Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-based UAV tracking using deep reinforcement learning with simulated data

In recent years, Unmanned Aerial Vehicles (UAVs) have seen significant technological advances, with a wide range of applications. However, their arbitrary uses continue to pose a great threat to public safety and privacy. This has sparked the interest of the research community, which is developing solutions based on Artificial Intelligence (AI) to detect and track in real time these unmanned flying objects in sensitive areas. In this paper, we propose a vision-based Deep Reinforcement Learning (DRL) algorithm to track drones in various simulated scenarios, within the Microsoft AirSim simulator. The proposed approach is promising and achieves high tracking accuracy in different realistic simulated environments. It allowed to process videos at high frame rates and achieved a mean average precision (mAP) above 80%.
Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models

This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.
Depth, coverage and spacing of the furrows in mechanized sugarcane plantation with planter and distributor in the day and night shifts

Mechanized planting, is one of the main operations in the sugarcane production cycle that directly affects the longevity of the shoots, the crop yield and the profitability of the crop. Thus, this study aims to evaluate the furrow depth, furrow coverage and furrows spacing in a mechanized sugarcane plantation with a planter and a distributor in the day and night shifts of operation, using as statistical analysis quality control tool. In both shifts were sampled 30 points, where was collect data of furrow depth, furrow coverage and furrows spacing. The quality indicators of mechanized sugarcane planting are not influenced by the day and night shifts, and present values considered satisfactory when carrying out mechanized planting with a planter or distributor.
ARV-471, an estrogen receptor (ER) PROTACdegrader, combined with palbociclib in advanced ER+/human epidermal growth factor receptor 2–negative (HER2-) breast cancer: Phase 1b cohort (part C) of a phase 1/2 study.

TPS1120 Background: ARV-471 is a novel, potent, orally bioavailable PROteolysis TArgeting Chimera (PROTAC) protein degrader that selectively targets the ER. In xenograft models, ARV-471 demonstrated substantially greater ER degradation and antitumor activity compared with the selective ER degrader fulvestrant. In the phase 1 dose escalation portion (Part A) of the first-in-human phase 1/2 study, ARV-471 monotherapy was well tolerated and showed antitumor activity in patients with ER+/HER2- locally advanced or metastatic breast cancer who had previously received endocrine therapy and a cyclin-dependent kinase (CDK)4/6 inhibitor; the clinical benefit rate (rate of confirmed complete or partial response or stable disease ≥24 weeks) was 40% (95% CI: 26%–56%) in 47 evaluable patients. The phase 2 VERITAC expansion cohort (Part B) is further evaluating ARV-471 monotherapy in this patient population. Palbociclib, a CDK4/6 inhibitor, plus fulvestrant is a standard treatment option for patients with ER+/HER2- breast cancer who have had disease progression on endocrine therapy. ARV-471 plus palbociclib resulted in substantially greater tumor growth inhibition in xenograft models compared with palbociclib plus fulvestrant, supporting further investigation of the ARV-471 plus palbociclib combination in patients with ER+ breast cancer. Here we describe Part C of the phase 1/2 study, which evaluates the safety and clinical activity of ARV-471 plus palbociclib in patients with breast cancer who previously received endocrine therapy. Methods: Eligible patients (aged ≥18 years) have histologically or cytologically confirmed ER+/HER2- advanced breast cancer and have received ≥1 prior endocrine therapy and ≤2 prior chemotherapy regimens for advanced disease; prior CDK4/6 inhibitor therapy is permitted. Patients with known symptomatic brain metastases requiring steroids are excluded. ARV-471 and palbociclib will be administered orally once daily in 28-day cycles; ARV-471 will be given continuously and palbociclib for 21 days followed by 7 days off treatment. Primary objectives are to evaluate the safety and tolerability of ARV-471 plus palbociclib and select the recommended phase 2 dose and schedule of the combination (based on the incidence of dose-limiting toxicities during the first cycle and the frequency and severity of adverse events and laboratory abnormalities). Secondary objectives are to assess preliminary antitumor activity of ARV-471 plus palbociclib (based on overall response rate per Response Evaluation Criteria in Solid Tumors v1.1, clinical benefit rate, progression-free survival, and duration of response) and pharmacokinetic parameters. Clinical trial information: NCT04072952.
Lower Expression of TWEAK is Associated with Poor Survival and Dysregulate TIICs in Lung Adenocarcinoma

