Miracle-dz/UniFAR-FTUs huggingface dataset:
Policy Gradient Reinforcement Learning Without Regret

This thesis consists of two independent projects, each contributing to a central goal of artificial intelligence research: to build computer systems that are capable of performing tasks and solving problems without problemspecific direction from us, their designers. I focus on two formal learning problems that have a strong mathematical grounding. Many real-world learning problems can be cast as instances of one of these two problems. Whenever our translation from the real to the formal accurately captures the character of the problem, then the mathematical arguments we make about algorithms in the formal setting will approximately hold in the realworld as well. The first project focuses on an open question in the theory of policy gradient reinforcement learning methods. These methods learn by trial and error and decide whether a trial was good or bad by comparing its outcome to a given baseline. The baseline has no impact on the formal asymptotic guarantees for policy gradient methods, but it does alter their finite-time behaviour. This immediately raises the question: which baseline should we use? I propose that the baseline should be chosen such that a certain estimate used internally by policy gradient methods has the smallest error. I prove that, under slightly idealistic assumptions, this baseline gives a good upper bound on the regret of policy gradient methods. I derive closed-form expressions for this baseline in terms of properties of the formal learning problem and the computer’s behaviour. The quantities appearing in the ii closed form expressions are often unknown, so I also propose two algorithms for estimating this baseline from only known quantities. Finally, I present an empirical comparison of commonly used baselines that demonstrates improved performance when using my proposed baseline. The second project focuses on a recently proposed class of formal learning problems that is in the intersection of two fields of computing science research: reinforcement learning and online learning. The considered problems are sometimes called online Markov decision processes, or Markov decision processes with changing rewards. The unique property of this class is that it assumes the computer’s environment is adversarial, as though it were playing a game against the computer. This is in contrast to the more common assumption that the environment’s behaviour is determined entirely by stochastic models. I propose three new algorithms for learning in Markov decision processes with changing rewards under various conditions. I prove theoretical performance guarantees for each algorithm that either complement or improve the best existing results and that often hold even under weaker assumptions. This comes at the cost of increased (but still polynomial) computational complexity. Finally, in the development and analysis of these algorithms, it was necessary to analyze an approximate version of a well-known optimization algorithm called online mirror ascent. To the best of my knowledge, this is the first rigorous analysis of this algorithm and it is of independent interest. iii
Level set incorporated with an improved MRF model for unsupervised change detection for satellite images

ABSTRACT This study proposes the use of a level set incorporated with an improved Markov random field (MRF) model in unsupervised change detection for satellite images. MRF provides a means of modelling the spatial contextual information in the level set, and an edge indicator function is introduced into the MRF model to control the contribution of local information in the boundary areas to change detection. On the basis of the improved MRF model, local label relationships and edge information are considered in the level set energy functional to conduct a novel local term and attract the contours into desired objects. By merging the novel energy term, the proposed approach not only reduces noise but also obtains accurate outlines of the changed regions. Experimental results obtained with Landsat 7 Enhanced Thematic Mapper Plus and SPOT 5 data sets confirm the superiority of the proposed model when compared with state-of-the-art change detection methods.
Low-frequency acoustic optimization of a double-wall panel by means of vibration absorbers

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
The Role of Edge-Based and Surface-Based Information in Incidental Category Learning: Evidence From Behavior and Event-Related Potentials

Although it has been demonstrated that edge-based information is more important than surface-based information in incidental category learning, it remains unclear how the two types of information play different roles in incidental category learning. To address this issue, the present study combined behavioral and event-related potential (ERP) techniques in an incidental category learning task in which the categories were defined by either edge- or surface-based features. The results from Experiment 1 showed that participants could simultaneously learn both edge- and surface-based information in incidental category learning, and importantly, there was a larger learning effect for the edge-based category than for the surface-based category. The behavioral results from Experiment 2 replicated those from Experiment 1, and the ERP results further revealed that the stimuli from the edge-based category elicited larger anterior and posterior P2 components than those from the surface-based category, whereas the stimuli from the surface-based category elicited larger anterior N1 and P3 components than those from the edge-based category. Taken together, the results suggest that, although surface-based information might attract more attention during feature detection, edge-based information plays more important roles in evaluating the relevance of information in making a decision in categorization.
Dihydro-Resveratrol Attenuates Oxidative Stress, Adipogenesis and Insulin Resistance in In Vitro Models and High-Fat Diet-Induced Mouse Model via AMPK Activation

