Miracle-dz/UniFAR-FTUs huggingface dataset:
2020). Adaptive Optimal Control via Continuous-Time Q-Learning for Unknown Nonlinear Affine Systems. In 2019 IEEE 58th

—This paper proposes two novel adaptive optimal control algorithms for continuous-time nonlinear afﬁne systems based on reinforcement learning: i) generalised policy iteration (GPI) and ii) Q-learning. As a result, the a priori knowledge of the system drift f ( x ) is not needed via GPI, which gives us a partially model-free and online solution. We then for the ﬁrst time extend the idea of Q-learning to the nonlinear continuous-time optimal control problem in a noniterative manner. This leads to a completely model-free method where neither the system drift f ( x ) nor the input gain g ( x ) is needed. For both methods, the adaptive critic and actor are continuously and simultaneously updating each other without iterative steps, which effectively avoids the hybrid structure and the need for an initial stabilising control policy. Moreover, ﬁnite-time convergence is guaranteed by using a sliding mode technique in the new adaptive approach, where the persistent excitation (PE) condition can be directly veriﬁed online. We also prove the overall Lyapunov stability and demonstrate the effectiveness of the proposed algorithms using numerical examples.
Computational Cognitive Neuroscience

Cognitive neuroscience was born in the 1990’s amid a technological explosion that produced powerful new methods for noninvasively studying the human brain, including functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS). These exciting new technologies revolutionized the scientific study of the mind, giving unprecedented observability into the neural processes that mediate human thought and action. With the new data came a growing need for new kinds of theories that could simultaneously account for the behavioral data that are the bread and butter of traditional mathematical psychology as well as the brain-related measures coming from the new technologies. Computational Cognitive Neuroscience (CCN) was created to fill this void. CCN evolved from computational neuroscience on one side and connectionism, neural network theory, and machine learning on the other. Like computational neuroscience, CCN strives for neurobiological accuracy and like connectionism, a major goal is to account for behavior. But CCN is unique because most computational neuroscience models make no attempt to account for behavior and most connectionist models make no attempt to be biologically detailed. The biological detail included in CCN models adds many more constraints on the modeling process than more traditional approaches. As a result, two researchers independently modeling the same behavior are more likely to converge on highly similar models with this new approach, and for this reason the resulting models should have a permanence that is unusual with older approaches. Not surprisingly, a growing of number of researchers are now pursuing the endeavor of CCN modeling (e.g., Anderson, Fincham, Qin, & Stocco, 2008; Ashby & Helie, 2011; Frank, 2005; Hartley, Burgess, Lever, Cacucci, & O’Keefe, 2000; O’Reilly, Munakata, Frank, Hazy, et al., 2012).
ASPECTS OF THE RELATIONSHIP BETWEEN FIXED DENTAL PROSTHESES AND PERIODONTAL TISSUES

The health of periodontal tissues is the key to the long-term success of prosthetic restorations. A negative impact on it can have subgingival margins, an inaccurate marginal fit, materials that favor the accumulation of plaque and can suffer the corrosion process. All these characteristics can stimulate the appearance of a biological response, that can be translated by increased proinflammatory markers (interleukins, matrix metalloproteinases) in gingival crevicular fluid. The acquisition of the aspects that describe the relationship between the two elements mentioned above, could ensure a favorable prognosis of fixed prosthetic therapy.
MCMC-based time-lapse full-waveform inversion

In this study, we have proposed a Bayesian time-lapse full-waveform inversion (FWI) based on the Markov chain Monte Carlo (MCMC) algorithm and a new method to estimate the data error standard deviation for the time-lapse data according to its feature. To achieve the MCMC-based time-lapse FWI, we have employed the inversion strategies including the double-difference time-lapse FWI (DDFWI), the time-domain multi-source data, the local-updating target-oriented inversion, calculating model covariance with the adaptive Metropolis algorithm, and the new data error standard deviation estimation method. The MCMC algorithm applied is a random walk Metropolis-Hastings MCMC, a typical stochastic global optimization method. In the conventional deterministic optimization (DO) DDFWI containing a baseline inversion for the baseline model and a monitoring inversion for the monitoring model, both inversions are performed by the DO FWI. In the MCMC DDFWI proposed in this work, we keep the DO FWI for the baseline inversion but employ the MCMC algorithm for the monitoring inversion. And the ﬁnal time-lapse model is the difference between the inverted monitoring model and the baseline model. Synthetic data tests using a 2D acoustic model have demonstrated the feasibility of MCMC DDFWI on both time-lapse model inversion and uncertainty qualiﬁcation. We also have compared the MCMC DDFWI with the conventional DO DDFWI, which shows that the inverted average time-lapse model of MCMC DDFWI can provide the results with clearer edges of the nonzero time-lapse model change and fewer coherent errors.
The Outer Limits of the Continental Shelf in the Polar Regions

