helps in assigning storage to renters on request. The ownership of storage can be proved by logging all storage transactions in a public ledger (the blockchain), which can be verified by any user. A host cannot host the same storage to two users at the same time, nor can it tamper with the data of the renter. Renters cannot frame a host of cheating. BlockStore uses proofs of storage and data possession to verify that the hosts do not tamper with data and penalizes parties for misbehavior. Users can encrypt data for privacy. Payment and penalty are handled using smart contracts. BlockStore differs from existing solutions, by providing stronger audit that detects and penalizes misbehaving parties earlier than existing schemes.
Adversarial Neural Networks for Cross-lingual Sequence Tagging

We study cross-lingual sequence tagging with little or no labeled data in the target language. Adversarial training has previously been shown to be effective for training cross-lingual sentence classifiers. However, it is not clear if language-agnostic representations enforced by an adversarial language discriminator will also enable effective transfer for token-level prediction tasks. Therefore, we experiment with different types of adversarial training on two tasks: dependency parsing and sentence compression. We show that adversarial training consistently leads to improved cross-lingual performance on each task compared to a conventionally trained baseline.
DFT Based Beam-Time Delay Sparse Channel Representation for Channel State Information (CSI) Compression in 5G FDD Massive MIMO Systems

Massive MIMO is currently a very hot topic for mobile R&D industry due to its large capabilities of high throughput and enhanced coverage for multiple users simultaneously. In the following article a proposal for dealing with the Channel State Information (CSI) overhead for FDD MIMO systems is presented. The idea consists of using a Multidimensional Discrete Fourier Transform (DFT) compression, which exploits the frequency and spatial correlations in order to provide a fully sparse Beam-Time Delay Channel Representation. The proposed idea provides high scalability in a long range of channel compression regimes while presents low computational complexity. Simulation results are presented showing the feasibility of implementation for Full dimensional massive MIMO systems.
Cavitating flow-induced unsteady pressure pulsations in a low specific speed centrifugal pump

With the development of cavitation, the high-energy pressure wave from a cavitation bubble collapsing is detrimental to the stable operation of centrifugal pumps. The present paper concentrates on pressure pulsations under cavitation conditions, and pressure amplitudes at the blade-passing frequency (fBPF) and RMS values in the 0–500 Hz frequency band are combined to investigate cavitation-induced pressure pulsations. The results show that components at fBPF always dominate the pressure spectrum even at the full cavitation stage. For points P1–P7 on the volute side wall, with a decreasing cavitation number, the pressure energy first remains unchanged and then it rises rapidly after the critical point. For point In1 in a volute suction pipe located close to the cavitation region, the pressure energy changes slightly at high cavitation numbers; then for a particular cavitation number range, the pressure energy decreases, and finally increases again. For different flow rates, the pressure energy at the critical point is much lower than the initial amplitude at the non-cavitation condition for In1. This demonstrates that the cavitation cloud in the typical stage is partially compressible, and the emitted pressure wave from a collapsing cavitation bubble is absorbed and attenuated significantly. Finally, this leads to the pressure energy decreasing rapidly for the measuring point In1 near the cavitation region.
Performance Analysis, Comparison, and Optimization of Interweave and Underlay Spectrum Access in Cognitive Radio Networks

Cognitive radio networks (CRNs) have been proposed to exploit licensed bands opportunistically, with secondary users’ (SU) activity being subordinated to primary users (PU). The two most popular types of spectrum access in the CRN are interweave and underlay. There is no clear consensus which one provides better results, for different metrics, despite the fact that there has been a lot of research dedicated to each mode. In this paper, we analyze this problem theoretically, providing formulas that are in closed form. These expressions allow the performance comparison of interweave and underlay modes under a unified network setup. Our focus are two metrics, throughput and delay, which we analyze relying on the renewal-reward theory and queueing theory, respectively. These results enable an SU to decide what mode to use depending on what the optimization objective is, given the key network parameters. Furthermore, relying on the results of our analysis, we propose hybrid policies, which are dynamic, and allow the SU to switch between the two modes at any point. These policies offer an additional performance improvement of up to 50%. We validate our results with extensive realistic simulations.
On Sensing Nitro-Group Containing Compounds Using Thin Planar Arrays of Titanium Dioxide Nanowires

Previous laboratory results have suggested that unheated TiO<sub>2</sub> nanowire arrays provide a means to detect trace level concentrations of nitro-group containing compounds in the gas phase with fast response and high chemical selectivity. The detection method is based on the chemiresistive effect, where an electron-deficient compound adsorbed to the surface of an n-type semiconductor causes a surface charge deficit on the semiconductor leading to an increase in its resistance. However, a major issue with this method is that chemiresistive sensors based on TiO<sub>2</sub> are also sensitive to the presence of water vapor, and this cross-sensitivity could lead to artifacts for sensors used under environmental conditions. Results are presented here, where thin planar arrays of TiO<sub>2</sub> nanowires were tested to determine the sensitivity towards water vapor, along with the detection limits and response times towards several nitro-containing organic molecules as a function of gas-phase water vapor concentration. When water vapor concentrations were carefully controlled to ensure they remain constant throughout the testing cycle, it was found that TiO<sub>2</sub> nanowire devices could detect 2,4,6-trinitrotoluene at a concentration as low as a few tens of part per trillion by volume with a response time in the range of <inline-formula> <tex-math notation="LaTeX">$10^{2}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$10^{3}$ </tex-math></inline-formula> s, depending on experimental conditions.
Small Sample Learning in Big Data Era

