Miracle-dz/UniFAR-FTUs huggingface dataset:
Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.
Effects of Freezing Raw Yak Milk on the Fermentation Performance and Storage Quality of Yogurt

In this study, the effects of freezing yak milk at −20 °C and −40 °C for 30, 90 and 180 days on the fermentation characteristics and storage quality of the corresponding yogurt were discussed. The results showed that, compared with that of yogurt made from fresh yak milk, the lactic acid bacteria (LAB) growth and acid production rate of the yogurt in the −20 °C group decreased at 90 d. The water-holding capacity, viscosity and hardness decreased during storage, and a sour taste was prominent, while no significant changes were observed in the −40 °C group. At 180 d of freezing, the post-acidification of the yogurt in the −20 °C and −40 °C groups increased after 21 d of storage. Compared with the −40 °C group, the −20 °C group showed a significant decrease in LAB counts, a decrease in pH value to 3.63–3.80 and poor texture and sensory quality.
High-speed reach-extended IM-DD system with low-complexity DSP for 6G fronthaul [Invited]

With the rising traffic demand from the metaverse, holographic communications, and so on, in beyond-5G (B5G)/6G cloud radio access networks (C-RANs), fronthaul (FH) networks that transport radio information between antennas and distributed/central units will become increasingly demanding. Optical technologies are indispensable in supporting 6G FH deployment, which needs to be high-speed and power/cost-effective simultaneously; low latency is also crucial. This work is extended from our invited paper at OFC2023. While the conference version focused on the wired-wireless conversion/converging side [e.g., using digital signal processing (DSP)-enhanced radio-over-fiber (RoF) techniques] of FH, this work contributes to the complementary side, i.e., optical transmission techniques. Considering 6G capacity and cost requirements, beyond-${200 \text{-Gb/s/}}\lambda$ optics based on intensity-modulation direct-detection (IM-DD) PAM4 and low-complexity DSP are targeted, which transparently support digital RoF techniques like analog-to-digital-compression (ADX)-RoF (split Option 8) or Delta-sigma RoF, and other functional split options. On the other hand, while the reach of 5G FH may be around 10 km, in 6G FH routing and/or rural connectivity cases, distance can be extended to 20 km or even 40 km. In these scenarios, fiber dispersion in the C-band and even edge wavelengths of the O-band pose a significant challenge for the reach extension of 200G IM-DD systems. We validate through extensive experiments the efficient FH reach extension and dispersion mitigation based on a low-complexity optoelectronic feedforward equalization (OE-FFE) technique. We experimentally verify 224 Gb/s single-wavelength and dense WDM transmissions over up to 41 km distances in the challenging C-band with lean digital equalizers and hard-decision forward error correction (HD-FEC), which are among the first demonstrations to our knowledge. Even with split Option 8 and extended reach, the FH systems support up to a 10 Tb/s Common Public Radio Interface (CPRI)-equivalent rate and ${\gt}{300}\;{\rm Gb/s}$ peak wireless data rate. The presented systems can be useful for high-capacity, reach-extended FH scenarios toward 6G.
Toward a Brain-Inspired System: Deep Recurrent Reinforcement Learning for a Simulated Self-Driving Agent

An effective way to achieve intelligence is to simulate various intelligent behaviors in the human brain. In recent years, bio-inspired learning methods have emerged, and they are different from the classical mathematical programming principle. From the perspective of brain inspiration, reinforcement learning has gained additional interest in solving decision-making tasks as increasing neuroscientific research demonstrates that significant links exist between reinforcement learning and specific neural substrates. Because of the tremendous research that focuses on human brains and reinforcement learning, scientists have investigated how robots can autonomously tackle complex tasks in the form of making a self-driving agent control in a human-like way. In this study, we propose an end-to-end architecture using novel deep-Q-network architecture in conjunction with a recurrence to resolve the problem in the field of simulated self-driving. The main contribution of this study is that we trained the driving agent using a brain-inspired trial-and-error technique, which was in line with the real world situation. Besides, there are three innovations in the proposed learning network: raw screen outputs are the only information which the driving agent can rely on, a weighted layer that enhances the differences of the lengthy episode, and a modified replay mechanism that overcomes the problem of sparsity and accelerates learning. The proposed network was trained and tested under a third-party OpenAI Gym environment. After training for several episodes, the resulting driving agent performed advanced behaviors in the given scene. We hope that in the future, the proposed brain-inspired learning system would inspire practicable self-driving control solutions.
ERROR ANALYSIS OF A CONSERVATIVE FINITE-ELEMENT APPROXIMATION FOR THE KELLER-SEGEL SYSTEM OF CHEMOTAXIS