Background Lung cancer remains the leading cause of cancer death worldwide, and the most subtype is lung adenocarcinoma (LUAD). Tumor-infiltrating immune cells (TIICs) greatly impact the prognosis of LUAD. Tumor necrosis factor–like weak inducer of apoptosis (TWEAK), signal via its receptor fibroblast growth factor-inducible 14 (Fn14), dysregulates immune cell recruitment within tumor environment, thus promoting the progression of autoimmune diseases and cancer. We aimed to explore its role in LUAD. Methods The expression level of TWEAK was explored in Tumor Immune Estimation Resource 2.0 (TIMER2.0) and Oncomine databases. The Tumor Immune Dysfunction and Exclusion (TIDE) and Lung Cancer Explorer (LCE) databases were applied to evaluate the survival in correlation to TWEAK expression. TIICs were assessed with TIMER2.0 and TIDE datasets. The expression of TWEAK protein was detected in LUAD cell lines and also in tissue samples from LUAD patients via western blotting or combination with immunochemistry. Results Our results showed that TWEAK was downregulated in LUAD tumors compared to normal tissues in TIMER2.0, Oncomine, cell lines, and clinical specimens. Poor survival was uncovered in lower TWEAK expression of LUAD patients in LCE (meta − HR = 0.84 [95% CI, 0.76-0.92]) and TCGA (Continuous Z = −1.97, p = 0.0486) and GSE13213@PRECOG (Continuous Z = −4.25, p = 2.12e − 5) in TIDE. Multiple tumor-infiltrating immune cells (TIICs) were found closely correlated with TWEAK expression in LUAD, especially hematopoietic stem cell (Rho = 0.505, p = 2.78e − 33), common lymphoid progenitor (Rho = −0.504, p = 3.79e − 33), and myeloid-derived suppressor cells (MDSCs) (Rho = −0.615, p = 1.36e − 52). Conclusion Lower level of TWEAK was linked with poor survival and aberrant recruitment and phenotype of TIICs in LUAD, which might motivate immune escape and weaken the effects of immunotherapy.
Conducting Hormonal, Biochemical and Serological Tests in Autoimmune Thyroid Patients

Background: One of the most frequent autoimmune illnesses affecting the thyroid gland is immunological thyroid disease. Antibodies against the enzyme thyroglobulin and thyroid-stimulating hormone receptors are produced by the immune system. Hashimoto's disease and Graves' disease are the two primary forms of autoimmune diseases. Methods: 91 blood samples were taken, including 71 for thyroid disease patients and 20 for healthy persons as control samples, from both sexes and of various ages. The tests were performed using a variety of methodologies. Results: Some of autoantibodies were found in varying quantities in 44 individuals. While TPO antibodies were found in 75% of 33 individuals, Anti-TG Ab was found in 72.72% of the patients, 15.90% in seven patients, and anti-TSHR Ab and antinuclear antibodies were found in 22.72% of the patients compared to the control samples. Any type of autoantibody, as well as the results of hormonal testing, revealed disease in a variety of individuals at varying rates. Because the patients were on therapy, the highest rate was 59.09% for thyroid-stimulating hormones. Chemical analyses revealed a sugar and fat imbalance with no discernible link. Interleukin 4 was detected in 4 individuals (9.09%), including one patient with autoimmune hypothyroidism (2.27%) and three patients in C3 18 with immunological hyperthyroidism (6.82%). The findings revealed an increase in complement molecule concentration in 40.90% of patients, including 15 patients with autoimmune hypothyroidism (34.09%) and 3 patients with immune thyroid gland C3 hyperactivity (6.71%). The statistical study revealed a P < 0.01 level of significance correlation between the presence of sugar and triglycerides and the complement molecule. Interleukin 4 and antibodies to receptors have a substantial correlation at the P < 0.01TSHR Ab level TSH. Conclusions: The C3 gland patients for thyroid peroxidase had the largest amount of antibodies, lipids, and sugar, according to the study. Interleukin-4 occurred in certain individuals who had antibodies to thyroid-stimulating hormone receptors, according to the research.
Invading the Occupied Niche: How a Parasitic Copepod of Introduced Oysters Can Expel a Congener From Native Mussels

