Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Multi-Kernel Learning with Orthogonal Random Features

Kernel-based methods have well-appreciated performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. To cope with this limitation, multi-kernel learning has gained popularity thanks to its flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops an online multi-kernel learning scheme to infer the intended nonlinear function ‘on the fly. ’ Performance analysis shows that the novel algorithm can afford sublinear regret. Numerical tests on real datasets are carried out to showcase the effectiveness of the proposed algorithms.
Backlight Compensation Algorithms to Improve Power Consumption in LED- LCD Displays

In this paper, three types of algorithms for LED-TFT -LCD displays' content-aware management are presented. Their aim is to minimize the power consumption of the displays, while simultaneously preserving the quality of the displayed content, utilizing global backlight dimming. The first category of algorithms is developed based on proper camera measurements, while the second is developed via lightmeter measurements. The third category is formed by combining the strengths of the previous two. In contrast to previous approach, these algorithms take into account the different contribution of basic colors to the overall image illuminance and adjust accordingly to accomplish greater resemblance between the original and the processed image. The algorithms were tested assuming various pictures with different color patterns. Our experimental results reveal that a 30% power reduction can be achieved with less than 10% average image distortion.
Non-coding RNA in drug resistance of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) has been one of the most highly lethal cancers. The acquisition of drug resistance accounts for the majority of poor effects of chemotherapy in HCC. Non-coding RNAs (ncRNAs) including miRNAs, long ncRNAs (lncRNAs), and circular RNA (circRNA) have been well-documented to participate in cancer occurrence and progression. Recently, multiple studies have highlighted the key roles of ncRNAs in chemoresistance of HCC. In addition, accumulating evidence has demonstrated that they can serve as biomarkers in diagnosis, treatment, and prognosis of HCC. In this review, we first overviewed up-to-date findings regarding miRNA and lncRNA in drug resistance of HCC, then summarized specific mechanisms that they modulate chemoresistance of HCC, and finally discussed their potential clinical application in overcoming the obstacle of HCC chemoresistance in the future.
Generating Sound Words from Audio Signals of Acoustic Events with Sequence-to-Sequence Model

Representing various sounds in language, such as sound words, or onomatopoeias, is not only useful as an auxiliary means for automatic speech recognition, but also essential in emerging fields such as natural human-machine communication, searching audio archives for acoustic events, and abnormality detection based on sounds. This paper proposes a novel method for sound word generation from audio signals. The method is based on an end-to-end, sequence-to-sequence framework to solve the audio segmentation problem to find an appropriate segment of audio signals along time that corresponds to a sequence of phonemes, and the ambiguity problem, where multiple words may correspond to the same sound, depending on the situations or listeners. Our tests show that the method worked efficiently and achieved a 2.8% mean phoneme error rate (MPER) and a 7.2% word error rate (WER) in a sound word generation task.
Robust Adaptive Bilateral Control of Teleoperation Systems with Uncertain Parameters and Unmodeled Dynamics

Kinematic and dynamic parameters of robot manipulators are difficult to measure exactly. Also, time varying unknown dynamical parameters of human arm, during interaction with the master robot and unknown parameters of environment during interaction with the slave robot, in teleoperation systems, insert further uncertainties. Furthermore, unknown parameters, unmodeled dynamics of master/slave robots, human arm model and environment introduce more uncertainties. In this paper, a robust adaptive master-slave teleoperation control strategy is introduced which require neither the exact knowledge about the parameters of the master/slave robots, human arm and environment, because of the adaptability with the unknown parameters, nor the exact dynamical equation of master/slave Falcon robot, because of the robustness against the unmodeled dynamics. Two Novint Falcon robots are used as master/slave robots and due to having the highly nonlinear complexity of these robots, they are considered as a single translational equivalent mechanism with known inertia, damping and stiffness coefficient and an unmodeled dynamic term because of this approximation. The human arm and environment are modeled as a 1-DoF mass, spring and damper model (MSD) with unknown coefficients. Moreover, an additional nonlinear spring and nonlinear damper has been used for better approximation of nonlinear property of the human arm and the environment. A Lyapunov function is introduced for stability and the position tracking convergence proof of the entire teleoperation system. The validity of the theory is confirmed by simulations.
A Data Analytics Framework for Aggregate Data Analysis

In many contexts, we have access to aggregate data, but individual level data is unavailable. For example, medical studies sometimes report only aggregate statistics about disease prevalence because of privacy concerns. Even so, many a time it is desirable, and in fact could be necessary to infer individual level characteristics from aggregate data. For instance, other researchers who want to perform more detailed analysis of disease characteristics would require individual level data. Similar challenges arise in other fields too including politics, and marketing. In this paper, we present an end-to-end pipeline for processing of aggregate data to derive individual level statistics, and then using the inferred data to train machine learning models to answer questions of interest. We describe a novel algorithm for reconstructing fine-grained data from summary statistics. This step will create multiple candidate datasets which will form the input to the machine learning models. The advantage of the highly parallel architecture we propose is that uncertainty in the generated fine-grained data will be compensated by the use of multiple candidate fine-grained datasets. Consequently, the answers derived from the machine learning models will be more valid and usable. We validate our approach using data from a challenging medical problem called Acute Traumatic Coagulopathy.
Do Patients with Second Primary Colorectal Cancer Hold the Similar Prognosis and Therapeutic Benefits as Those with Initial Primary Colorectal Cancer?

