Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles

Machine learning (ML) has made incredible impacts and transformations in a wide range of vehicular applications. As the use of ML in Internet of Vehicles (IoV) continues to advance, adversarial threats and their impact have become an important subject of research worth exploring. In this paper, we focus on Sybil-based adversarial threats against a deep reinforcement learning (DRL)-assisted IoV framework and more specifically, DRL-based dynamic service placement in IoV. We carry out an experimental study with real vehicle trajectories to analyze the impact on service delay and resource congestion under different attack scenarios for the DRL-based dynamic service placement application. We further investigate the impact of the proportion of Sybil-attacked vehicles in the network. The results demonstrate that the performance is significantly affected by Sybil-based data poisoning attacks when compared to adversary-free healthy network scenario.
Toward Explainable Artificial Intelligence for Early Anticipation of Traffic Accidents

Traffic accident anticipation is a vital function of Automated Driving Systems (ADS) in providing a safety-guaranteed driving experience. An accident anticipation model aims to predict accidents promptly and accurately before they occur. Existing Artificial Intelligence (AI) models of accident anticipation lack a human-interpretable explanation of their decision making. Although these models perform well, they remain a black-box to the ADS users who find it to difficult to trust them. To this end, this paper presents a gated recurrent unit (GRU) network that learns spatio-temporal relational features for the early anticipation of traffic accidents from dashcam video data. A post-hoc attention mechanism named Grad-CAM (Gradient-weighted Class Activation Map) is integrated into the network to generate saliency maps as the visual explanation of the accident anticipation decision. An eye tracker captures human eye fixation points for generating human attention maps. The explainability of network-generated saliency maps is evaluated in comparison to human attention maps. Qualitative and quantitative results on a public crash data set confirm that the proposed explainable network can anticipate an accident on average 4.57 s before it occurs, with 94.02% average precision. Various post-hoc attention-based XAI methods are then evaluated and compared. This confirms that the Grad-CAM chosen by this study can generate high-quality, human-interpretable saliency maps (with 1.23 Normalized Scanpath Saliency) for explaining the crash anticipation decision. Importantly, results confirm that the proposed AI model, with a human-inspired design, can outperform humans in accident anticipation.
NON-PREEMPTIVE PRIORITIES IN SIMPLE FORK-JOIN QUEUES

We study a fork-join queue with m servers and m classes of jobs, where ajob belonging to class le splits into le tasks. The effect of assigning non-preemptive priorities to the different classes, on the expected response time, is the subject of this work. We first consider the situation where each server knows to which class each task in its queue belongs, but does not know the situation at the other servers. We show conditions under which class 1 jobs should be given the top priority, and other conditions where giving such priority is undesirable. To demonstrate the possible reduction in the expected response times, we obtain closed form results for a simple example. In the remainder of the work we consider some aspects of assigning priorities to classes le > I, and derive some properties of interest.
Extreme Metrics and Large Ensembles

Abstract. We consider the problem of estimating the ensemble sizes required to characterize the forced component and the internal variability of a range of extreme metrics. While we exploit existing large ensembles contributed to the CLIVAR Large Ensemble Project, our perspective is that of a modeling center wanting to estimate a-priori such sizes on the basis of an existing small ensemble (we use five members here). We therefore ask if such small-size ensemble is sufficient to estimate the population variance in a way accurate enough to apply a well established formula that quantifies the expected error as a function of n (the ensemble size). We find that indeed we can anticipate errors in the estimation of the forced component for temperature and precipitation extreme metrics as a function of n by applying the population variance derived by five members in the formula. For a range of spatial and temporal scales, forcing levels (we use RCP8.5 simulations), and both models considered here as our proof of concept, CESM1-CAM5 and CanESM2, it appears that an ensemble size of 20 or 25 members can provide estimates of the forced component for the extreme metrics considered that remain within small absolute and percentage errors. Additional members beyond 20 or 25 add only marginal precision to the estimate, which remains true when extreme value analysis is used. We then ask about the ensemble size required to estimate the ensemble variance (a measure of internal variability) along the length of the simulation, and – importantly – about the ensemble size required to detect significant changes in such variance along the simulation with increased external forcings. When an F-test is applied to the ratio of the variances in question, one estimated on the basis of only 5 or 10 ensemble members, one estimated using the full ensemble (up to 50 members in our study) we do not obtain significant results even when the analysis is conducted at the grid-point scale. While we recognize that there will always exist applications and metric definitions requiring larger statistical power and therefore ensemble sizes, our results suggest that for a wide range of analysis targets and scales an effective estimate of both forced component and internal variability can be achieved with sizes below 30 members. This invites consideration of the possibility of exploring additional sources of uncertainty, like physics parameter settings, when designing ensemble simulations.
ESTIMATING PEAKEDNESS FROM ARRIVAL COUNTS