Management of obesity has become a prevalent strategy for preventing the diseases closely integrated with excess body weight such as diabetes over the last half century. Searching for therapeutic agents acting on oxidative stress, adipogenesis and insulin resistance is considered as an efficient approach to control obesity-related diseases. The present study was designed to examine the in vitro and in vivo effects of dihydro-resveratrol (DR2), a naturally occurring compound from Dendrobium medicinal plants, on oxidative stress aggravation, adipogenesis, lipogenesis and insulin sensitivity. We utilized an in vitro 3T3-L1 adipocyte differentiation model to show that DR2 could reduce pre-adipocyte maturation by activation of AMPK/SIRT1 signaling proteins to inhibit p38MAPK proteins. With the use of in vitro oxidative-stress-induced hepatocytes and myoblasts models, DR2 was also shown to be able to reduce oxidative stress aggravation through mediation of Nrf2-related antioxidative cascade, reduce intracellular lipid accumulation through phosphorylation of ACC protein, reduce lipid peroxidation in hepatocytes and promote insulin sensitivity via activation of AKT protein in the insulin-resistant HepG2 cells and C2C12 cells. The effects of DR2 on adipogenesis, lipid accumulation, insulin resistance and blood glucose clearance were further demonstrated in the high-fat diet-induced obesity mouse model. Our in vitro and in vivo studies determined that DR2 could contain therapeutic potential for the treatment of obesity and type 2 diabetes.
Data-Driven Deterministic Symbolic Regression of Nonlinear Stress-Strain Relation for RANS Turbulence Modelling

This work presents developments towards a deterministic symbolic regression method to derive algebraic Reynolds-stress models for the Reynolds-Averaged Navier-Stokes (RANS) equations. The models are written as tensor polynomials, for which optimal coe cients are found using Bayesian inversion. These coe cient fields are the targets for the symbolic regression. A method is presented based on a regularisation strategy in order to promote sparsity of the inferred models and is applied to high-fidelity data. By being data-driven the method reduces the assumptions commonly made in the process of model development in order to increase the predictive fidelity of algebraic models.
Adaptive Collision-Free Trajectory Tracking Control for String Stable Bidirectional Platoons

Autonomous vehicle (AV) platoons, especially those with the bidirectional communication topology, have significant practical value, as they not only increase link capacity and reduce vehicle energy consumption, but also reduce the consumption of communication resources. Small gaps between AVs in a platoon easily lead to emergency braking or even collisions between consecutive AVs. This paper applies barrier Lyapunov functions to collision avoidance between AVs in a bidirectional platoon during trajectory tracking. Based on backstepping technique, an adaptive collision-free platoon trajectory tracking control algorithm is developed to distributedly design control laws for each AV in the platoon. The control algorithm does not need to introduce additional car-following models to simulate AV driving, and only needs to integrate the position trajectories of consecutive AVs to avoid inter-vehicle collisions. Two sign functions are introduced into the control laws of each AV to ensure strong string stability for bidirectional AV platoons. Moreover, uncertainties and external disturbances in vehicle motion are effectively compensated by introducing adaptation laws. Strong string stability is rigorously proved. CarSIM-based comparison simulations verify the effectiveness of the proposed control algorithm in avoiding inter-vehicle collisions, compensating for uncertainties in vehicle motion, and suppressing the amplification of spacing errors along the platoon.
Entrepreneurial intention and sustainability: an analysis through bibliometric networks

The aim of this paper was to analyze international scientific production on entrepreneurial intention and sustainability. Bibliometric research was carried out according to bibliometric laws (Lotka, Bradford and Zipf). In addition, content analysis was used to identify the main methodological approaches adopted. From the 76 documents analyzed, emerging topics related to entrepreneurial intention and sustainability were found: entrepreneurial education and gender; sustainable practices; innovation and personality traits; intention, sustainable entrepreneurship, and social entrepreneurship. These results suggest the development of research that addresses the context of social, economic and sustainable entrepreneurship. This research is relevant for providing reflections and knowledge for future research in the field of entrepreneurship, especially in relation to the alignment of entrepreneurial intention and sustainability.
Towards high precision internal camera calibration