In species introductions, non-native species are often confronted with new niches occupied by more specialized natives, and for introduced parasites this conflict can be amplified because they also face novel hosts. Despite these obstacles, invasions of introduced parasites occur frequently, but the mechanisms that facilitate parasite invasion success are only rarely explored. Here, we investigated how the parasitic copepod Mytilicola orientalis, that recently spilled over from its principal host - the Pacific oyster Crassostrea gigas, managed to invade the niche of blue mussel Mytilus edulis intestines, which is densely occupied by its specialist congener, Mytilicola intestinalis. From field observations demonstrating invasion dynamics in nature, we designed a series of experiments addressing potential mechanisms facilitating a successful occupation of the new niche. As expected the specialist M. intestinalis can only infect mussel hosts, but displayed higher infection success there than M. orientalis in both principal host species combined. In the absence of direct competitive interactions M. orientalis compensated its lower infection success (1) by recurrent spill-over from its high-fitness reservoir oyster host, and (2) by active aggregation interference enhancing its own mating success while limiting that of M. intestinalis. The introduced parasite could thus avoid direct competition by changing its own epidemiology and indirectly decreasing the reproductive success of its competitor in the new host. Such mechanisms outside of direct competition have seldom been considered, but are crucial to understand invasion success, parasite host range and community assembly in the context of species introductions.
Influence of game and quarter results on external peak demands during games in under-18 years, male basketball players

To quantify and compare the external peak demands (PD) encountered according to game result (win vs. loss), quarter result (win vs. tie vs. loss), and quarter point difference (± difference in score) in under-18 years (U18), male basketball players. Thirteen basketball players had external load variables monitored across 9 games using local positioning system technology, including distance covered, distance covered in different intensity zones, accelerations, decelerations, and PlayerLoad™. PD were calculated across 30-s, 1-min, and 5-min time windows for each variable. Linear mixed models were used to compare PD for each variable according to game result (win vs. loss), quarter result (win vs tie vs loss), and quarter point difference (high vs. low). External PD were comparable between games that were won and lost for all variables and between quarters that were won and lost for most variables (p > 0.05, trivial-small effects). In contrast, players produced higher (p < 0.05, small effects) 1-min high-speed running distance and 5-min PlayerLoadTM in quarters that were won compared to quarters that were lost. Additionally, high quarter point differences (7.51 ± 3.75 points) elicited greater (p < 0.05, small effects) external PD (30-s PlayerLoadTM, 30-s and 5-min decelerations, and 1-min and 5-min high-speed running distance) than low quarter point differences (-2.47 ± 2.67 points). External PD remain consistent (trivial-small effects) regardless of game result, quarter result, and quarter point difference in U18, male basketball players. Accordingly, external PD attained during games may not be a key indicator of team success.
Numerical evidence for phase transitions of NP-complete problems for instances drawn from Lévy-stable distributions