An estimate of peakedness z of a stationary renewal process can be obtained from bhe variance-mean ratio of the number of arrivals, N(T) ~ over a sufficiently long time interval T. It 1S shown that when the traffic stream is nonstationary, inflated values of the z-estimate ~an occur which may be misleading for engineer1ng procedures. For example, if one assumes the variability of N(T) is due to z while it is actually due to nonstationarity, an incorrect number of servers in a loss or delay system could be indicated. A method of separating z from time variations is also presented.
Application of Adaptive Materials and Coatings to Increase Cutting Tool Performance: Efficiency in the Case of Composite Powder High Speed Steel

The paper proposes a classification of adaptive materials and coatings for tool purposes, showing the ability to adapt to external heat and power influences, thereby improving tool life. Creating a cutting tool made of composite powder high speed steels containing refractory TiC, TiCN, and Al2O3 compounds for milling 41CrS4 steel demonstrated the effectiveness of the adaptive materials. The tool material characteristics under the external loads’ influence and the surface layer adaptation to the heat–power exposure conditions were shown by the temperature field study using a semiartificial microthermocouple method (the level of fields is reduced by 20%–25% for 80% HSS + 20% TiCN), frictional interaction high-temperature tribometry (the coefficient of friction did not exceed 0.45 for 80% HSS + 20% TiCN at +20 and 600 °C), laboratory performance tests, and spectrometry of the surface layer secondary structures. Spectral analysis shows the highest spectrum intensity of TiC2 after 5 min of running in. After 20 min of milling (V = 82 m/min, f = 0.15 mm/tooth), dicarbide decomposes and transits to thermally stable secondary phase films of good lubricity such as TiO (maximum) and TiN (partially). There was an increase in tool life of up to 2 times (>35 min for 80% HSS + 20% TiCN), and a decrease in the roughness of up to 2.9 times (Ra less than 4.5 µm after 25 min of milling).
The Role of N-Linked Glycosylation of Sindbis Virus Glycoproteins E2 and E1 in Viral Infectivity and Membrane Fusion Activity

The ectodomains of Sindbis virus (SIN) glycoproteins E2 and E1 are glycosylated at aspargine residues in a consensus sequence Asn-X-Ser/Thr. Here, we studied the biological role of the N-linked oligosaccharides at positions E2:196 and E1:139, using SIN mutants with an asparagine-to-glutamine substitution at either one of these positions. It was observed that the removal of a single glycosylation site dramatically affects the infectivity of the virus on baby hamster kidney cells (BHK-21). Moreover, deglycosylated SIN mutants were impaired in their ability to fuse with liposomes under mildly acidic conditions, indicating that the reduced infectivity of the virus mutants on BHK-21 cells lies at the level of the fusion process. We suggest that the membrane fusion process of deglycosylated SIN mutants is affected by subtle changes in the conformation of the viral spike proteins, influencing the stability of the E2/E1 heterodimer. Membrane fusion of Sindbis virus glycosylation mutants 109
Health-Related and Motor Performance-Related Fitness and Physical Activity Among Youth With Cystic Fibrosis

