Miracle-dz/UniFAR-FTUs huggingface dataset:
The pitfall of using question detection as a proxy for action inference in natural text conversations: a position paper

Detecting calls to action in natural text conversations, such as emails, chats and instant messages, is a pre-processing step that enables assistive technologies, such as the ones used in virtual assistants and chatbots, to capture and handle human-to-human interactions. Different forms of question detection are often used as a proxy solution for detecting intended actions. In this position paper, we provide empirical evidence that, despite their accuracy in detecting questions, both state-of-the-art industrial and academic question detection technologies are not good proxy solutions for inferring intended actions. We provide empirical data to demonstrate that the problem should be approached differently and as a comparison we present results obtained with a solution derived from our patent pending intention inference technology.
Multi-Agent Path Finding with Kinematic Constraints

Multi-Agent Path Finding (MAPF) is well studied in both AI and robotics. Given a discretized environment and agents with assigned start and goal locations, MAPF solvers from AI find collision-free paths for hundreds of agents with user-provided sub-optimality guarantees. However, they ignore that actual robots are subject to kinematic constraints (such as finite maximum velocity limits) and suffer from imperfect plan-execution capabilities. We therefore introduce MAPF-POST, a novel approach that makes use of a simple temporal network to postprocess the output of a MAPF solver in polynomial time to create a plan-execution schedule that can be executed on robots. This schedule works on non-holonomic robots, takes their maximum translational and rotational velocities into account, provides a guaranteed safety distance between them, and exploits slack to absorb imperfect plan executions and avoid time-intensive replanning in many cases. We evaluate MAPF-POST in simulation and on differential-drive robots, showcasing the practicality of our approach.
An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints

Participatory sensing systems can be used for concurrent event monitoring applications, like noise levels, fire, and pollutant concentrations. However, they are facing new challenges as to how to accurately detect the exact boundaries of these events, and further, to select the most appropriate participants to collect the sensing data. On the one hand, participants’ handheld smart devices are constrained with different energy conditions and sensing capabilities, and they move around with uncontrollable mobility patterns in their daily life. On the other hand, these sensing tasks are within time-varying quality-of-information (QoI) requirements and budget to afford the users’ incentive expectations. Toward this end, this article proposes an event-driven QoI-aware participatory sensing framework with energy and budget constraints. The main method of this framework is event boundary detection. For the former, a two-step heuristic solution is proposed where the coarse-grained detection step finds its approximation and the fine-grained detection step identifies the exact location. Participants are selected by explicitly considering their mobility pattern, required QoI of multiple tasks, and users’ incentive requirements, under the constraint of an aggregated task budget. Extensive experimental results, based on a real trace in Beijing, show the effectiveness and robustness of our approach, while comparing with existing schemes.
Novel time-frequency-eigen filter for intraoperative neurophysiologic monitoring in spinal surgeries

We present a novel signal-processing algorithm to extract the posterior tibial somatosensory evoked potentials (tSSEP) using a minimum number of trials. We analyze the proposed algorithm and compare it with the clinically used conventional signal averaging method for 12 surgical procedures. The tSSEP trials are continuously fed to our processing algorithm that displays the extracted SSEP after processing 12 successive unrejected sweeps. A unique filtering process employing time, frequency and eigen systems, in that order, was used to extract the SSEP from this set of 12 trials. The algorithm then detects, marks and records the P37 and N45 peaks using the first order differentials obtained through Walsh transformation. The monitoring using the algorithm was successful and proved conclusive to the clinical information through the different surgical procedures. Higher accuracy and faster execution time in determining the SSEP signals provides for a much improved and effective neurophysiological monitoring process.
A Novel MRI Biomarker of Spinal Cord White Matter Injury: T2*-Weighted White Matter to Gray Matter Signal Intensity Ratio