This chapter provides a brief overview of the obligations of states in relation to the definition of the continental shelf under article 76 of the law of the sea (LOS) convention. Next, it looks at how the commission on the limits of the continental shelf (CLCS) and the meeting of states parties to the LOS convention (SPLOS) has given further content to the rules contained in the LOS Convention. The chapter focuses on the Polar Regions. First of all, regional aspects of the implementation of article are considered. Then, the chapter looks at the cooperation between the states concerned in such issues as the gathering of data and the delimitation of bilateral boundaries. Finally, it discusses a number of issues that have come up in the consideration of the submissions of Australia, Norway and the Russian Federation by the CLCS. Keywords: CLCS; law of the sea (LOS) convention; Polar regions; SPLOS
Study of the treatment performance of the technique of Natural lagoons of the treatment plant for domestic wastewater of the city Azilal -MAROC-

In Morocco, like in all developing countries, sanitation and sewage treatment are one of the biggest environmental problems. The lack of public network for waste water treatment, the absence of control and of environmental sensitivity contribute to spreading diseases, degrading landscapes and contaminating surface as well as underground water. Wastewater is considered their main polluting factor.In Morocco, the treatment of wastewater by the natural lagoon is probably the most efficient solution both in economic terms and in terms of performance. The objective of this work is to study the physical, chemical and bacteriological parameters of raw and treated water from the station.For this, we opted carried out a series of measures: For the physicochemical parameters as temperature, pH, salinity, turbidity, the redox potential, electrical conductivity (EC), biochemical oxygen demand BOD5, chemical oxygen demand (COD), suspended solids SS, and O2 dissolved oxygen and bacteriological parameters as fecal coliforms (FC) and total coliforms (CT). Data analysis showed that the treatment efficiency of the station reached respectively 15,86%, 55,34%, 55,58% of the SS, COD and BOD5 which does not meet the discharge standards. The bacteriological analysis showed faecal pollution of human origin and performance of abatement of fecal germs can be deduced that the treatment adopted by facultative ponds far beyond the values required by Moroccan or European standards.
HYDROGEN PRODUCTION POTENTIAL OF WHEAT STRAW

In the last decade, most research has tended to focus on developing an economical biofuels production process. Much emphasis is being given to the production of hydrogen from lignocellulosic biomass. Among the agricultural crop residues, wheat straw is the largest biomass feedstock in Europe and the second largest in the world after rice straw (Kim and Dale, 2004). Wheat (Triticum aestivum L.) is the world’s most widely grown crop, cultivated in over 115 nations under a wide range of environmental conditions. About 21% of the world’s food depends on the wheat crop and its global production needs to be increased to satisfy the growing demand of human consumption (Ortiz et al., 2008) therefore, wheat straw would serve as a great potential feedstock for production of hydrogen in future. The recent study was done with the aim of selection of popular wheat varieties mostly grown in northern and central India which has not been studied before with respect to hydrogen production potential. Characterization of the wheat varieties on the basis of fibre, sugar content and other chemical constituents, showed it as a potential feedstock for hydrogen production. Pretreatment is considered the most important step in determining the cost and success of the process of enzymatic saccharification of lignocellulosic biomass. The effectiveness of pretreatment using microwave assisted dilute acid to produce maximum fermentable sugars in terms of total soluble sugar from the straw was investigated. The results showed that microwave assisted acid pretreatment is a promising approach to enhance fermentable sugar yields. The wheat variety HD 2967 was found to be the best followed by WR 544, C 306 and K 68 with respect to the release of soluble sugars. HD 2967 had significantly higher value in terms of sugar than other varieties. The temperature 160 C and time period 10 min was best for sugar recovery from wheat straw.
It’s Nothing but a Deepfake! The Effects of Misinformation and Deepfake Labels Delegitimizing an Authentic Political Speech

Mis-and disinformation labels are increasingly weaponized and used as delegitimizing accusations targeted at mainstream media and political opponents. To better understand how such accusations can affect the credibility of real information and policy preferences, we conducted a two-wave panel experiment ( N wave2 = 788) to assess the longer-term effect of delegitimizing labels targeting an authentic video message. We find that exposure to an accusation of misinformation or disinformation lowered the perceived credibility of the video but did not affect policy preferences related to the content of the video. Furthermore, more extreme disinformation accusations were perceived as less credible than milder misinformation labels. The effects lasted over a period of three days and still occurred when there was a delay in the label attribution. These findings indicate that while mis-and disinformation labels might make authentic content less credible, they are themselves not always deemed credible and are less likely to change substantive policy preferences.
Nanobodies Raised against Monomeric alpha-Synuclein Distinguish between Fibrils at Different Maturation Stages