Little is known about motor competence and the longitudinal development of motor performance among youth with cystic fibrosis (CF). In this study, we assessed aspects of motor performance in different age groups of young patients with CF and compared them with a healthy reference group of same aged children. We also examined the development of motor performance among different age groups of these children with CF, using The Deutscher Motorik Test (DMT) to assess attributes of health-related and motor performance-related fitness. We used an incremental ergometer cycle test to determine maximal exercise capacity (expressed as peak workload). We evaluated and recorded habitual physical activity (PA) as measured by the number of steps per day and the time spent in different PA intensities (expressed in metabolic equivalents). In total, 31 children and adolescents with CF agreed to participate (13 girls,18 boys) aged 6–17 years (M = 11.3, SD =3.3 years); they had a mean one second forced expiratory volume (expressed as a percentage of predicted value [% pred]) of 87.2% (SD = 22.3%). We found their values of health-related and motor performance-related fitness to be significantly lower (p < 0.05) than those of their healthy peer participants. In contrast to the reference group, participants with CF up to 14 years of age showed a linear improvement in these values and in their PA, followed by a plateau or even a nonsignificant decrease after age 14. These findings have important implications for the development and prescription of exercise programs for children with CF. Besides aerobic and strength exercises, we recommend that neuromuscular training be integrated into exercise programs to improve the coordinative abilities of youth with CF. More attention should be paid to vulnerable older adolescents to ensure their long-term motivation to maintain exercise participation.
Meta-analysis: association of homocysteine with recurrent spontaneous abortion

ABSTRACT We analyzed the differences in serum homocysteine levels between patients with a history of recurrent spontaneous abortion (RSA) and those who had not experienced pregnancy-related complications. To this end, we retrieved literature and data on the association of RSA and serum homocysteine levels published before September 1st 2019 from the PubMed, EMBASE, China National Knowledge Infrastructure, and Wanfang databases. We further narrowed our literature review by focusing on peer-reviewed and full-text literature reporting on studies that used similar research methods and provided raw data or means and standard deviations while reporting results. Utilizing Stata 12.0 for a combined statistical analysis of the data, we assessed the quality of the included literature using the Newcastle Ottawa Scale. Patients who experienced RSA had higher serum homocysteine levels than the controls, with the difference being statistically significant (p < .05). High serum homocysteine levels may be an important risk factor for RSA, indicating that homocysteine may be useful as a noninvasive marker for the diagnosis of recurrent abortions.
Cognition, hallucination severity and hallucination-specific insight in neurodegenerative disorders and eye disease

ABSTRACT Introduction: Hallucinations occur across neurodegenerative disorders, with increasing severity, poorer cognition and impaired hallucination-specific insight associated with worse outcomes and faster disease progression. It remains unclear how changes in cognition, temporal aspects of hallucinations, hallucination-specific insight and distress relate to each other. Methods: Extant samples of patients experiencing visual hallucinations were included in the analyses: Parkinson’s Disease (n = 103), Parkinson’s Disease Dementia (n = 41), Dementia with Lewy Bodies (n = 27) and Eye Disease (n = 113). We explored the relationship between factors of interest with Spearman’s correlations and random-effect linear models. Results: Spearman’s correlation analyses at the whole-group level showed that higher hallucination-specific insight was related to higher MMSE score (r s = 0.39, p < 0.001) and less severe hallucinations (r s = −0.28, p < .01). Linear mixed-models controlling for diagnostic group showed that insight was related to higher MMSE (p < .001), to hallucination severity (p = 0.003), and to VH duration (p = 0.04). Interestingly, insight was linked to the distress component but not the frequency component of severity. No significant relationship was found between MMSE and hallucination severity in these analyses. Conclusion: Our findings highlight the importance of hallucination-specific insight, distress and duration across groups. A better understanding of the role these factors play in VH may help with the development of future therapeutic interventions trans-diagnostically.
Forecasting the Evolution of North Atlantic Hurricanes: A Deep Learning Approach