We are concerned with the finite-element approximation for the Keller-Segel system that describes the aggregation of slime molds resulting from their chemotactic features. The scheme makes use of a semi-implicit time discretization with a time-increment control and Baba-Tabata's conservative upwind finite-element approximation in order to realize the positivity and mass conservation properties. The main aim is to present error analysis that is an application of the discrete version of the analytical semigroup theory.
Arabic Text Mining Using Rule Based Classification

A well-known classification problem in the domain of text mining is text classification, which concerns about mapping textual documents into one or more predefined category based on its content. Text classification arena recently attracted many researchers because of the massive amounts of online documents and text archives which hold essential information for a decision-making process. In this field, most of such researches focus on classifying English documents while there are limited studies conducted on other languages like Arabic. In this respect, the paper proposes to investigate the problem of Arabic text classification comprehensively. More specifically the study measures the performance of different rule based classification approaches adopted from machine learning and data mining towards the problem of text Arabic classification. In particular, four different rule based classification approaches: Decision trees (C4.5), Rule Induction (RIPPER), Hybrid (PART) and Simple Rule (One Rule) are evaluated against the published Corpus of Contemporary Arabic Arabic text collection. This experimentation is carried out by employing a modified version of WEKA business intelligence tool. Through analysing the produced results from the experimentation, we determine the most suitable classification algorithms for classifying Arabic texts.
From vocal prosody to movement prosody, from HRI to understanding
 humans

Human–Human and Human–Robot Interaction are known to be influenced by a variety of modalities and parameters. Nevertheless, it remains a challenge to anticipate how a given mobile robot’s navigation and appearance will impact how it is perceived by humans. Drawing a parallel with vocal prosody, we introduce the notion of movement prosody, which encompasses spatio-temporal and appearance dimensions which are involved in a person’s perceptual experience of interacting with a mobile robot. We design a novel robot motion corpus, encompassing variables related to the kinematics, gaze, and appearance of the robot, which we hypothesize are involved in movement prosody. Initial results of three perception experiments suggest that these variables have significant influences on participants’ perceptions of robot socio-affects and physical attributes.
The Beneficial Effects of Pterostilbene on Post-Thawed Bovine Spermatozoa

Simple Summary The present study was designed to investigate the effect of pterostilbene, a phenolic compound found in fruits and red wine, on bovine sperm quality characteristics. The spermatozoon was safely handled in the laboratory before subjecting it to Assisted Reproductive Techniques, such as in vitro fertilization, which generates free radicals that might negatively affect structural integrity and fertilizing capacity. An approved approach to protect spermatozoa is the supplementation of the media with exogenous antioxidants. The multiple benefits and pleiotropic actions of antioxidants make them scientifically interesting and popular. In this study, the effect of two different concentrations (10 μΜ and 25 μΜ) of pterostilbene have been tested during two incubation periods, 60 min and 240 min, after thawing. The addition of 25 μM pterostilbene in the bovine sperm preparation media had advantageous effects on motility, viability, intracellular superoxide anion concentration, and acrosomal status of spermatozoa. Thus, the role of pterostilbene seems potentially beneficial for spermatozoa and should be further investigated. Abstract Reactive Oxygen Species (ROS), primarily produced by cellular metabolism, are highly reactive molecules that modify cellular compounds. During sperm preparation in Assisted Reproductive Techniques (ARTs), intrinsic and extrinsic sources of ROS can impact spermatozoa’s oxidative status. The modification of the media with compounds that enhance sperm quality characteristics is of great significance. The current study investigated the effect of pterostilbene, a phenolic compound, on bovine sperm quality. Cryopreserved spermatozoa from six bulls were thawed, supplemented with pterostilbene (0, 10 μΜ, 25 μΜ) and incubated for 60 min and 240 min. Spermatozoa were analyzed in terms of motility, viability, acrosomal status and intracellular concentration of superoxide anion in each time point. The incubation of spermatozoa with 25 μΜ pterostilbene resulted in the preservation of quality parameters through superoxide anion mitigation, while its presence in capacitating conditions resulted in higher percentage of acrosome-reacted spermatozoa. The results of the present study indicate that the addition of pterostilbene prevents oxidative insult to spermatozoa and preserves the sperm quality parameters.
Towards Mirror-Less Graphene-Based Perfect Absorbers