Nanobodies are single-domain fragments of camelid antibodies that are emerging as versatile tools in biotechnology. We describe here the interactions of a specific nanobody, NbSyn87, with the monomeric and fibrillar forms of α -synuclein ( α Syn), a 140-residue protein whose aggregation is associated with Parkinson's disease. We have characterized these interactions using a range of biophysical techniques, including nuclear magnetic resonance and circular dichroism spectroscopy, isothermal titration calorimetry and quartz crystal microbalance measurements. In addition, we have compared the results with those
Formant tracking in Sound Tools eXtended (STx) 5.0

In this paper, we introduce the new formant tracker of Sound Tools eXtended (STx) version 5.0, an acoustic speech and sound processing application. Formants occur at the resonant frequencies of the human vocal tract and can be correlated with articulatory features such as tongue height and backness. They are indispensable for acoustic phonetic analysis since they allow for an iden-tiﬁcation of vowel sounds. Our formant tracker uses dynamic programming to ﬁnd the best path among formant candidates derived from Linear Predictive Coding (LPC) analysis. The transition and observation costs in the search algorithm are optimized with manually corrected formant tracks. The new version of STx 5.0 also has an interface to WebMAUS that allows automatic phonetic transcription and segmentation of speech for different languages. Furthermore STx 5.0 contains an integrated, simpliﬁed and compact GUI, designed for speech analysis for phoneticians, linguists, psychologists, and researchers in related ﬁelds.
THE CYTOTOXIC AND APOPTOTIC EFFECTS OF

Cancer has become one of the most critical health issues, with an increasing incidence and mortality in recent years. Meanwhile, many studies are carried out on discovering new compounds which reflects effective results on cancer cells. Therefore, this study aimed to examine the cytotoxic, anti-proliferative and apoptotic effects on cancer cell lines by using the extract obtained from Uludağ Fir ( Abies nordmanniana subsp. bornmülleriana Mattf ) resin. Uludağ Fir resin extract was obtained by applying the methanol extraction method. In our study, fibroblast cell L-929, human metastatic prostate cancer PC-3 and human metastatic prostate cancer DU-145 cell lines were cultured in RPMI-1640 medium. The effects of the resin extract concentrations on the viability of the cells were determined with the Muse™ Cell Count & Viability test. In addition, apoptosis and cell cycle phases of the cells were determined using the Muse™ cell analyzer. Finally, RT-qPCR analysis was performed to determine the resin extract effect on pro-apoptotic and anti-apoptotic gene expression. The apoptotic effect of Uludağ Fir resin extract on cancer cell lines was significantly higher in contrast to the healthy normal cells. In addition, it was determined that Uludağ Fir extract caused G0/G1 cell cycle arrest in cancer cells. In RT-qPCR analysis, the pro-apoptotic gene expression levels were apparently increased in cancer cells, which was followed by a decrease in the anti-apoptotic gene expression levels. All taken together, these results indicate that Uludağ Fir resin extract exerts two prong effects as it induces a cell cycle arrest and apoptotic pathway activation on human prostate cancer cell lines.
Superpixel Positional Encoding to Improve ViT-based Semantic Segmentation Models

In this paper, we present a novel superpixel-based positional encoding technique that combines Vision Transformer (ViT) features with superpixels priors to improve the performance of semantic segmentation architectures. Recently proposed ViT-based segmentation approaches employ a Transformer backbone and exploit self-attentive features as an input to a convolutional decoder, achieving state-of-the-art performance in dense prediction tasks. Our proposed technique is plug-and-play, model-agnostic, and operates by computing superpixels over the input image. It determines a positional encoding based on the centroids and shapes of the superpixels, and then unifies this semantic-aware information with the self-attentive features extracted by the ViT-based backbone. Our results demonstrate that this simple positional encoding strategy, when applied to the decoder of ViT-based architectures, leads to a significant improvement in performance without increasing the number of parameters and with negligible impact on the training time. We evaluate our approach on different backbones and architectures and observe a significant improvement in terms of mIoU on the ADE20K and Cityscapes datasets. Notably, our approach provides improved performance on classes with low occurrence in the dataset while mitigating overfitting on classes with higher representation, ensuring a good balance between generalization and specificity.
Enclaves as accelerators: learning lessons from GPU computing for designing efﬁcient runtimes for enclaves

