Miracle-dz/UniFAR-FTUs huggingface dataset:
Why Don't Languages Adapt to Their Environment?

The issue of whether languages adapt to their environment depends on our understanding of language, adaptation and environment. I consider these three concepts from an internalist or biolinguistic point of view. If adaptation is defined as the result of the differential transmission of phenotypic traits by means of natural selection, then both natural species and languages are adapted. Recall that according to Darwin’s own insight, the evolutionary mechanisms for species and languages are “curiously the same” (or “curiously parallel”). However, if the concept of adaptation entails that the environment is the essential source of the structure of evolving objects, then neither natural species nor languages can be said to be adapted to their environment. In the case of languages, I will argue that much of their structure is insensitive to historical change and, therefore, incapable of adaptation to the external environment. The immediate environment of languages is in fact internal to the mind/brain and is thus less variable than the social and physical environment in which people live. On the other hand, the dimensions of languages that are variable have such an indirect relation with the physical and social environment that the notion of adaptation to extra-linguistic reality can only be applied weakly, and then it is unable to explain the main patterns of linguistic structural diversity.
On the Importance of Uncertainty Representation in Active SLAM

The purpose of this work is to highlight the paramount importance of <italic>representing</italic> and <italic> quantifying</italic> uncertainty to correctly report the associated confidence of the robot's location estimate at each time step along its trajectory and therefore decide the correct course of action in an active SLAM mission. We analyze the monotonicity property of different decision-making criteria, both in 2-D and 3-D, with respect to the representation of uncertainty and of the orientation of the robot's pose. Monotonicity, the property that uncertainty increases as the robot moves, is essential for adequate decision making. We analytically show that, by using differential representations to propagate spatial uncertainties, monotonicity is preserved for all optimality criteria, <italic>A-opt</italic>, <italic>D-opt</italic>, and <italic>E-opt</italic>, and for Shannon's entropy. We also show that monotonicity does not hold for any criteria in absolute representations using Roll-Pitch-Yaw and Euler angles. Finally, using unit quaternions in absolute representations, the only criteria that preserve monotonicity are <italic>D-opt</italic> and Shannon's entropy.
ISPAD Clinical Practice Consensus Guidelines 2018: Diabetes in adolescence

-Pre-conceptional counseling should begin early during puberty -New recommendations for type of contraception that should be used for girls are made. -Transition programs and care for young adults are now included. -Mental health problems that may be associated with poor metabolic control should be treated early. -Adherence remains that greatest impediment to successful deployment of diabetes-related technologies. This article is protected by copyright. All rights reserved.
User location prediction based cell discovery scheme for user-centric ultra-dense networks

Ultra-Dense Network (UDN) is foreseen as one of the promising technologies in 5G to meet the ever-increasing mobile traffic demands. However, with existing cell discovery mechanisms, such massive deployment of access points (APs) would lead to a considerable increase in energy consumption and a significant reduction in offloading opportunity. To solve those problems, a User Location Prediction based Cell Discovery (ULPCD) scheme for User-centric Ultra-Dense Network (UUDN) is proposed. In the scheme, the network deployment information is sent to the user equipment (UE) the first time user enters in the range of cells. Then, with the aid of positioning information, the probability of a user entering into a new cell is calculated. Using the probability, UE can predict whether to send a connection request to activate an idle AP before entering in the AP's cell range. Simulation results demonstrate that our proposed scheme reduces over 60% energy consumption of UE and saves at least 20% working time of APs compared with state-of-the-art schemes. Meanwhile, our scheme provides higher detection probability, shorter access delay, and more offloading opportunity.
Concept design of three-dimensional modulation using beam hopping for 5G networks

In this paper, a novel transmission scheme which is called three-dimensional modulation using beam hopping (3DModBH) and a new receiver algorithm are suggested. The new concepts improve the spectral efficiency and the reliability of the transmit signals of 5G networks by using the spatial domain of multiple small cells. The main distinguishing feature of 3DModBH transmission scheme is that it maps additional information bits by exploiting the spatial domain of multiple small cells or base stations (BSs). More specifically, the suggested 3DModBH uses the conventional two-dimensional (2-D) modulation techniques plus the spatial domain of multiple small cells or BSs to transmit the information bits, whereas the state-of-the-art transmission scheme (SoAT) uses only the conventional 2-D modulation techniques to transmit the data. We demonstrate the advantages of the novel scheme in small cell scenario under strong interference in 5G networks. We propose a new receiver algorithm to detect the transmitted information bits in practical interference limited scenario. The performance of 3DModBH scheme and the suggested receiver are compared with the SoAT in term of average bit error rate, receiver complexity, and average bits per channel use in practical 5G scenario.
Neuroprotection in glaucoma: recent advances and clinical translation

