Miracle-dz/UniFAR-FTUs huggingface dataset:
ReFOCUS: Reusing Light for Efficient Fourier Optics-Based Photonic Neural Network Accelerator

In recent years, there has been a significant focus on achieving low-latency and high-throughput convolutional neural network (CNN) inference. Integrated photonics offers the potential to substantially expedite neural networks due to its inherent low-latency properties. Recently, on-chip Fourier optics-based neural network accelerators have been demonstrated and achieved superior energy efficiency for CNN acceleration. By incorporating Fourier optics, computationally intensive convolution operations can be performed instantaneously through on-chip lenses at a significantly lower cost compared to other on-chip photonic neural network accelerators. This is thanks to the complexity reduction offered by the convolution theorem and the passive Fourier transforms computed by on-chip lenses. However, conversion overhead between optical and digital domains and memory access energy still hinder overall efficiency.We introduce ReFOCUS, a Joint Transform Correlator (JTC) based on-chip neural network accelerator that efficiently reuses light through optical buffers. By incorporating optical delay lines, wavelength-division multiplexing, dataflow, and memory hierarchy optimization, ReFOCUS minimizes both conversion overhead and memory access energy. As a result, ReFOCUS achieves 2× throughput, 2.2× energy efficiency, and 1.36× area efficiency compared to state-of-the-art photonic neural network accelerators.CCS CONCEPTS• Computer systems organization → Architectures; • Hardware → Emerging technologies; Emerging optical and photonic technologies; • Computing methodologies → Artificial intelligence; Machine learning.
Drought Monitoring over Egypt by using MODIS Land Surface Temperature and Normalized Difference Vegetation Index

Drought is a serious climatic condition that affects nearly on all climatic zones worldwide, with semi-arid regions being especially susceptible to drought conditions because of their low annual precipitation and sensitivity to climate changes. In the present work, we use the Vegetation Health Index (VHI) as drought indices to monitoring the drought over Egypt.The Moderate Resolution Imaging Spectrometer (MODIS) on the terra satellite has been used the normalized difference vegetation index (NDVI) and land surface temperature (LST) to calculate the Vegetation Condition Index (VCI) , Temperature Condition Index (TCI) and vegetation health index (VHI) over Egypt, for the seasons from 2000/2001 to 2010/2011. The time series of (VCI) and (TCI) show that; in certain years they were corresponded to each other, while in other years they were one counter the other and their resultant determined the occurrence and severity of drought, which is reflected in the Vegetation Health Index (VHI). The results show that; the effect of drought on vegetation varies noticeably between areas, a pattern that is determined mainly by the location of land-cover types. The drought frequency increased during the last four years. The agricultural seasons 2000/2001 and 2010/2011 were the highest agricultural area affected by high drought while the 2006/2007 was the lowest agricultural area affected by high drought. (Khalil1 A.A.; M.M. Abdel-Wahab; M. K. Hassanein; B.Ouldbdey ; B. Katlan; and Y.H. Essa. Drought Monitoring over Egypt by using MODIS Land Surface Temperature and Normalized Difference Vegetation Index. Nat Sci 2013;11(11):116-122). (ISSN: 1545-0740). http://www.sciencepub. net . 17
Impact of Dietary Protein on Osteoporosis Development

Osteoporosis is a frequent yet unsolved health problem among older people. The influence of dietary protein still raises many questions regarding its quality and quantity in the context of bone health. The aim of this manuscript is to review the latest evidence on plant and animal protein influences on bone health in various groups of patients. The review is based on original studies, meta-analyses, randomized controlled trials, and prospective cohort studies published in PubMed and Cochrane databases during the last five years. Combining plant and animal protein with physical activity has the best effect on bones (muscle strengthening and reducing the risk of falls), while high protein intake can have adverse effects during bed rest. Despite the content of isoflavones, plant protein is not more beneficial than animal protein (dairy products) and can increase bone resorption markers. Hypoestrogenism due to menopause or eating disorders leads to low bone density and an increased risk of osteoporosis. A well-balanced diet with sufficient energy supply and protein intake (both of plant and animal origins) and adequate physical activity are crucial to ensure bone health. Dietary interventions should consider the quantity and quality of protein in patients with other comorbidities, particularly in an aging society.
New leisure forum for old leisure practices: online and offline interaction and presentations of self in Figueres, Spain

It is widely believed that new media have the potential to eradicate traditional forms of leisure by altering how we interact and communicate at a global level in light of ubiquitous and placeless connectivity. However, contrary to pervading assumptions that local leisure traditions will naturally be displaced in favour of new media environments, this paper suggests that evolving leisure practices on the Internet are fundamentally shaped by existing, offline (face-to-face) patterns of interaction. Based on data collected from 15 months of ethnographic fieldwork in the Catalan city of Figueres, Spain, it focuses on the blueprints of traditional leisure practices in a local setting to argue that they may be enriched by web-based activities. I advocate an innovative approach to new leisure spaces in an increasingly mediated world by examining the geographic realities and cultural contexts within which new technologies are appropriated.
A Full and Detailed Proof for the Riemann Hypothesis & the Simple Inductive proof of Goldbach’s Conjecture