Owing to its exceptional electronic and optical properties, graphene has attracted extensive attention among researchers in the development of high-performance optoelectronic devices. However, the light absorption of pure graphene is very poor, limiting its development in practical application. In this review, as a solution for this issue, various types of graphene-based perfect absorbers are addressed in terms of their operation principles and design requirements. Their recent progress and potential applications such as photodetectors and modulators are also discussed. In particular, we emphasize the importance of mirror-less (in particular, one-port mimicking) perfect absorber design due to simplified fabrication processes or enhanced tolerance for fabrication error.
DEMOCRATIZING EDUCATION: PEDAGOGICAL ACTIVISM AND TECHNOLOGICAL FUTURES

The current and ongoing urgencies for pedagogical invention in both philosophy and practice demand teaching and learning designs and applications that have consequence in terms of civic engagement. The achievement of democratic civic understandings, values, and practices depends on the recognition by educators that they are in fact, charged with the critical and ennobling task of developing through content and action the present and future civic capacities of their students; and such capacities are antidotal to the troubling ascendency of populism, political autocracies and thuggeries around the globe. In an era irrefutably afflicted with the profusion of disinformation, the erosion of public trust, and the destabilization of truth, education for democratic participation is both intervenor and instigator for the broad project of citizenship and social change. In this sense, pedagogies are necessarily activist in their commitment to social engagement and change. The recognition of the exigencies of democratic education is more than a matter of curricula content that engages critically with the definitions and principles of deliberative democracy; rather, such recognition should result in pedagogical approaches and practices that model democratic participation especially in terms of learning environments and an infinitely more expansive view of the classroom. Key and orienting in the project of democratizing education are the determinants and elaborations of technology, and in particular AI in the educational context. While there is considerable handwringing around potential compromises to academic integrity and a rapid and unrestrained increase in academic dishonesty without efforts to neutralize the foundations and capacities of AI-generated papers, this paper explores affordances of current and emerging technologies in terms of precise practices toward democratic education, from pedagogical innovation, creative approaches to course design, new evaluation methods and criteria, expansive and experiential learning spaces, and more.
The Evolving Role of Cryosurgery in Breast Cancer Management: A Comprehensive Review

Simple Summary Breast cancer is the most prevalent cancer and the second leading cause of death among women, primarily due to the development of deadly metastases in 25–50% of patients, resulting in an unfavorable prognosis. Consequently, there is growing interest in exploring innovative therapeutic approaches to improve clinical outcomes and enhance the quality of life for breast cancer patients. One such approach that shows promise is the application of ultra-low cryogenic temperatures through cryosurgery. This review aims to critically examine the current understanding and state-of-the-art practices of cryosurgery as a treatment for breast cancer, while also addressing the challenges and prospects associated with this approach. By conducting a comprehensive literature review, this paper intends to provide an updated and comprehensive perspective on the topic. Abstract Breast cancer is the most commonly diagnosed type of cancer, accounting for approximately one in eight cancer diagnoses worldwide. In 2020, there were approximately 2.3 million new cases of breast cancer globally, resulting in around 685,000 deaths. Consequently, there is an ongoing need to develop innovative therapeutic approaches that can improve both clinical outcomes and patient quality of life. The use of ultra-low cryogenic temperatures, facilitated by cryogenic media such as liquid nitrogen, has revolutionized the biomedical field and opened up new possibilities for advanced clinical treatments, including cryosurgery. Cryosurgery has demonstrated its feasibility as a minimally invasive technique for destroying breast tumors and eliciting a significant antitumor immune response in the host. This feature sets cryosurgery apart from other ablative techniques. It has been shown to be well tolerated and effective, offering several advantages such as simplicity, the avoidance of general anesthesia, minimal pain, low morbidity, short recovery time, cost-effectiveness, and notably, improved aesthetic outcomes. The reviewed studies indicate that cryosurgery holds promise in the management of early-stage breast cancer and metastatic disease, especially in triple-negative and Her2-positive molecular subtypes in conjunction with checkpoint inhibitors and anti-Her2 antibodies, respectively. Furthermore, the effectiveness of cryosurgery in the management of ductal carcinoma in situ should be investigated as an alternative modality to surgery or surveillance. The minimally invasive nature of cryosurgery has the potential to significantly enhance the quality of life for patients.
Downlink Traffic Demand-Based Gateway Activation in LoRaWAN