BACKGROUND AND PURPOSE: T2*-weighted imaging provides sharp contrast between spinal cord GM and WM, allowing their segmentation and cross-sectional area measurement. Injured WM demonstrates T2*WI hyperintensity but requires normalization for quantitative use. We introduce T2*WI WM/GM signal-intensity ratio and compare it against cross-sectional area, the DTI metric fractional anisotropy, and magnetization transfer ratio in degenerative cervical myelopathy. MATERIALS AND METHODS: Fifty-eight patients with degenerative cervical myelopathy and 40 healthy subjects underwent 3T MR imaging, covering C1–C7. Metrics were automatically extracted at maximally compressed and uncompressed rostral/caudal levels. Normalized metrics were compared with t tests, area under the curve, and logistic regression. Relationships with clinical measures were analyzed by using Pearson correlation and multiple linear regression. RESULTS: The maximally compressed level cross-sectional area demonstrated superior differences (P = 1 × 10−13), diagnostic accuracy (area under the curve = 0.890), and univariate correlation with the modified Japanese Orthopedic Association score (0.66). T2*WI WM/GM showed strong differences (rostral: P = 8 × 10−7; maximally compressed level: P = 1 × 10−11; caudal: P = 1 × 10−4), correlations (modified Japanese Orthopedic Association score; rostral: −0.52; maximally compressed level: −0.59; caudal: −0.36), and diagnostic accuracy (rostral: 0.775; maximally compressed level: 0.860; caudal: 0.721), outperforming fractional anisotropy and magnetization transfer ratio in most comparisons and cross-sectional area at rostral/caudal levels. Rostral T2*WI WM/GM showed the strongest correlations with focal motor (−0.45) and sensory (−0.49) deficits and was the strongest independent predictor of the modified Japanese Orthopedic Association score (P = .01) and diagnosis (P = .02) in multivariate models (R2 = 0.59, P = 8 × 10−13; area under the curve = 0.954, respectively). CONCLUSIONS: T2*WI WM/GM shows promise as a novel biomarker of WM injury. It detects damage in compressed and uncompressed regions and contributes substantially to multivariate models for diagnosis and correlation with impairment. Our multiparametric approach overcomes limitations of individual measures, having the potential to improve diagnostics, monitor progression, and predict outcomes.
Polarization-resolved Second Harmonic Microscopy in collagenous tissues: theoretical and experimental developments, and application to microstructural imaging during biomechanical assays in tendon.

Collagen is a major structural protein that forms various macromolecular organizations in tissues and is responsible for the biomechanical properties of most organs. Second harmonic generation (SHG) microscopy is a valuable imaging technique to probe collagen fibrillar organization. This work is aimed at implementing and characterizing polarization-resolved SHG (P-SHG) and coupling this technique to biomechanical assays to provide multiscale structural information on collagen tissues. We first studied the linear propagation effects that affect P-SHG imaging of thick anisotropic tissues such as collagen tissues. We developed a theoretical model that accounts for birefringence, diattenuation, and polarization scrambling, and obtained an excellent agreement with our P-SHG measurements in rattail tendon. Moreover we performed numerical simulations of light propagation and harmonic generation in tendon and cornea, which confirmed the crucial role of birefringence for P-SHG signal formation. We then implemented a new experimental device that combined mechanical testing with SHG imaging. It enabled visualization of the tendon crimp morphology at the micrometer scale during macroscopic strain-stress measurements. Our results proved that continuous SHG imaging allows for elucidating the link between macroscopic response and microscopic structure of tissues. Finally, we developed a theoretical model, which relates the P-SHG signal anisotropy to the orientational order of its SHG-capable constituents at submicrometer scale. We tested our model by performing P-SHG measurements in increasingly stretched tendon, and successfully characterized variations of fibril disorder within fascicle with strain.
Triarylmethyl-based biradical as a superoxide probe

Abstract Superoxide radical represents one of the most biologically relevant reactive oxygen species involved in numerous physiological and pathophysiological processes. Superoxide measurement through the decay of an electron paramagnetic resonance (EPR) signal of a triarylmethyl (TAM) radical possesses the advantage of a high selectivity and relatively high rate constant of TAM reaction with the superoxide. Hereby we report a straightforward synthesis and characterization of a TAM–TAM biradical showing a high reactivity with superoxide (second-order rate constant, (6.7 ± 0.2) × 103 M−1 s−1) enabling the measurement of superoxide radical by following the increase of a sharp EPR signal associated with the formation of a TAM-quinone-methide monoradical product.
Improved a priori snr estimation in speech enhancement

In this paper, a modified a priori SNR estimation method with application in speech enhancement is presented. The decision directed (DD) approach for the a priori SNR estimation has been the most popular method due to its ability to eliminate musical noise in speech enhancement. However, a limitation of this method is the slow tracking of speech onsets that generates a transient distortion to the speech. The proposed method modifies the DD a priori SNR estimator by employing an adaptive smoothing factor based on the instan-taneous a posteriori SNR and an adaptive sigmoid function to obtain an improved tracking of speech onsets. Experimental results using several instrumental measures show the ability of the proposed method to preserve weak speech components when compared to two reference methods while maintaining the advantage of the DD approach in eliminating the musical noise.
A bio-inspired secure IPv6 communication protocol for Internet of Things

The Internet of Things (IoT) with a direct internet connectivity for global access is one of the promising technologies for future communication systems. The Internet Protocol version 6 (IPv6) based routing protocols that are currently available for IoT are prone to various vulnerabilities. Hence, the Personally Identifiable Information (PII) stored and/or communicated in IoT can easily be compromised. In this paper, we propose a Bio-inspired Secure IPv6 Communication Protocol (BSCoP) for IoT. We have enhanced the IPv6 Routing Protocol for Low-Power and Lossy Networks (RFC 6550) by incorporating a classification algorithm inspired by the Artificial Immune System (AIS). The AIS-based algorithm works by detecting the routing behavior of the communicating nodes in the local routing tables and classify the misbehaving nodes as non-self. With the help of the classification algorithm, we can detect excessive broadcasts and isolate them to improve power and transmission rates of the transmitted packets in a cluster of IoT nodes. The detailed experimental results show the promising performance of the proposed protocol while offering the security desired in cyberspace in terms of energy consumption and data packet rate.
Effect of orange juice intake on vitamin C concentrations and biomarkers of antioxidant status in humans.