As in my previous two papers [2] & [3] about the boundary of the prime gap still cause some misunderstanding, I here in this paper tries to clarify those detailed steps in proving such boundary of the prime gap for a contradiction. Indeed, the general idea of my designed proof is to make all of the feasible case of the Riemann Zeta function with exponents ranged from 1 to s = u + v*I becomes nonsense (where u, v are real numbers with I is imaginary equals to (-1)1/2 except that u = 0.5 with some real numbers v as the expected zeta roots. Once if we can exclude all other possibilies unless u = 0.5 with some real numbers v in the Riemann Zeta function’s exponent “s”, then the Riemann Hypothesis will be proved immediately. The truth of the hypothesis further implies that there is a need for the shift from the line x = 0 to the line x = 0.5 as all of the zeta roots lie on it. However, NOT all of the points on x = 0.5 are zeros as we may find from the model equation that has been well established in [2]. One of my application is in the quantum filtering for an elimination of noise in a quantum system but NOT used to filter human beings like the political counter-parts.In general, this author suggests that for all of the proof or disproof to any cases of hypothesis, one may need to point out those logical contradictions [14] among them. Actually, my proposition works very well for the cases in my disproof of Continuum Hypothesis [15] together with the proof in Riemann Hypothesis
Pushdown Layers: Encoding Recursive Structure in Transformer Language Models

Recursion is a prominent feature of human language, and fundamentally challenging for self-attention due to the lack of an explicit recursive-state tracking mechanism. Consequently, Transformer language models poorly capture long-tail recursive structure and exhibit sample-inefficient syntactic generalization. This work introduces Pushdown Layers, a new self-attention layer that models recursive state via a stack tape that tracks estimated depths of every token in an incremental parse of the observed prefix. Transformer LMs with Pushdown Layers are syntactic language models that autoregressively and synchronously update this stack tape as they predict new tokens, in turn using the stack tape to softly modulate attention over tokens -- for instance, learning to"skip"over closed constituents. When trained on a corpus of strings annotated with silver constituency parses, Transformers equipped with Pushdown Layers achieve dramatically better and 3-5x more sample-efficient syntactic generalization, while maintaining similar perplexities. Pushdown Layers are a drop-in replacement for standard self-attention. We illustrate this by finetuning GPT2-medium with Pushdown Layers on an automatically parsed WikiText-103, leading to improvements on several GLUE text classification tasks.
Energy Extraction from Spinning Stringy Black Holes

We perform the first numerical simulations modeling the inflow and outflow of magnetized plasma in the Kerr-Sen spacetime, which describes classical spinning black holes (BHs) in string theory. We find that the Blandford-Znajek (BZ) mechanism, which is believed to power astrophysical relativistic outflows or ``jets'', is valid even for BHs in an alternate theory of gravity, including near the extremal limit. The BZ mechanism releases outward Poynting-flux-dominated plasma as frame-dragging forces magnetic field lines to twist. However, for nonspinning BHs, where the frame-dragging is absent, we find an alternate powering mechanism through the release of gravitational potential energy during accretion. Outflows from non-spinning stringy BHs can be approximately $250\%$ more powerful as compared to Schwarzschild BHs, due to their relatively smaller event horizon sizes and, thus, higher curvatures. Finally, by constructing the first synthetic images of near-extremal non-Kerr BHs from time-dependent simulations, we find that these can be ruled out by horizon-scale interferometric images of accreting supermassive BHs.
Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI

Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of the datasets available for learning, and how to effectively combine neuroimaging and tabular data (e.g. demographic information and clinical characteristics). This paper evaluates several solutions based on two strategies. The first is to use 2D images that summarise MRI scans. The second is to select key features that improve classification accuracy. Additionally, we introduce the novel approach of training a convolutional neural network (CNN) on images that combine regions-of-interest extracted from MRIs, with symbolic representations of tabular data. We evaluate a series of CNN architectures (both 2D and a 3D) that are trained on different representations of MRI and tabular data, to predict whether a composite measure of post-stroke spoken picture description ability is in the aphasic or non-aphasic range. MRI and tabular data were acquired from 758 English speaking stroke survivors who participated in the PLORAS study. The classification accuracy for a baseline logistic regression was 0.678 for lesion size alone, rising to 0.757 and 0.813 when initial symptom severity and recovery time were successively added. The highest classification accuracy 0.854 was observed when 8 regions-of-interest was extracted from each MRI scan and combined with lesion size, initial severity and recovery time in a 2D Residual Neural Network.Our findings demonstrate how imaging and tabular data can be combined for high post-stroke classification accuracy, even when the dataset is small in machine learning terms. We conclude by proposing how the current models could be improved to achieve even higher levels of accuracy using images from hospital scanners.
Evaluation Framework for Understanding Sensitive Attribute Association Bias in Latent Factor Recommendation Algorithms