As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. In this paper, we aim to present a survey to comprehensively introduce the current techniques proposed on this topic. Specifically, current SSL techniques can be mainly divided into two categories. The first category of SSL approaches can be called "concept learning", which emphasizes learning new concepts from only few related observations. The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis. The second category is called "experience learning", which usually co-exists with the large sample learning manner of conventional machine learning. This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures. More extensive surveys on both categories of SSL techniques are introduced and some neuroscience evidences are provided to clarify the rationality of the entire SSL regime, and the relationship with human learning process. Some discussions on the main challenges and possible future research directions along this line are also presented.
Analyzing Inverse Problems with Invertible Neural Networks

In many tasks, in particular in natural science, the goal is to determine hidden system parameters from a set of measurements. Often, the forward process from parameter- to measurement-space is a well-defined function, whereas the inverse problem is ambiguous: one measurement may map to multiple different sets of parameters. In this setting, the posterior parameter distribution, conditioned on an input measurement, has to be determined. We argue that a particular class of neural networks is well suited for this task -- so-called Invertible Neural Networks (INNs). Although INNs are not new, they have, so far, received little attention in literature. While classical neural networks attempt to solve the ambiguous inverse problem directly, INNs are able to learn it jointly with the well-defined forward process, using additional latent output variables to capture the information otherwise lost. Given a specific measurement and sampled latent variables, the inverse pass of the INN provides a full distribution over parameter space. We verify experimentally, on artificial data and real-world problems from astrophysics and medicine, that INNs are a powerful analysis tool to find multi-modalities in parameter space, to uncover parameter correlations, and to identify unrecoverable parameters.
A Linear-Mode LiDAR Sensor Using a Multi-Channel CMOS Transimpedance Amplifier Array

This paper presents a linear-mode optical sensor for the feasible applications of unmanned vehicle LiDAR systems, in which a pulsed-erbium fiber laser is exploited as a light source and a 16-channel transimpedance amplifier (TIA) array is utilized in an optical Rx module with low-cost InGaAs PIN photodiodes. In particular, a voltage-mode CMOS feedforward (VCF-TIA) is newly proposed to achieve twice higher transimpedance gain with lower noise and similar bandwidth characteristics than a conventional inverter TIA, thereby enabling longer detection. Test chips of the 16-channel VCF-TIA array realized in a standard 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process demonstrate 76.3-dB<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> transimpedance gain, 6.3-pA/sqrt(Hz) average noise current spectral density, less than −33-dB crosstalk between channels, and 29.8-mW power dissipation per channel from a single 1.8-V supply. Automatic gain control is also equipped to extend input dynamic range for near-range detection. Hence, the proposed linear-mode optical sensor clearly detects the reflected optical pulses from the target of 5% reflection rate within the range of 0.5–25 m.
Interactive Multiobjective Optimization: A Review of the State-of-the-Art

Interactive multiobjective optimization (IMO) aims at finding the most preferred solution of a decision maker with the guidance of his/her preferences which are provided progressively. During the process, the decision maker can adjust his/her preferences and explore only interested regions of the search space. In recent decades, IMO has gradually become a common interest of two distinct communities, namely, the multiple criteria decision making (MCDM) and the evolutionary multiobjective optimization (EMO). The IMO methods developed by the MCDM community usually use the mathematical programming methodology to search for a single preferred Pareto optimal solution, while those which are rooted in EMO often employ evolutionary algorithms to generate a representative set of solutions in the decision maker’s preferred region. This paper aims to give a review of IMO research from both MCDM and EMO perspectives. Taking into account four classification criteria including the interaction pattern, preference information, preference model, and search engine (i.e., optimization algorithm), a taxonomy is established to identify important IMO factors and differentiate various IMO methods. According to the taxonomy, state-of-the-art IMO methods are categorized and reviewed and the design ideas behind them are summarized. A collection of important issues, e.g., the burdens, cognitive biases and preference inconsistency of decision makers, and the performance measures and metrics for evaluating IMO methods, are highlighted and discussed. Several promising directions worthy of future research are also presented.
An Integrated SRM Drive with Constant Current Constant Voltage Charging Capability for Electric Vehicle Application

Multiport converter based integrated AC charging for electric vehicle (EV) batteries is investigated in this paper. An EV with a Switched Reluctance (SR) drive is considered and the proposed control scheme achieves constant current and constant voltage (CCCV) charging of the on-board battery with the charging integrated SRM Drive. In addition, unity power factor on the AC side is achieved without using any additional circuitry. Operation and control design is presented in detail and simulations are shown to verify the control strategy and assess the performance of proposed scheme.
An Emotion Oriented Topic Modeling Approach to Discover What Students are Concerned about in Course Forums