Intraocular pressure (IOP) reduction is currently the only evidence‐based treatment strategy for glaucoma. However, IOP control in some individuals is challenging. Despite optimal treatment, a significant proportion of individuals will progress, with loss of visual field, loss of driving vision and impaired quality of life. A new modality that could augment current treatment and reduce the rate of neurodegeneration to preserve vision throughout life would be a major breakthrough. A vast number of studies have reported effective neuroprotection in animal models of glaucoma; however, translation to the clinic remains a major hurdle. Herein, we explore the therapeutic advancements in non‐IOP‐dependent neuroprotection research based upon potential pathogenic mechanisms and propose strategies to improve the clinical translation of neuroprotective research in glaucoma.
Utilization of waveform measurements for degradation analysis of AlGaN/GaN HFETs

This paper employs, for the first time, RF waveform engineering to monitor device degradation over an RF "burn in" period. Measured RF current and voltage waveforms are used to monitor the degradation effects seen in GaN HFET transistors during large signal CW RF stress testing. The technique provides extra information on device performance compared with standard RF performance measures, demonstrating clearly where on the output IV plane the degradation is occurring and allowing device designers advanced insight into the degradation mechanisms limiting RF performance.
On a new adaptive multivariable twisting sliding mode control approach and its application

This paper presents a new adaptive multivariable twisting sliding mode control for uncertain systems. A Lyapunov function based approach is utilized to show the finite-time convergence of the control system in the presence of a class of disturbance. Overestimation of control gains is avoided via the proposed adaptation mechanism. The application to the attitude control law design for reusable launch vehicle (RLV) validates the proposed approach.
Optimal automatic train operation via deep reinforcement learning

The energy consumption occupies a considerable part in the total cost of the high-speed train operation. This paper focuses on minimizing the energy consumption of high-speed train by providing an optimal trajectory planning method. In this case, several other conditions including punctuality standard, comfort standard and varying speed limitation are taken into consideration in order to systematically evaluate the performance of a designed trajectory. The air resistance related to the current speed of the train and the regenerative braking system related to the braking force enhance the difficulty of t he trajectory planning problem. In previous studies of trajectory planning, either the effort produced by the high-speed train or the distance is regarded as a discrete variable, while the modern train usually provides continuous effort. This paper will propose an algorithm to deal with the trajectory planning problem mentioned based on the Deep Deterministic Policy Gradient.
Technical potentials and challenges within internal logistics 4.0

The industrial environment is characterized by a permanent change. As a result, the production requirements and their processes are constantly changing. The present paper takes up the latest transformation of industry through digitization and describes future “smart solutions” for new value-added concepts. The focus is on the impacts of changes on the logistics sector within Industrie 4.0 as well as on the technical potentials for internal logistics. In this context a new description approach is developed. The aim is to show a new approach that ranges from key elements of elementary logistics to the diverse new requirements of Logistics 4.0 Furthermore, the future role of humans in the industrial working environment is controversially discussed with proceeding autonomy and digitization.
Consensus-Based Two-Stage Salvo Attack Guidance

The problem of salvo attack of multiple missiles without time-to-go estimation has been addressed by a two-stage guidance scheme in this correspondence. In the first stage, a simple decentralized control law is designed to provide desired initial conditions (target-missile relative range and velocity lead angle) for the latter stage. As for the second stage, all missiles are governed by classical pure proportional navigation guidance law. Numerical simulations under two-dimensional (2-D) and 3-D cases demonstrate the effectiveness of the proposed formulation.
A public opinion keyword vector for social sentiment analysis research

In the Internet era, online platforms are the most convenient means for people to share and retrieve knowledge. Social media enables users to easily post their opinions and perspectives regarding certain issues. Although this convenience lets the internet become a treasury of information, the overload also prevents user from understanding the entirety of various events. This research aims at using text mining techniques to explore public opinion contained in social media by analyzing the reader's emotion towards pieces of short text. We propose Public Opinion Keyword Embedding (POKE) for the presentation of short texts from social media, and a vector space classifier for the categorization of opinions. The experimental results demonstrate that our method can effectively represent the semantics of short text public opinion. In addition, we combine a visualized analysis method for keywords that can provide a deeper understanding of opinions expressed on social media topics.
Learning Temporal Intervals in Neural Dynamics