Random NP-Complete problems have come under study as an important tool used in the analysis of optimization algorithms and help in our understanding of how to properly address issues of computational intractability. In this thesis, the Number Partition Problem and the Hamiltonian Cycle Problem are taken as representative NP-Complete classes. Numerical evidence is presented for a phase transition in the probability of solution when a modified Lévy-Stable distribution is used in instance creation for each. Numerical evidence is presented that show hard random instances exist near the critical threshold for the Hamiltonian Cycle problem. A choice of order parameter for the Number Partition Problem’s phase transition is also given. Finding Hamiltonian Cycles in Erdös-Rényi random graphs is well known to have almost sure polynomial time algorithms, even near the critical threshold. To the author’s knowledge, the graph ensemble presented is the first candidate, without specific graph structure built in, to generate graphs whose Hamiltonicity is intrinsically hard to determine. Random graphs are chosen via their degree sequence generated from a discretized form of Lévy-Stable distributions. Graphs chosen from this distribution still show a phase transition and appear to have a pickup in search cost for the algorithms considered. Search cost is highly dependent on the particular algorithm used and the graph ensemble is presented only as a potential graph ensemble to generate intrinsically hard graphs that are difficult to test for Hamiltonicity. Number Partition Problem instances are created by choosing each element in the list from a modified Lévy-Stable distribution. The Number Partition Problem has no known good approximation algorithms and so only numerical evidence to show the phase transition is provided without considerable focus on pickup in search cost for the solvers used. The failure of current approximation algorithms and potential candidate approximation algorithms are discussed. i
Making Something out of Nothing: Building Robust Task-oriented Dialogue Systems from Scratch

In this paper, we introduce Gauchobot, a task-oriented dialogue system developed for the Alexa Prize Taskbot Challenge. We identified two main obstacles to building better conversational AI assistants in real-world applications. The first is great human efforts needed in data annotation and engineering a dialogue system that provides service in a new domain from scratch. The high cost, often ignored by existing research work, has blocked the broad deployment of dialogue systems. The second is a lack of robustness when facing undesirable situations during a conversation in real scenarios. The existing paradigm, which pre-defines dialogue flows and confines the users to a box with restricted options, makes dialogue systems easily stumped by complex conversations. To solve these two issues, we invent a methodology that can automatically generate data with minimum human efforts to train a unified framework capable of handling various sub-tasks in completing a task-oriented conversation. Feedback from real-world users can be easily incorporated into the model by automatically generating more training data and thus improving the model over time. Besides, we integrate multiple generative-based and retrieval-based response generation models into our Gauchobot, making it capable of handling not only task-oriented commands, but also QA, chit-chat, and other out-of-domain cases. As a result, Gauchobot can not only help complete tasks with rich user experience, but also provide a general framework of building robust task-oriented dialogue systems quickly from scratch.
Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

: There are more than 962 million people aged 60 and up globally. Physical activity declines as people get older, as does their capacity to undertake everyday tasks, effecting both physical and mental health. Many researchers use machine learning and deep learning methods to recognize human activities, but very few studies have been focused on human activity recognition of elderly people. This paper focuses on providing assistance to elderly people by monitoring their activities in different indoor and outdoor environments using gyroscope and accelerometer data collected from a smart phone. Smart phones have been routinely used to monitor the activities of persons with impairments; routine activities such as sitting, walking, going upstairs, going downstairs, standing, and lying are included in the dataset. Conventional Machine Learning and Deep Learning algorithms such as k-Nearest Neighbors, Random Forest, Support Vector Machine, Artiﬁcial Neural Network, and Long Short-Term Memory Network are used for human activity recognition. Long Short-Term Memory is a recurrent neural network variation that is best suited to handling temporal sequences. Two-fold and ten-fold cross-validation methods were performed to show the effect of changing the data in the training and testing dataset. Among all the classiﬁcation techniques, the proposed Long Short-Term Memory Network gave the best accuracy of 95.04%. However, Support Vector Machine gave 89.07% accuracy with a very low computational time of 0.42 min using 10-fold cross-validation.
Exploration of D-Cliques Variations and Edges Cases for Decentralized Federated Learning