Course forums offer an interactive channel for learners to express opinions and feedback, which contain valuable emotions and topic information towards courses. In this paper, we propose an emotion oriented topic probabilistic model that can be used to calculate distributions of emotion-topic over words to discover what students are most concerned about. An experiment on real-life data indicates that students had a positive attitude for knowledge applications, a negative experience for the learning system, and expressed confusion about the final exam. We also visualize the temporal trends of emotions of the whole group and the groups with different levels of achievement. The proposed model has a potential in discovering students' emotions in their feedback, thus improving the online learning experience, and identifying at-risk students timely.
The Critical Role of Chemokine (C–C Motif) Receptor 2-Positive Monocytes in Autoimmune Cholangitis

The therapy of primary biliary cholangitis (PBC) has lagged behind other autoimmune diseases despite significant improvements in our understanding of both immunological and molecular events that lead to loss of tolerance to the E2 component of pyruvate dehydrogenase, the immunodominant autoepitope of PBC. It is well known that Ly6Chi monocytes are innate immune cells infiltrating inflammatory sites that are dependent on the expression of C–C motif chemokine receptor 2 (CCR2) for emigration from bone marrow. Importantly, humans with PBC have a circulating monocyte pro-inflammatory phenotype with macrophage accumulation in portal tracts. We have taken advantage of an inducible chemical xenobiotic model of PBC and recapitulated the massive infiltration of monocytes to portal areas. To determine the clinical significance, we immunized both CCR2-deficient mice and controls with 2OA-BSA and noted that CCR2 deficiency is protective for the development of autoimmune cholangitis. Importantly, because of the therapeutic potential, we focused on inhibiting monocyte infiltration through the use of cenicriviroc (CVC), a dual chemokine receptor CCR2/CCR5 antagonist shown to be safe in human trials. Importantly, treatment with CVC resulted in amelioration of all aspects of disease severity including serum total bile acids, histological severity score, and fibrosis stage. In conclusion, our results indicate a major role for Ly6Chi monocytes and for CCR2 in PBC pathogenesis and suggest that inhibition of this axis by CVC should be explored in humans through the use of clinical trials.
Multisensory stimulation modulates perceptual and post perceptual face representations: Evidence from event‐related potentials

Seeing a face being touched in spatial and temporal synchrony with the own face produces a bias in self‐recognition, whereby the other face becomes more likely to be perceived as the self. The present study employed event‐related potentials to explore whether this enfacement effect reflects initial face encoding, enhanced distinctiveness of the enfaced face, modified self‐identity representations, or even later processing stages that are associated with the emotional processing of faces. Participants were stroked in synchrony or asynchrony with an unfamiliar face they observed on a monitor in front of them, in a situation approximating a mirror image. Subsequently, event‐related potentials were recorded during the presentation of (a) a previously synchronously stimulated face, (b) an asynchronously stimulated face, (c) observers’ own face, (d) filler faces, and (e) a to‐be‐detected target face, which required a response. Observers reported a consistent enfacement illusion after synchronous stimulation. Importantly, the synchronously stimulated face elicited more prominent N170 and P200 responses than the asynchronously stimulated face. By contrast, similar N250 and P300 responses were observed in these conditions. These results suggest that enfacement modulates early neural correlates of face encoding and facial prototypicality, rather than identity self‐representations and associated emotional processes.
Comparison of Radiofrequency Ablation and Cryoablation for the Recovery of Atrial Contractility and Survival

Background Limited comparative data are available on the efficacy of cryoablation versus radiofrequency ablation in patients with atrial fibrillation. This study aimed to compare radiofrequency ablation and cryoablation with regard to clinical outcomes and the restoration of sinus rhythm or atrial contractility. Methods A total of 239 patients who underwent surgical ablation between August 2003 and December 2016 at our institution were included. The patients were divided into 2 groups according to the energy device that was used (group A: n=140, radiofrequency ablator; group B: n=99, cryoablator). Echocardiographic data, overall survival, and major cardiovascular and cerebrovascular event (MACCE)-free survival were compared between the 2 groups. Results At 1 year of follow-up, the atrial contractility recovery rate was 32.2% (19 of 59) in group A and 48.8% (21 of 44) in group B. In addition, cryoablation was found to be a predictive factor for the recovery of atrial contractility (cryoablation vs. radiofrequency ablation: odds ratio, 2.540; 95% confidence interval, 1.063–6.071; p=0.036). The left ventricular ejection fraction was significantly higher in group B (53.1%±11.5% vs. 59.1%±6.3%, p=0.001). The median follow-up duration was 36 months. The 5-year overall survival rate was 80.1%±3.6% in group A and 92.1%±2.9% in group B (p=0.400). The 5-year MACCE-free survival rate was 70.3%±4.0% in group A and 70.9%±5.6% in group B (p=0.818). Conclusion Cryoablation was associated with a higher atrial contractility restoration rate and better left ventricular function than radiofrequency ablation. However, no significant relationship was observed between the energy source and overall or MACCE-free survival.