Aim The objective is to compare the differences on prognosis and the therapeutic benefits between initial and second primary colorectal cancer (pCRC). Methods A dataset containing 377,271 initial pCRC cases and 18,617 second pCRC cases from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) 1988–2015 was evaluated. Survival comparisons were made using the log-rank test. Cox proportional hazards models were used to assess the survival benefits. Results The cancer-specific survival rate of patients with initial pCRC was significantly higher than that of patients with second pCRC (5-years survival rate: 64.85% vs. 60.22%, P<0.001). The Chi-square of stratified log rank for age at diagnosis was lower than that for primary site, pTNM stage, sex, race, histology, and grade (Chi-square=86.73). There were almost no differences on therapeutic benefits between patients with initial and second pCRC except that treatments with chemotherapy were significantly associated with longer survival rate compared with treatments without chemotherapy among stage III surgical initial and second primary left-sided colon cancers patients (HR=0.764 vs. 0.581; P for interaction =0.008). Conclusion Patients with second pCRC have worse prognosis than those with initial pCRC primarily because of older age in the former group. The results evidenced that the therapeutic benefits on the prognosis for colorectal cancer were generally similar between patients with initial and second pCRC.
Evolving SDN for Low-Power IoT Networks

Software Defined Networking (SDN) offers a flexible and scalable architecture that abstracts decision making away from individual devices and provides a programmable network platform. Low-power wireless Internet of Things (IoT) networks, where multi-tenant and multi-application architectures require scalable and configurable solutions, are ideally placed to capitalize on this research. However, implementing a centralized SDN architecture within the constraints of a low-power wireless network faces considerable challenges. Not only is controller traffic subject to jitter due to unreliable links and network contention, but the overhead generated by SDN can severely affect the performance of other traffic. This paper addresses the challenge of bringing high-overhead SDN architecture to IEEE 802.15. 4 networks. We explore how the traditional view of SDN needs to evolve in order to overcome the constraints of low-power wireless networks, and discuss protocol and architectural optimizations necessary to reduce SDN control overhead - the main barrier to successful implementation. Additionally, we argue that interoperability with the existing protocol stack is necessary to provide a platform for controller discovery, and coexistence with legacy networks. We consequently introduce $\mu \text{SDN}$, a lightweight SDN framework for Contiki OS with both IPv6 and underlying routing protocol interoperability, as well as optimizing a number of elements within the SDN architecture to reduce control overhead to practical levels. We evaluate $\mu \text{SDN}$ in terms of latency, energy, and packet delivery. Through this evaluation we show how the cost of SDN control overhead (both bootstrapping and management) can be reduced to a point where comparable performance and scalability is achieved against an IEEE 802.15. 4-2012 RPL-based network. Additionally, we demonstrate $\mu \text{SDN}$ through simulation: providing a use-case where the SDN configurability can be used to provide Quality of Service (QoS) for critical network flows experiencing interference, and we achieve considerable reductions in delay and jitter in comparison to a scenario without SDN.
Evaluation of Nasal Inlet Ports Having Simplified Geometry for the Pharmacopeial Assessment of Mass Fraction of Dose Likely to Penetrate Beyond the Nasopharynx: a Preliminary Investigation

Nasal cavity breakthrough to the airways of the lungs is associated with nasally inhaled droplets whose size is smaller than ca. 10 μm aerodynamic diameter that behave as an aerosol rather than a spray in terms of their transport. The purpose of the present laboratory-based study was to evaluate a nasal product quality control procedure involving a new inlet for the quantification of mass of such droplets emitted by commercially available aqueous nasal spray pump products by cascade impactor. This inlet is more representative of the adult nasal vestibule in terms of entry angle for the spray as well as internal volume for plume expansion. Sampling was also undertaken via a spherical 1-L glass expansion vessel as inlet, previously established for quantification of these aerosol droplets. The selected solution- and suspension-formulated products containing azelastine and fluticasone propionate respectively were shown to contain < 1% of the total spray mass per actuation associated with droplets < 14.1 μm aerodynamic diameter. These measurements were consistent with laser diffraction-based measurements of the entire droplet size distribution. Comparable measures of aerosol droplet mass fraction were obtained when the spray was sampled by the cascade impactor method using either the 1-L glass expansion chamber or the new metal inlet as entry for the spray produced by either product evaluated. We conclude that the metal inlet has the potential to be adopted as a suitable induction port in the assessment of nasal product quality, where currently no standardized inlet exists.
All‐Cause and Cause‐Specific Mortality Trends of End‐Stage Renal Disease Due to Lupus Nephritis From 1995 to 2014