Federated learning aims at training a machine learning algorithm, for instance, deep neural networks, on multiple local datasets contained in local nodes without explicitly exchanging data samples. The general principle consists in training local models on local data samples and exchanging the weights between these local nodes at some frequency to generate a global model shared by all nodes. A recent approach developed by the SACS laboratory at EPFL is D-Cliques (Bellet et al. 2021). This topology provides an intuitive structure, where a local node neighborhood called Cliques should aim to provide a close representation in terms of class distribution to the global network. In this report, we take a closer look at Greedy Swap, an iterative algorithm that can build D-Cliques topology with impressive performance after training. First, we study the impact of the selection of the permutation on the convergence rate of both the skew and the accuracy during training. Then in the following section, we discuss the requirements and hypothetical results we could obtain with a decentralized implementation of Greedy Swap. In the next section, we look at the relationship between the sparsity of connections between Cliques and the redundancy of the data. Finally, we study the first implementation of multi-clique ownership, where a node could belong simultaneously to multiple Cliques.
Towards a Computational Model of Narrative on Social Media

This thesis describes a variety of approaches to developing a computational model of narrative on social media. Our goal is to use such a narrative model to identify efforts to manipulate public opinion on social media platforms like Twitter. We present a model in which narratives in a collection of tweets are represented as a graph. Elements from each tweet that are relevant to potential narratives are made into nodes in the graph; for this thesis, we populate graph nodes with tweets’ authors, hashtags, named entities (people, locations, organizations, etc.,), and moral foundations (central moral values framing the discussion). Two nodes are connected with an edge if the narrative elements they represent appear together in one or more tweets, with the edge weight corresponding to the number of tweets in which these elements coincide. We then explore multiple possible deep learning and graph analysis methods for identifying narratives in a collection of tweets, including clustering of language embeddings, topic modeling, community detection and random walks on our narrative graph, training a graph neural network to identify narratives in the graph, and training a graph embedding model to generate vector embeddings of graph nodes. While much work still remains to be done in this area, several of our techniques, especially the generation and clustering of graph embeddings, were able to identify groups of related and connected nodes that might form the beginnings of narratives. Further study of these or other techniques could allow for the reliable identification of full narratives and information operations on social media.
Implementation of a multicomponent mixture model in 3D into the WaLBerla-Framework

Simulations of chemical processes are computationally extremly expensive tasks. Especially diffusionprocesses draw attention from industrial usage. The presented work deals with an implementation of a multicomponent mixture model in 3D, based on Ficks’ first law of diffusion, into the WaLBErla Framework by the university of Erlangen-Nurmeberg. The single-fluidapproach uses herefore the substantially more precise High-Order Compact Scheme (HOC), which offers an accuracy of O(h) while mainting a stencil dependent only of directly neighboring cells. The concluding tests primarily serve as verification of the convectionand diffusion-implementation, but also act as an approximation for the diffusioncoefficientand fluidvelocity-range. Simulationen chemischer Vorgaenge sind extrem rechenaufwendige Prozesse. Insbesondere Diffusionsprozesse ruecken dabei in den Mittelpunkt der Aufmerksamkeit in der Industrie. Diese Arbeit behandelt eine Implementation eines Multikomponenten-Mixtur-Modells in 3D, basierend auf Ficks erstem Gesetz der Diffusion, in das WaLBErla Framework der Universitaet Erlangen-Nuernberg. Der Single-Fluid-Ansatz verwendet dazu den wesentlich genaueren HighOrder Compact Scheme (HOC), der trotz eines nur von direkten Nachbarzellen abhaengigen Stencils eine relative Rechengenauigkeit von O(h) gewaehleistet. Die finalen Test dienen vornehmlich der Verifizierung der Konvektionsund Diffusions-Implementierung, aber auch der Abschaetzung eines Wertebereichs fuer die Parameter des Diffusionskoeffizienten und der Stroemunsgeschwindigkeiten.