Storing and reproducing temporal intervals is an important component of perception, action generation, and learning. How temporal intervals can be represented in neuronal networks is thus an important research question both in study of biological organisms and artificial neuromorphic systems. Here, we introduce a neural-dynamic computing architecture for learning temporal durations of actions. The architecture uses a dynamic neural fields (DNFs) representation of the elapsed time and a memory trace dynamics to store the experienced action duration. Interconnected dynamical nodes signal beginning of an action, its successful accomplishment, or failure, and activate formation of the memory trace that corresponds to the action’s duration. The accumulated memory trace influences the competition between the dynamical nodes in such a way that the failure node gains a competitive advantage earlier if the stored duration is shorter. The model uses neurally based DNF dynamics and is a process model of how temporal durations may be stored in neural systems, both biological and artificial ones. The focus of this paper is on the mechanism to store and use duration in artificial neuronal systems. The model is validated in closed-loop experiments with a simulated robot.
Mean shift detection using active learning in dermatological images

A scheme for detecting heterogenous regions in dermatological images with malignant melanoma is proposed. The scheme works without setting any parameter. The mean shift detection problem is divided into two stages: window size optimization and detection. In the first stage, the maximum circular neighborhood centered on each pixel for which it is true that all the elements belong to the same class as the central one is estimated using redundant data sets generated with overlapping groups. Statistics are computed from all these neighborhoods and associated ot the respective central pixels. As expected, larger values of a minimizing energy function are assigned to pixels belonging to heterogeneous regions. In the second stage, those regions are detected by applying first an expectation-maximization algorithm and, afterwards, automatically defining a threshold between homogeneous and heterogeneous regions. The present scheme is tested on a set of synthetical images. Results are shown on synthetical and real images. Extensions of the scheme to textural cases are also shown.
Multivariate prediction intervals for photovoltaic power generation

The current literature in probabilistic forecasting is focused on quantifying the uncertainty of each random variable individually. This leads to the failure in informing about interdependence structure of uncertainty at different locations and/or different lead times. When there is a positive or negative association between a number of random variables, the prediction regions for them should be reflected by multivariate or joint uncertainty sets. The existing literature is very primitive in the area of multivariate uncertainty sets modeling. In this paper, uncertainty regions are generated in the form of multivariate prediction intervals. We will examine the performance of Gaussian and R-Vine copulas in characterizing the correlated behavior of PV power generations at successive lead-times. Copulas are compared based on goodness-of-fit metrics as well as skill scores. A framework is elaborated to generate multivariate prediction intervals out of the scenarios generated from Gaussian and R- vine multivariate densities. The resultant multivariate prediction intervals are evaluated based on their calibration and sharpness. The approaches are tested on a real-world dataset including PV power measurements and weather forecasts. This paper provides a series of useful analyses and comparative results for multivariate uncertainty modeling of PV power that can serve as a basis for future works in the area.
An implementation of IoT gateway for narrow bandwidth and massive machine-type communications over B4G cellular network

Along with the continuous process of standard and infrastructure deployments of LTE (Long Term Evolution) technology, it is expected that in the coming future, B4G telecommunication can provide a wireless access service with less power consumption for massive IoT (Internet of Things) devices using narrow bandwidth. Consequently, application paradigms will transfer from HTC (Human Type Communications) to MTC (Machine-Type Communications) to provide more intelligent services such as traffic control, environment monitor, and health care. Therefore, this paper is to provide an implementation and operation scheme of LTE gateway for narrow bandwidth requirements and Massive Machine-Type Communications (mMTC) over B4G (Beyond 4G) cellular network to enhance the power efficiency for IoT devices accessing to the B4G network.
Loss‐Framed Financial Incentives and Personalized Goal‐Setting to Increase Physical Activity Among Ischemic Heart Disease Patients Using Wearable Devices: The ACTIVE REWARD Randomized Trial

Background Regular physical activity reduces the risk of cardiovascular events, but most ischemic heart disease (IHD) patients do not obtain enough. Methods and Results ACTIVE REWARD (A Clinical Trial Investigating Effects of a Randomized Evaluation of Wearable Activity Trackers with Financial Rewards) was a 24‐week home‐based, remotely monitored, randomized trial with a 16‐week intervention (8‐week ramp‐up incentive phase and 8‐week maintenance incentive phase) and an 8‐week follow‐up. Patients used wearable devices to track step counts and establish a baseline. Patients in control received no other interventions. Patients in the incentive arm received personalized step goals and daily feedback for all 24 weeks. In the ramp‐up incentive phase, daily step goals increased weekly by 15% from baseline with a maximum of 10 000 steps and then remained fixed. Each week, $14 was allocated to a virtual account; $2 could be lost per day for not achieving step goals. The primary outcome was change in mean daily steps from baseline to the maintenance incentive phase. Ischemic heart disease patients had a mean (SD) age of 60 (11) years and 70% were male. Compared with control, patients in the incentive arm had a significantly greater increase in mean daily steps from baseline during ramp‐up (1388 versus 385; adjusted difference, 1061 [95% confidence interval, 386–1736]; P<0.01), maintenance (1501 versus 264; adjusted difference, 1368 [95% confidence interval, 571–2164]; P<0.001), and follow‐up (1066 versus 92; adjusted difference, 1154 [95% confidence interval, 282–2027]; P<0.01). Conclusions Loss‐framed financial incentives with personalized goal setting significantly increased physical activity among ischemic heart disease patients using wearable devices during the 16‐week intervention, and effects were sustained during the 8‐week follow‐up. Clinical Trial Registration URL: https://www.clinicaltrials. gov. Unique identifier: NCT02531022.
Deciphering the Adaptation of Corynebacterium glutamicum in Transition from Aerobiosis via Microaerobiosis to Anaerobiosis