A prior study showed that premature mortality among patients with end‐stage renal disease (ESRD) due to lupus nephritis (LN) persisted in the US between 1995 and 2006. The present study was undertaken to extend this analysis through 2014 to examine more recent trends, including key cause‐specific mortality trends.
Calibrating Cameras in Poor-Conditioned Pitch-Based Sports Games

Camera calibration is a preliminary step in sports analytics which enables us to transform player positions to standard playing area coordinates. While many camera calibration systems work well when the visual content contains sufficient clues, such as a key frame, calibrating without such information, such as may be needed when processing footage captured by a coach from the sidelines or stands, is challenging. In this paper an innovative automatic camera calibration system, which does not make use of any key frames, is presented for sports analytics. The proposed system consists of three components: a robust linear panorama module, a playing area estimation module, and a homography estimation module. It can eliminate distortion and calibrate the camera in each frame simultaneously, using correspondences between pairs of consecutive frames. Experiments on real data evaluate the performance and demonstrate the robustness of the system.
Analysis of Slow Wave Modes in Modified Photonic Crystal Waveguides Using the MPB Package

Modified photonic crystal waveguides with additional layers on the boundaries of hollow core are investigated. Dispersion diagrams and spatial distributions of the longitudinal electric field component are calculated. It is shown that modification of the photonic crystal waveguide results in changing of slow wave electric field cross section in the waveguide channel. Field intensity maxima of the slow wave even modes are formed not only on hollow core boundaries but also in the center of waveguide channel. Thus modified photonic crystal waveguides with hollow core can be used as novel slow-wave systems for vacuum electron devices with linear electron beams especially in terahertz frequency range.
Inverse Reinforcement Learning Approach for Elicitation of Preferences in Multi-objective Sequential Optimization

It is crucial to know which criterion should be focused on, in a multi-objective decision making context, to select the best alternative from the multiple Pareto optimal solutions. However, in general, it is hard for the decision maker to express his/her own preference order for each criterion. In this study, we propose a preference elicitation method to estimate relative importance in terms of weights for each criterion by observing his/her processes of decision making. This method would make expert's preference elicited, and contribute at an important decision making point, such as urban planning,
The contributions of ankle, knee and hip joint work to individual leg work change during uphill and downhill walking over a range of speeds

The muscles surrounding the ankle, knee and hip joints provide 42, 16 and 42%, respectively, of the total leg positive power required to walk on level ground at various speeds. However, each joint's contribution to leg work when walking up/downhill at a range of speeds is not known. Determining each biological joint's contribution to leg work over a range of speeds and slopes can inform the design of biomimetic assistive devices (i.e. prostheses). Twenty healthy adults walked 1.00, 1.25 and 1.50 m s−1 on 0°, ±3°, ±6° and ±9° while we collected kinematic and kinetic data. We calculated sagittal plane joint work and individual leg work over the entire stance phase. The ratio of ankle joint to total individual leg positive work (summed ankle, knee and hip joint work) did not change (0.42) with speed or slope, but the ratio of ankle joint to individual leg negative work was 0.38 at −9°, 0.42 at 0° and 0.27 at +9° across all speeds. The ratio of ankle joint to total individual leg negative work was 0.41 at 1.00 m s−1 and 0.32 at 1.50 m s−1 across all slopes. The ratio of knee joint to total individual positive leg work (0.22) did not change with speed or slope. The ratio of knee joint to total individual leg negative work was 0.39 at 1.00 m s−1 and 0.45 at 1.50 m s−1 across all slopes. The ratio of hip joint to total individual leg positive work did not change with speed but was 0.34 at −9°, 0.33 at 0° and 0.37 at +9° across all speeds. The ratio of hip joint to total individual leg negative work was 0.21 at 1.00 m s−1, and 0.24 at 1.50 m s−1 across all slopes and 0.17 at −9°, 0.19 at 0° and 0.29 at +9° across all speeds. The ankle significantly contributes to walking on slopes and this contribution changes during sloped compared with level-ground walking, thus assistive devices that provide biomimetic ankle function must adapt to accommodate walking at different speeds and slopes; whereas assistive biomimetic devices for the knee only need to adapt at different speeds.
Performance Investigation of WAVE Network Under Influence of Highway Mobility