This dissertation focuses on internal camera calibration and, especially, on its high-precision aspects. Two main threads are followed and examined: lens chromatic aberration correction and estimation of camera intrinsic parameters. For the chromatic aberration problem, we follow a path of digital post-processing of the image in order to get rid from the color artefacts caused by dispersion phenomena of the camera lens system, leading to a noticeable color channels misalignment. In this context, the main idea is to search for a more general correction model to realign color channels than what is commonly used - different variations of radial polynomial. The latter may not be general enough to ensure stable correction for all types of cameras. Combined with an accurate detection of pattern keypoints, the most precise chromatic aberration correction is achieved by using a polynomial model, which is able to capture physical nature of color channels misalignment. Our keypoint detection yields an accuracy up to 0.05 pixels, and our experiments show its high resistance to noise and blur. Our aberration correction method, as opposed to existing software, demonstrates a final geometrical residual of less than 0.1 pixels, which is at the limit of perception by human vision. When referring to camera intrinsics calculation, the question is how to avoid residual error compensation which is inherent for global calibration methods, the main principle of which is to estimate all camera parameters simultaneously - the bundle adjustment. Detachment of the lens distortion from camera intrinsics becomes possible when the former is compensated separately, in advance. This can be done by means of the recently developed calibration harp, which captures distortion field by using the straightness measure of tightened strings in different orientations. Another difficulty, given a distortion-compensated calibration image, is how to eliminate a perspective bias. The perspective bias occurs when using centers of circular targets as keypoints, and it gets more amplified with increase of view angle. In order to avoid modelling each circle by a conic function, we rather incorporate conic affine transformation function into the minimization procedure for homography estimation. Our experiments show that separate elimination of distortion and perspective bias is effective and more stable for camera's intrinsics estimation than global calibration method
Análise Sistemática e Bibliométrica da Literatura sobre Violação Gramatical nas Demonstrações Contábeis

Objetivo: Este estudo apresenta uma revisão sistemática e bibliométrica da literatura relacionada à violação gramatical nas demonstrações contábeis e aos seus efeitos no mercado de capitais. Conforme estudos anteriores, tem-se como base conceitual que a violação de regras gramaticais possui associação com a qualidade das demonstrações contábeis. Método: A metodologia adotada utilizou dois caminhos alternativos e complementares: i) revisão sistemática, em que a proxy de violação gramatical é apresentada como uma ferramenta útil, capaz de medir a qualidade das informações contábeis, por meio da análise textual das notas explicativas das demonstrações contábeis; e ii) análise bibliométrica do tema na base de periódicos listados na Web of Science, na Biblioteca Bibliometrix do software R. Resultados e Contribuições: A análise profunda da literatura pela revisão sistemática e, ao mesmo tempo, ampla pela bibliometria permite que este estudo sirva de base conceitual para trabalhos futuros. Ainda se discutem potenciais rumos na pesquisa contábil sobre o tema, desenvolvido fortemente nos Estados Unidos e ainda pouco explorado no Brasil e em outros países. Por fim, as análises sugerem que a estrutura de disponibilização de dados ao mercado pode ser um fator de restrição para maior aplicação da proxy.
PREDICTORS OF CHARACTER STRENGTHS AMONG YOUNG ADULTS AND ADULTS IN BANDUNG

This study aims to measure the contribution of traits and productive parenting patterns as predictors of character strengths. Features were assumed as a natural or an innate factor in individuals, while effective parenting patterns were considered a nurture or an external factor. In this study, the trait concept is studied using the theory of Five-Factor Personality from Costa and McRae; the productive parenting pattern is studied using Baumrind's approach, and the idea of character strengths is explored using the theory proposed by Peterson and Seligman. The validity and reliability of all questionnaires are examined by construct validity and internal consistency. Respondents were 247 individuals with a young adult to the adult developmental range. The result of regression analysis shows that traits and productive parenting patterns significantly contributed to establishing all character strengths with the value of R2 from 0.226 to 0.562. This finding concludes that character strengths will be well developed in individuals if the innate factor (traits) are optimized with productive parenting (with acceptance and warmth and clear and consistent rules from the parents).
Clinicopathological Spectrum of Thyroid Swelling: A Single Institutional Prospective Study from Northeast India

Purpose: The pathologic lesions of the thyroid gland are manifested by varied morphologies. The current study aims to report the clinicopathologic features of patients with thyroid swelling from a tertiary centre of North-East India with particular emphasis on histopathological examination (HPE). Method: It is a prospective hospital-based study. Fifty-three resected specimens of thyroid surgery were included in the study. All the specimens were undertaken for HPE, and the malignant cases were subjected to immunohistochemistry (IHC). Results: Female preponderance (84.9%) of the disease was found with a maximum incidence between the age group younger than 40 (60.37%). 86.8% of cases were benign on HPE; colloid goitre reported a maximum number (62.3%). Only seven cases were found malignant, and six out of them were papillary carcinoma. All the six papillary carcinomas and one follicular carcinoma showed immunoreactivity of tumour cells to TTF 1, CK 7 and Thyroglobulin.Conclusion: Asymptomatic thyroid swelling is a prevalent entity, and the incidence of malignancy in the current study is 13.2%. Identifying malignant thyroid swelling out of the common benign or apparently benign goitres has always been problematic, and preoperative fine-needle aspiration cytology followed by HPE and IHC plays a significant role.
Evaluation of eight bambara groundnut (Vigna subterranea (L.) Verdcourt) accessions for agronomic characters and proximate composition in Uyo, Akwa Ibom State, Nigeria