BACKGROUND Consumption of fruit and vegetables is associated with improved health and a decreased prevalence of chronic degenerative processes. OBJECTIVES The objectives were to assess the bioavailability of vitamin C from orange juice and its influence on plasma vitamin C and 8-epi-prostaglandin F(2 alpha) (8-epi-PGF(2 alpha)) concentrations in a healthy human population. DESIGN Six men and 6 women consumed 500 mL commercial fresh-squeezed orange juice/d for 14 d, corresponding to an intake of 250 mg ascorbic acid/d. On the first day of the study, the subjects drank the juice in one dose (dose-response study), and on days 2-14 they consumed 250 mL in the morning and 250 mL in the afternoon. Blood was collected every hour for 6 h on the first day and again on days 7 and 14. RESULTS Baseline plasma vitamin C concentrations were significantly higher (P = 0.03) among the women than among the men (56.4 +/- 4.4 compared with 44.3 +/- 3.5 micromol/L). In the dose-response study, the maximum increase in plasma vitamin C occurred 3 h postdose in both the men and the women. Vitamin C concentrations remained significantly higher on days 7 and 14 than at baseline. Baseline concentrations of 8-epi-PGF(2 alpha) were significantly higher (P = 0.03) among the men than among the women (249.6 +/- 25.4 compared with 177.7 +/- 6.2 pg/mL) but decreased significantly (P = 0.04) by day 14 of the intervention. A significant inverse correlation was observed between vitamin C and 8-epi-PGF(2 alpha) (r = -0.791, P = 0.0022). Among smokers, baseline vitamin C was lower and 8-epi-PGF(2 alpha) higher than among nonsmokers. CONCLUSIONS Drinking orange juice (500 mL/d) increases plasma concentrations of vitamin C and reduces concentrations of 8-epi-PGF(2 alpha) in humans. These effects were significantly more pronounced in smokers.
Operads of Finite Posets

We describe four natural operad structures on the vector space generated by isomorphism classes of finite posets. The three last ones are set-theoretical and can be seen as a simplified version of the first, the same way the NAP operad behaves with respect to the pre-Lie operad. Moreover the two first ones are isomorphic.
Robust Transceiver Design for Multiuser MIMO Downlink with Channel Uncertainties

This letter addresses the problem of robust transceiver design for the multiuser multiple-input-multiple-output (MIMO) downlink where the channel state information at the base station (BS) is imperfect. A stochastic approach which minimizes the expectation of the total mean square error (MSE) of the downlink conditioned on the channel estimates under a total transmit power constraint is adopted. The iterative algorithm reported in [2] is improved to handle the proposed robust optimization problem. Simulation results show that our proposed robust scheme effectively reduces the performance loss due to channel uncertainties and outperforms existing methods, especially when the channel errors of the users are different.
Predicting Future Lane Changes of Other Highway Vehicles using RNN-based Deep Models

In the event of sensor failure, it is necessary for autonomous vehicles to safely execute emergency maneuvers while avoiding other vehicles on the road. In order to accomplish this, the sensor-failed vehicle must predict the future semantic behaviors of other drivers, such as lane changes, as well as their future trajectories given a small window of past sensor observations. We address the first issue of semantic behavior prediction in this paper, by introducing a prediction framework that leverages the power of recurrent neural networks (RNNs) and graphical models. Our prediction goal is to predict the future categorical driving intent, for lane changes, of neighboring vehicles up to three seconds into the future given as little as a one-second window of past LIDAR, GPS, inertial, and map data. We collect real-world data containing over 500,000 samples of highway driving using an autonomous Toyota vehicle. We propose a pair of models that leverage RNNs: first, a monolithic RNN model that tries to directly map inputs to future behavior through a long-short-term-memory network. Second, we propose a composite RNN model by adopting the methodology of Structural Recurrent Neural Networks (RNNs) to learn factor functions and take advantage of both the high-level structure of graphical models and the sequence modeling power of RNNs, which we expect to afford more transparent modeling and activity than the monolithic RNN. To demonstrate our approach, we validate our models using authentic interstate highway driving to predict the future lane change maneuvers of other vehicles neighboring our autonomous vehicle. We find that both RNN models outperform baselines, and they outperform each other in certain conditions.
VARIABLE SELECTION FOR ROAD SEGMENTATION IN AERIAL IMAGES