In Wireless Access/Vehicular Networks (WAVE) networks, the inter-connections link varies rapidly between vehicles, when transmitting information from one node to another, due to the frequent movement and varying mobility speeds of vehicles based on IEEE 802.11p; which, consequences in frequent network topology fluctuations and subsequently, results in route failures. This work is an effort to demonstrate an efficiently designed WAVE network used to transmit Dedicated Short-Range Communication (DSRC) message using vehicle-to-vehicle (V2V) link establishment and to calculate its QoS performance by means of NetSim 10.1 Simulator along with SUMO.
Cross-Domain Labeled LDA for Cross-Domain Text Classification

Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the feature distribution differences of the two domains. Most existing studies explicitly minimize such differences by an exact alignment mechanism (aligning features by one-to-one feature alignment, projection matrix etc.). Such exact alignment, however, will restrict models' learning ability and will further impair models' performance on classification tasks when the semantic distributions of different domains are very different. To address this problem, we propose a novel group alignment which aligns the semantics at group level. In addition, to help the model learn better semantic groups and semantics within these groups, we also propose a partial supervision for model's learning in source domain. To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and Reuters dataset, extensive quantitative (classification, perplexity etc.) and qualitative (topic detection) experiments are conducted to show the effectiveness of the proposed group alignment and partial supervision.
WiDetect: A Robust and Low-Complexity Wireless Motion Detector

Motion detection as a key component in modern security systems has received an increasing attention recently, but most existing solutions require special installation, calibration, and only have a limited coverage. In this paper, we propose WiDetect, a highly accurate, calibration-free, and low-complexity wireless motion detector. By exploiting the statistical theory of electromagnetic waves, we establish a link between the autocorrelation function of the physical layer channel state information (CSI) and motion in the environment. Temporal, frequency and spatial diversity are also exploited to further improve the robustness and accuracy of WiDetect. Extensive experiments conducted in several facilities show that WiDetect can achieve similar detection performance compared to a commercial home security system, while with much larger coverage and lower cost.
Dense Correspondence of Cone-Beam Computed Tomography Images Using Oblique Clustering Forest

Cone-beam computed tomography (CBCT) images provide insight into underlying 3D anatomies and facilitate treatment planning and evaluations in clinical orthodontics. Establishing dense correspondence of CBCT images is a cornerstone for automatic attribute transfer and statistical shape analysis. In this paper, we propose a novel oblique clustering forest-based metric for dense correspondence of CBCT images without prior labeling. The binary partitions in branch nodes are initialized by the dominant principal component and reﬁned by iterative point assignments. The randomly selected feature channels and the binary partition are used to learn the oblique hyperplane as the node splitting criterion. Instead of empirical forest learning, we provide a learning bound of the oblique clustering forest, which considers both the clustering compactness and the node splitting criteria determining tree traversals. We also propose a tree selection scheme under the guidance of the learning bound, which facilitates to obtain a lightweight forest. An incremental forest reﬁnement scheme is presented to account for the growing available volumetric images. Quantitative evaluation conﬁrms that the proposed method provides performance improvements for dense correspondences and the transfer of volumetric attributes, including landmarks and segmentation maps of CBCT images.
A Whole Body Attitude Stabilizer for Hybrid Wheeled-Legged Quadruped Robots

This work presents a new attitude balancing strategy implemented and validated on a quadrupedal robot equipped with a custom hybrid wheel-legged mobility system. The proposed method uses an inverse kinematics solution scheme based on Quadratic Programming optimization to generate full body motions that ensure the desired balancing performances. The strategy generates a compliant behaviour to cope with the applied external forces resulting in a stable and smooth reaction response. Furthermore, the method takes advantage of the robot hybrid wheeled-legged mobility system to provide new motion capabilities and balancing reactions as it will be shown through the paper. Extensive simulation studies on the Centauro robot are presented. Results show the efficiency of the propose method demonstrating significant contribution in the rejection of the applied external disturbances.
Optimum Sparse Array Design for Multiple Beamformers with Common Receiver

The problem of optimum sparse array beamformer design to maximize output signal-to-interference-plus-noise ratio (SINR) in the case of multiple narrowband sources was recently investigated. This was based on seeking both optimum sensor placement as well as optimum a single beamformer for all sources in the array field of view. In this paper, we consider multiple beamformers with a common sparse array. That is, we deal with a more prevalent case in radar and communications where each source is assigned its own beam. This could be the case for both switched and simultaneous or staring beams. The paper considers optimum sparse array design for both narrowband and wideband sources. Analysis and simulation examples demonstrate that the optimum sparse array configuration depends on both the arrival angle and the frequency of the incoming signal and it plays a vital role in determining the performance of multiple beamformer receivers.