Bambara groundnut (Vigna subterranea [L.] Verdc.) is a valuable but underutilised legume crop grown in sub-Saharan Africa. The objective of this study was to evaluate the yield and yield components of eight Bambara groundnut accessions obtained from the National Root Crops Research Institute in Umudike, Nigeria. The experiment was conducted at the Teaching and Research farm of the University of Uyo, Uyo Southern Nigeria during the 2021 cropping seasons, using a randomised complete block design with three replications. Growth, yield and nutritional parameters were collected and subjected to analysis of variance, correlation and principal component analysis. Plant height, number of leaves, and leaf area were all significantly different (P≤0.05) among the accessions three months after planting. Number of pods per plant, seed weight, and 100-seed weight differed significantly (P≤0.05). Caly PSC (2853 kg/ha), Caly SK 46 (2803 kg/ha), Zeina (2538 kg/ha), and BNT (2488 kg/ha) were the top yielders among the accessions. Yields of Bambara groundnut differ significantly (P≤0.05) between accessions studied, ranging from 1624.67 kg/ha to 2853.33 kg/ha. Principal component (PC) analysis identified eight influential components, two of which, PC1 and PC3, contributed 27% and 17% of the total variation, respectively. In this study, the correlation analysis revealed that plant height and petiole length, plant height and number of seeds per plot were all negatively correlated. The leaf area and the number of pods per plot, the number of seeds per pod and the fibre content, the leaf area and seed weight, and the plant height and seed weight were all noted positively correlated. The nutritive value of the eight Bambara groundnut accessions varied significantly (P≤0.05). The mean protein levels of eight Bambara nut accessions studied ranged from 18.82 to 20.39%. Findings from this study clearly indicate that Bambara groundnut is suitable for production in Uyo, Akwa Ibom State. Thus, high yielding accessions identified in this study are recommended for increased production in Akwa Ibom State of Nigeria.
Scutellaria baicalensis Georgi and Their Natural Flavonoid Compounds in the Treatment of Ovarian Cancer: A Review

Ovarian cancer (OC) is one of the most common types of cancer in women with a high mortality rate, and the treatment of OC is prone to high recurrence rates and side effects. Scutellaria baicalensis (SB) is a herbal medicine with good anti-cancer activity, and several studies have shown that SB and its flavonoids have some anti-OC properties. This paper elucidated the common pathogenesis of OC, including cell proliferation and cell cycle regulation, cell invasion and metastasis, apoptosis and autophagy, drug resistance and angiogenesis. The mechanisms of SB and its flavonoids, wogonin, baicalein, baicalin, Oroxylin A, and scutellarein, in the treatment of OC, are revealed, such as wogonin inhibits proliferation, induces apoptosis, inhibits invasion and metastasis, and increases the cytotoxicity of the drug. Baicalein also inhibits vascular endothelial growth factor (VEGF) expression etc. Analyzing their advantages and disadvantages in treating OC provides a new perspective on the role of SB and its flavonoids in OC treatment. It serves as a resource for future OC research and development.
Green IoT Framework for Deep Forest Surveillance

Forests are one of the essential natural resources required to preserve the planet’s ecological equilibrium. This paper offers a technique for creating an extensive environmental monitoring system. Along with that, it keeps an check over illegal activities taking place nearby, such as invasion into reserved forest areas and natural calamities like forest fire. The datas will be captured, classified, and reported to the official by the device. This can be accomplished by interfacing various sensors like an flame sensor, an ultrasonic sensor, and a DHT sensor with Arduino Nano board. Additionally, it offers guidance on safety measures and has the capacity to forecast natural calamities. Results are compared to numerous cutting-edge methods for figuring out total performance. The concern of finite energy supplies, however, affects the sensor nodes used in such networks. Keeping this concern in mind, energy can be saved to a greater extend by implementing green IoT technologies. The continual changes in temperature and humidity may be observed using the Adafruit cloud platform. The internet connectivity that connects Arduino to the Adafruit platform is provided by Node MCU. Wi-Fi is the transmission technique used in this. Real-time application of Lora WAN technology is possible to link sensors to cloud platforms.