Accurate prediction of storm evolution from genesis onwards may be of great importance considering that billions of dollars worth of property damage and numerous casualties are inﬂicted each year all over the globe. In the present work, two classes of Recurrent Neural Network (RNN) models for predicting storm-eye trajectory have been developed. These models are trained on input features available in or derived from the North Atlantic hurricane database maintained by the National Hurricane Center (NHC). The models utilize historical data to estimate probabilities of storms passing through any location. Furthermore, inputs to the models are such so that the model development methodology is applicable to any oceanic basin in any part of the globe. Model forecasting errors have been analyzed in detail. The error analysis shows that the Many-To-Many class of models are appropriate for long-term forecasting while Many-To-One prediction models perform comparably well for 6 − hr predictions. Application of these models to predicting more than 40 test storms in the North Atlantic basin shows that, at least for forecasts of up to 12 hours, they outperform all data-based storm trajectory prediction models cited in the paper. Apart from providing very fast predictions, the RNN models require less information than models used in current practice. Because the models presented herein emulate the trajectory trends of historical storms, they are currently being used for the simulation of synthetic storm tracks and features and the subsequent estimation of extreme wind speeds with various mean recurrence intervals.
Sliced Iterative Normalizing Flows SUPPLEMENTARY DOCUMENT

= max -K-SWp(p1, p2) + max -K-SWp(p2, p3), (S1) where the first inequality comes from the triangle inequality of Wasserstein distance, and the second inequality follows Minkowski inequality. Therefore max -K-SWp satisfies the triangle inequality. Now we prove the identity of indiscernibles. For any probability measures μ1 and μ2 in Pp(Ω) with probability density function p1 and p2, let θ̂ = arg maxθ∈Sd−1 Wp((Rp1)(·, θ), (Rp2)(·, θ)), and {θ∗ 1 , · · · , θ∗ K} = arg max{θ1,··· ,θK} orthonormal ( 1 K ∑K k=1W p p ((Rp1)(·, θk), (Rp2)(·, θk)) ) 1 p , we have
Comparison of some traffic models for ATM performance studies

We investigate the performance of an ATM switch as a function of the traffic model on its input lines. Three models are considered: (1) The traffic on each input line is described by a discrete time process which explicitly incorporates periodicities in the packet streams. (2) Each input line is modeled by a continuous time four-state Markov modulated Poisson Process (MMPP). (3) The traffic aggregated over all input lines is represented by a two-state MMPP. Although these models are directly comparable in the sense that many of their statistical characteristics agree, their microscopic features differ in the way packets are generated during bursts. The simulation results suggest that (1) When compared with the discrete model, the four-state per line MMPP performs reasonably well; while slightly overestimating cell loss probabilities, the four-state MMPP reacts to changes in the model parameters exactly the same way as the discrete model. Their steady-state queue length distributions differ only in the tails, irrespective of the parameter values. (2) The two-state MMPP model can give significantly different cell loss estimates and queue length distributions as compared to the discrete model.
An organismic inspired strategy for adaptive control

We focus on the connection between internal processes of regulation and their inﬂuence in the formation of behavioral patterns, and how they serve to provide a basis from where a biological organism derives a relation of value with its own activity. We touch on ongoing work exploring simulation modelling approaches to try to understand more about the issues required to overcome in order to capture the circularity involved in these processes, with particular attention to learning and sequential decision making formalisms of the perception-action loop, such as reinforcement learning and active inference. We describe an experimental setup where an agent learns to act in order to maintain the set of constraints that emerge and unfold as a consequence of the particular niche it inhabits.
Production of a Potentially Probiotic Product for Animal Feed and Evaluation of Some of Its Probiotic Properties