Due to the radio duty cycle restrictions in the sub- GHz unlicensed spectrum imposed in many regions, the downlink resources of the gateways in LoRaWAN are depleted fast even in the presence of moderate uplink traffic. To tackle the high demand for downlink traffic, additional gateways - from a pool of available positions - can be deployed. Not all gateways can initially be activated because of the extra operating costs that this action would cause. On the contrary, this paper studies the problem of dynamically activating the minimum estimated number of gateways in order to adapt the network to the traffic demands, and thus, improve the network reliability and energy consumption. The approach is based on theoretical foundations of downlink traffic as well as on empirical results and simple heuristics to select the gateway positions to be dynamically activated without taking into account the positions of the end-devices. Simulation results show that the proposed methodology in combination with the proposed heuristics exhibit a packet delivery ratio of over 90 % even with a single retransmission. The average energy consumption decreases considerably as well. Finally, the proposed approach presents similar results with an existing approach which, however, considers the positions of the end-devices as known.
Dictionary Learning-Guided Deep Interpretable Network for Hyperspectral Change Detection

Hyperspectral image (HSI) change detection is a technique to observe the change information between the multitemporal HSIs, which is currently considered a major focus of research in the field of remote-sensing intelligent interpretation. Most existing deep-learning-based methods have created satisfactory performance, but these methods lack transparency and have poor generalization. To tackle the problems outlined above, we propose a dictionary learning-guided deep interpretable network (DLDINet) for hyperspectral change detection, which unfolds a dictionary learning-based change detection model into an interpretable deep neural network. Specifically, we first design a dictionary learning-based change detection model, whose solution process can be decomposed into two iterative subproblems. Then, the mathematical model can be unfolded into a dual-branch deep neural network with two modules iterating with each other. Finally, the difference map of the coefficient’s output from the ultimate stage is classified to obtain the change detection result. Experimental results prove that the proposed method has comparable or even better performance than state-of-the-art methods.
Japanese Event Factuality Analysis in the Era of BERT

Recognizing event factuality is a crucial factor for understanding and generating texts with abundant references to possible and counterfactual events. Because event factuality is signaled by modality expressions, identifying modality expression is also an important task. The question then is how to solve these interconnected tasks. On the one hand, while neural networks facilitate multi-task learning by means of parameter sharing among related tasks, the recently introduced pre-training/fine-tuning paradigm might be powerful enough for the model to be able to learn one task without indirect signals from another. On the other hand, ever-increasing model sizes make it practically difficult to run multiple task-specific fine-tuned models at inference time so that parameter sharing can be seen as an effective way to reduce the model’s size. Through experiments, we found: (1) BERT-CRF outperformed non-neural models and BiLSTM-CRF; (2) BERT-CRF did neither benefit from nor was negatively impacted by multi-task learning, indicating the practical viability of BERT-CRF combined with multi-task learning.
The Effect of Static And Dynamic Warm-Up Protocols on Fitness Component And Body Fat Percentage of Athletes in Different Branches

The aim of this study is to investigate the effects of static and dynamic warming protocols on some motor tests and body fat percentage of athletes in different branches. In this study, 50 (25 female and 25 male) athletes from Mersin University High School of Physical Education and Sports volleyball, football, basketball, wrestling and handball branches were formed the sample group of the research. The anthropometric characteristics of the individuals Height and weight measurements were made to determine and body fat percentages were determined by Bioelectrical Impedance Analysis.As engine performance tests, Vertical jump test, Sprint performance test (20m), sit- reach flexibility test were applied. One-way analysis of variance (ANOVA) was performed to determine whether there was a statistically significant difference between measurements. Data was tested for normality with the “Kolmogorov Smirnov Test”. Kruskall Wallis tests were used for comparison among five groups. According to our findings no significant difference was observed between the male branches in terms of body fat percentage (%BF). Female soccer players were found to have the least body fat percentage when compared to other branches (p <0.01). Static and dynamic warming protocols it was found to have an effect statistically significant (p <0.05, P <0.001) in sit- reach flexibility, vertical jump and speed test values of athletes in different branches. As a result; it has been found that the static and dynamic warming protocolshave different effects in terms of the different sports branches and motor performance parameters. When both male and female performance values ​​were examined, dynamic heating was found to be more dominant. It can be said that the physical and physiological values of the athletes are directly related to the performance of being suitable for the sports branch involved. For this reason, the use of warming protocols is recommended with regard to the results of the study presented here.