Abstract. For extraction of road pixels from combined image and elevation data, Wegner et al. (2015) proposed classification of superpixels into road and non-road, after which a refinement of the classification results using minimum cost paths and non-local optimization methods took place. We believed that the variable set used for classification was to a certain extent suboptimal, because many variables were redundant while several features known as useful in Photogrammetry and Remote Sensing are missed. This motivated us to implement a variable selection approach which builds a model for classification using portions of training data and subsets of features, evaluates this model, updates the feature set, and terminates when a stopping criterion is satisfied. The choice of classifier is flexible; however, we tested the approach with Logistic Regression and Random Forests, and taylored the evaluation module to the chosen classifier. To guarantee a fair comparison, we kept the segment-based approach and most of the variables from the related work, but we extended them by additional, mostly higher-level features. Applying these superior features, removing the redundant ones, as well as using more accurately acquired 3D data allowed to keep stable or even to reduce the misclassification error in a challenging dataset.
Incomplete host immunity favors the evolution of virulence in an emergent pathogen

Ratcheting up wild virulence Partially protective vaccination can sometimes select for increasingly virulent pathogens. Fleming-Davies et al. asked what happens in a natural system. In the United States, the house finch population is suffering an increasingly virulent epidemic caused by Mycoplasma gallisepticum. The pathogen induces incomplete immunity that clears less virulent pathogens and offers partial protection against strains of greater virulence. In the birds, the partial immune response does away with competition from the less virulent pathogens. The partial immunity of the host also hinders replication of the more virulent pathogens enough to allow some birds to survive. This allows increasingly virulent forms of the pathogen to be transmitted. Science, this issue p. 1030 A study of house finches and their bacterial pathogen Mycoplasma gallisepticum elucidates how virulence is enhanced. Immune memory evolved to protect hosts from reinfection, but incomplete responses that allow future reinfection may inadvertently select for more-harmful pathogens. We present empirical and modeling evidence that incomplete immunity promotes the evolution of higher virulence in a natural host-pathogen system. We performed sequential infections of house finches with Mycoplasma gallisepticum strains of various levels of virulence. Virulent bacterial strains generated stronger host protection against reinfection than less virulent strains and thus excluded less virulent strains from infecting previously exposed hosts. In a two-strain model, the resulting fitness advantage selected for an almost twofold increase in pathogen virulence. Thus, the same immune systems that protect hosts from infection can concomitantly drive the evolution of more-harmful pathogens in nature.
Ethanol Gas Sensor Based on a Hybrid Polymethyl Methacrylate–Silica Microfiber Coupler

Gas sensors have broad applications for either industrial process control or biological detection. We developed a versatile and compact optical hybrid coupler gas sensor composed of a polymethyl methacrylate microwire and a silica microfiber. The light propagating in the microfiber interacts with the polymer microwire. When the hybrid coupler is exposed to different concentrations of ethanol vapor, the refractive indices of the polymer and the surroundings are changed, resulting in shifts in the resonant wavelength. The experimental sensitivity for ethanol vapor is 0.65 pm/ppm.
Biodiversity of fungi in hot desert sands

The fungal community of six sand samples from Saudi Arabia and Jordan deserts was characterized by culture‐independent analysis via next generation sequencing of the 18S rRNA genes and by culture‐dependent methods followed by sequencing of internal transcribed spacer (ITS) region. By 18S sequencing were identified from 163 to 507 OTUs per sample, with a percentage of fungi ranging from 3.5% to 82.7%. The identified fungal Phyla were Ascomycota, Basal fungi, and Basidiomycota and the most abundant detected classes were Dothideomycetes, Pezizomycetes, and Sordariomycetes. A total of 11 colonies of filamentous fungi were isolated and cultured from six samples, and the ITS sequencing pointed toward five different species of the class Sordariomycetes, belonging to genera Fusarium (F. redolens, F. solani, F. equiseti), Chaetomium (C. madrasense), and Albifimbria (A. terrestris). The results of this study show an unexpectedly large fungal biodiversity in the Middle East desert sand and their possible role and implications on human health.
Kalman filter based team navigation for Multiple Unmanned Marine Vehicles

In applications employing multiple unmanned marine vehicles (MUMVs), the navigation has a very great importance to guarantee formation preservation and collision avoidance. While single vehicles usually base their navigation on absolute measurements (GPS, inertial navigation) to determine their position relative to the world, it may be reasonable to perform a relative navigation within vehicle teams. In this paper, we propose relative team navigation based on Kalman Filters to enable a velocity controller to establish a close formation under the typical marine constraints (narrow band width communication with low reliability). We will simulate a team of three marine vehicles and compare the results of different strategies for team navigation.