Nowadays, probiotics have been proposed for substituting antibiotics in animal feed since the European Union banned the latter compounds in 2006 to avoid serious side effects on human health. Therefore, this work aimed to produce a probiotic product for use in animal feed by fed-batch fermentation of whey with a combination of kefir grains, AGK1, and the fermented whole milk used to activate these kefir grains. The probiotic culture obtained was characterized by high levels of biomass (8.03 g/L), total viability (3.6 × 108 CFU/mL) and antibacterial activity (28.26 Activity Units/mL). Some probiotic properties of the probiotic culture were investigated in vitro, including its survival at low pH values, under simulated gastrointestinal conditions, after freezing in skim milk at −20 °C, and in the commercial feed during storage at room temperature. The viable cells of lactic and acetic acid bacteria and yeasts exhibited higher tolerance to acidic pH and simulated gastrointestinal conditions when the cells were protected with skim milk and piglet feed, compared with washed cells. The results indicated the feasibility of producing a probiotic product at a low cost with a potential application in animal feed.
Technical note: Quality assessment of ozone reanalysis products over subarctic Europe for biome modeling and ozone risk mapping

Abstract. We assess the quality of regional or global ozone reanalysis data for biome modeling and ozone (O3) risk mapping over subarctic Europe where monitoring is sparse. Reanalysis data can be subject to systematic errors originating from, e.g., quality of assimilated data, distribution and strength of precursor sources, incomprehensive atmospheric chemistry or land-atmosphere exchange, and spatiotemporal resolution. Here, we evaluate three selected global and regional ozone reanalysis products. Our analysis suggests that global reanalysis products do not reproduce observed ground-level ozone well in the subarctic region. Only the Copernicus Atmosphere Monitoring Service Regional Air Quality (CAMSRAQ) reanalysis ensemble sufficiently captures the observed seasonal cycle. We computed the root mean square error (RMSE) by season. The RMSE variation between (2.6–6.6) ppb suggests inherent challenges even for the best reanalysis product (CAMSRAQ). O3 concentrations in the region are systematically underestimated by (2–6) ppb compared to the tropospheric background ozone concentrations derived from observations. Furthermore, we explore the suitability of the CAMSRAQ for gap-filling at one site in northern Norway with a long-term record but not belonging to the observational network. We devise a reconstruction method based on Reynolds decomposition and adhere to recommendations by the United Nations Economic Commission for Europe (UNECE) Long Range Transboundary Air Pollution (LRTAP) convention. The thus reconstructed data for two weeks in July 2018 are compared with CAMSRAQ evaluated at the nearest neighboring grid point. Our reconstruction method performs better (78 % accuracy) than CAMSRAQ (73 % accuracy) but diurnal extremes are underestimated by both.
Epistemic Rules

According to a very natural picture of rational belief, we aim to believe only what is true. However, as Bernard Williams used to say, the world doesn't just inscribe itself onto our minds. Rather, we have to try to figure out what is true from the evidence available to us. To do this, we rely on a set of epistemic rules that tell us in some general way what it would be most rational to believe under various epistemic circumstances. We reason about what to believe; and we do so by relying on a set of rules. '
Rainfall-runoff relationships at event scale in western Mediterranean ephemeral streams

Abstract. Ephemeral streams are highly dependent on rainfall and terrain characteristics and, therefore, very sensitive to minor changes in these environments. Western Mediterranean area exhibits a highly irregular precipitation regime with a great variety of rainfall events driving the flow generation on intermittent watercourses, and future climate change scenarios depict a lower magnitude and higher intensity of precipitation in this area, potentially leading to severe changes in flows. We explored the rainfall-runoff relationships in two semiarid watersheds in southern Spain (Algeciras and Upper Mula) to model the different types of rainfall events required to generate new flow in both intermittent streams. We used a nonlinear approach through Generalized Additive Models at event scale in terms of magnitude, duration, and intensity, contextualizing resulting thresholds in a long-term perspective through the calculation of return periods. Results showed that the average ~ 1.2-day and <1.5 mm event was not enough to create new flows. At least a 4-day event ranging from 4 to 20 mm, depending on the watershed was needed to ensure new flow at a high probability (95 %). While these thresholds represented low return periods (from 4 to 10 years), the great irregularity of annual precipitation and rainfall characteristics, makes prediction highly uncertain. Almost a third part of the rainfall events resulted in similar or lower flow than previous day, emphasizing the importance of lithological and terrain characteristics that lead to differences in flow generation between the watersheds.