Zero-growth processes are a promising strategy for the production of reduced molecules and depict a steady transition from aerobic to anaerobic conditions. To investigate the adaptation of Corynebacterium glutamicum to altering oxygen availabilities, we conceived a triple-phase fermentation process that describes a gradual reduction of dissolved oxygen with a shift from aerobiosis via microaerobiosis to anaerobiosis. The distinct process phases were clearly bordered by the bacteria’s physiologic response such as reduced growth rate, biomass substrate yield and altered yield of fermentation products. During the process, sequential samples were drawn at six points and analyzed via RNA-sequencing, for metabolite concentrations and for enzyme activities. We found transcriptional alterations of almost 50% (1421 genes) of the entire protein coding genes and observed an upregulation of fermentative pathways, a rearrangement of respiration, and mitigation of the basic cellular mechanisms such as transcription, translation and replication as a transient response related to the installed oxygen dependent process phases. To investigate the regulatory regime, 18 transcriptionally altered (putative) transcriptional regulators were deleted, but none of the deletion strains showed noticeable growth kinetics under an oxygen restricted environment. However, the described transcriptional adaptation of C. glutamicum resolved to varying oxygen availabilities provides a useful basis for future process and strain engineering.
Automated classification of multiphoton microscopy images of ovarian tissue using deep learning

Abstract. Histopathological image analysis of stained tissue slides is routinely used in tumor detection and classification. However, diagnosis requires a highly trained pathologist and can thus be time-consuming, labor-intensive, and potentially risk bias. Here, we demonstrate a potential complementary approach for diagnosis. We show that multiphoton microscopy images from unstained, reproductive tissues can be robustly classified using deep learning techniques. We fine-train four pretrained convolutional neural networks using over 200 murine tissue images based on combined second-harmonic generation and two-photon excitation fluorescence contrast, to classify the tissues either as healthy or associated with high-grade serous carcinoma with over 95% sensitivity and 97% specificity. Our approach shows promise for applications involving automated disease diagnosis. It could also be readily applied to other tissues, diseases, and related classification problems.
Taxi model of unmanned aerial vehicle: Formulation and simulation

Unmanned Aerial Vehicles cannot work efficiently without a control system. Before any form of control is applied to it, modeling of the system is necessary. This work focuses on the development of a Taxi model of UAV. The reformulated model has been developed and simulated in Simulink and Simscape. Three scenarios have been considered in Simulation: Normal conditions with no wind, head and tail wind of 15 m/s and steering angle of 1 radian. The key objective of this model is to include all the necessary dynamics of UAV when it is in taxi phase. This has been done by modifying the vehicle body model in Simscape to fit to the specifications of the UAV and moreover integrating it with an undercarriage model from previous literature to include the forces.
Time-Optimal Trajectory Planning for Delta Robot Based on Quintic Pythagorean-Hodograph Curves

In this paper, a time-optimal trajectory planning method based on quintic Pythagorean-Hodograph (PH) curves is proposed to realize the smooth and stable high-speed operation of the Delta parallel robot. The trajectory is determined by applying the quintic PH curves to the transition segments in the pick-and-place operation trajectory and the 3-4-5 polynomial motion law to the trajectory. The quintic PH curves are optimized to reduce the cycle time of the pick-and-place operation. In addition, a comparison between different trajectory planning methods has been implemented so as to observe the performance of the obtained results. The MATLAB simulation results reveal that compared with the trajectory planning based on vertical and horizontal motion superposition, the trajectory planning based on quintic PH curves is completed with a shorter motion cycle time and more stable motion performance, with the velocities, accelerations, and jerks in joint space bounded and continuous. Experiments carried out on the prototype also confirm that the trajectory planning based on quintic PH curves has a shorter cycle time, which is of great importance to high-speed operations of Delta parallel robots.