Intel SGX enclaves is a novel technology that holds the promise to revolutionize the way secure and trustworthy applications are built. However, from the perspective of interaction with the rest of the system, some of the enclave’s characteristics are remarkably similar to the characteristics of traditional hardware accelerators, such as GPUs. For example, enclaves suffer from signiﬁcant invocation overheads, offer space-constrained private memory, and cannot directly invoke OS services such as network or ﬁle I/O. Over the course of GPU computing evolution there have been developed many techniques to improve the system performance and programmability. Our key observation is that the conceptual similarities between enclaves and accelerators may help building efﬁ-cient runtime support for enclaves by learning from the past experience with GPUs. We demonstrate this simple idea by implementing SGXIO, a simple yet powerful enhancement to the current SGX runtime which boosts the performance of I/O system calls from enclaves. SGXIO design is almost identical to the design of GPUfs and GPUnet [8, 4] systems for efﬁcient I/O services for GPU programs. Our pre-liminary evaluation shows that GXIO improves the performance of a simple network parameter server for distributed machine learning by up to 3.7 × . These promising results suggest new ways to design more efﬁcient runtime and system services for enclaves.
Use of electronic monitoring systems to optimize observer sampling protocols onboard French purse seiners of the Indian Ocean

Observer programs on tuna purse seine vessels are essential to collect information on bycatch and discards in order to monitor the impact of fisheries on populations and ecosystems. On-board observers estimate discards following a sampling protocol and sometimes extrapolation methods (based on the number of brailers or time) when counting exhaustively is not possible. However, these methods may be biased because brailers have different filling rates, which in turns results in a heterogeneous flow of discarded individuals during the sorting process, and may lead to biased estimates. Electronic Monitoring Systems (EMS) have been implemented since 2013 on French purse seiners to complement on-board observer programs on vessels that cannot embark observers. On-board cameras allow monitoring sorting operations continuously and monitoring the discard flow in time and space (upper vs lower deck). In this study, we used EMS « counts per minute » of discards from 5 vessels operating in the Indian Ocean to describe the general trends in sorting flow on the upper and lower decks. We analysed 50 FOB (Floating Objects) fishing sets with various sorting time and simulated different observer strategies (using bootstrap without resampling) on the total number of discarded individuals in order to optimize (i) the total sampling duration and (ii) the duration of sampling sequences. This analysis is detailed at the species level on the lower deck where the number of individuals and the level of identification were higher. We finally propose an optimized sampling strategy for evaluating discards that reduces both sampling time and estimation bias, and that can be applicable to both electronic and on-board observations.
EXOSC5 maintains cancer stem cell activity in endometrial cancer by regulating the NTN4/integrin β1 signalling axis

Endometrial carcinoma (EC) is a common type of uterine cancer in developed countries, originating from the uterine epithelium. The incidence rate of EC in Taiwan has doubled from 2005. Cancer stem cells (CSCs) are a subpopulation of cancer cells that have high tumorigenicity and play a crucial role in the malignant processes of cancer. Targeting molecules associated with CSCs is essential for effective cancer treatments. This study delves into the role of Exosome component 5 (EXOSC5) in EC. Data from The Cancer Genome Atlas suggests a correlation between high EXOSC5 mRNA expression and unfavorable EC prognosis. EXOSC5 knockdown diminished EC-CSC self-renewal and reduced expression of key cancer stemness proteins, including c-MYC and SOX2. Intriguingly, this knockdown significantly curtailed tumorigenicity and CSC frequency in EC tumor spheres. A mechanistic examination revealed a reduction in netrin4 (NTN4) levels in EXOSC5-depleted EC cells. Moreover, NTN4 treatment amplified EC cell CSC activity and, when secreted, NTN4 partnered with integrin β1, subsequently triggering the FAK/SRC axis to elevate c-MYC activity. A clear positive relation between EXOSC5 and NTN4 was evident in 93 EC tissues. In conclusion, EXOSC5 augments NTN4 expression, activating c-MYC via the integrin β1/FAK/SRC pathway, offering potential avenues for EC diagnosis and treatment.
LSAV: Lightweight source address validation in SDN to counteract IP spoofing-based DDoS attacks

: In this paper, we propose a design to detect and prevent IP spoofing-based distributed denial of service (DDoS) attacks on software-defined networks (SDNs). DDoS attacks are still one of the significant problems for internet service providers (ISPs) and individual users. These attacks can disrupt customer services by targeting the availability of the system, and in some cases, they can completely shut down the target infrastructure. Protecting the system against DDoS attacks is therefore crucial for ensuring the reliability and availability of internet services. To address this problem, we propose a lightweight source address validation (LSAV) framework that leverages the flexibility of SDN architecture in ISP networks and employs a lightweight filtering mechanism that considers the cost of operation to maintain high performance. Our setup for the proposed mechanism reflects client–server communication through an ISP SDN, and we use the entry points to eliminate malicious user requests targeting the systems. We then propose a novel algorithm on top of this setup to introduce a new and more eﬀicient approach to existing mitigation methodologies. In addition to filtering the traﬀic against IP spoofing-based DDoS attacks, LSAV also prioritizes low resource consumption and high performance in terms of delay and bandwidth. With this approach, we believe that ISPs can effectively defend against IP spoofing-based DDoS attacks while still preserving low resource consumption for the infrastructure and high-quality internet services for their customers.