We present a novel evaluation framework for representation bias in latent factor recommendation (LFR) algorithms. Our framework introduces the concept of attribute association bias in recommendations allowing practitioners to explore how recommendation systems can introduce or amplify stakeholder representation harm. Attribute association bias (AAB) occurs when sensitive attributes become semantically captured or entangled in the trained recommendation latent space. This bias can result in the recommender reinforcing harmful stereotypes, which may result in downstream representation harms to system consumer and provider stakeholders. LFR models are at risk of experiencing AAB due to their ability to entangle explicit and implicit attributes into the trained latent space. Understanding this phenomenon is essential due to the increasingly common use of entity vectors as attributes in downstream components in hybrid industry recommendation systems. We provide practitioners with a framework for executing disaggregated evaluations of AAB within broader algorithmic auditing frameworks. Inspired by research in natural language processing (NLP) observing gender bias in word embeddings, our framework introduces AAB evaluation methods specifically for recommendation entity vectors. We present four evaluation strategies for sensitive AAB in LFR models: attribute bias directions, attribute association bias metrics, classification for explaining bias, and latent space visualization. We demonstrate the utility of our framework by evaluating user gender AAB regarding podcast genres with an industry case study of a production-level DNN recommendation model. We uncover significant levels of user gender AAB when user gender is used and removed as a model feature during training, pointing to the potential for systematic bias in LFR model outputs.
Influence of Different Carrier Gases, Temperature, and Partial Pressure on Growth Dynamics of Ge and Si Nanowires

The broad and fascinating properties of nanowires and their synthesis have attracted great attention as building blocks for functional devices at the nanoscale. Silicon and germanium are highly interesting materials due to their compatibility with standard CMOS technology. Their combination provides optimal templates for quantum applications, for which nanowires need to be of high quality, with carefully designed dimensions, crystal phase, and orientation. In this work, we present a detailed study on the growth kinetics of silicon (length 0.1–1 μm, diameter 10–60 nm) and germanium (length 0.06–1 μm, diameter 10–500 nm) nanowires grown by chemical vapor deposition applying the vapour–liquid–solid growth method catalysed by gold. The effects of temperature, partial pressure of the precursor gas, and different carrier gases are analysed via scanning electron microscopy. Argon as carrier gas enhances the growth rate at higher temperatures (120 nm/min for Ar and 48 nm/min H2), while hydrogen enhances it at lower temperatures (35 nm/min for H2 and 22 nm/min for Ar) due to lower heat capacity. Both materials exhibit two growth regimes as a function of the temperature. The tapering rate is about ten times lower for silicon nanowires than for germanium ones. Finally, we identify the optimal conditions for nucleation in the nanowire growth process.
Isotopic Composition of Atmospheric Precipitation and Shallow Groundwater in Olkiluoto: O-18, H-2 and H-3

Isotopic composition of oxygen and hydrogen in local precipitation is a key parameter in modelling of local water circulation. This study was initiated in order to provide systematic monthly records of the isotope content of atmospheric precipitation in the Olkiluoto area and to establish the relation between local rainfall and shallow groundwater. During January 2005 – June 2008 a total of 35 cumulative monthly rainfall samples and 38 shallow groundwater samples were collected and their O-18, H-2 (= D) and H-3 values were detected. Based on the three years monitoring, the weighted annual mean isotope values of the precipitation and the mean values for groundwater are -12.15‰ and -11.31‰ for oxygen, -86.8‰ and -80.4‰ for hydrogen and 10.2 and 9.9 TU for tritium, respectively. In general, the isotope signatures of atmospheric precipitation and groundwater at Olkiluoto are close to unity and represent the typical isotope values of this area in Finland. The coastal location of the study area is reflected in the isotopic composition of oxygen and hydrogen in the precipitation. In contrast to inland rainfall, the temporal correlation between the stable isotope ratios and surface temperatures is poor and the seasonal isotopic variations are decreased. The seasonal behaviour of tritium concentrations in Olkiluoto precipitation follows a typical H-3 pattern observed in the northern hemisphere and, hence, not any significant input of secondary tritium can be suggested during this monitoring period. The precipitation data were used to establish the local meteoric water line (LMWL) for the Olkiluoto area. The line is formulated as: δD = 7.29 δO-18 + 2.01.
Defining a New NLP Playground

The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history. This has resulted in concerns that the field will become homogenized and resource-intensive. The new status quo has put many academic researchers, especially PhD students, at a disadvantage. This paper aims to define a new NLP playground by proposing 20+ PhD-dissertation-worthy research directions, covering theoretical analysis, new and challenging problems, learning paradigms, and interdisciplinary applications.
Wound Healing Performance in a Moist Environment of Crystalline Glucose/Mannose Film as a New Dressing Material Using a Rat Model: Comparing with Medical-Grade Wound Dressing and Alginate

Although medical wound dressings produced using hydrocolloids and alginate were effective in wound healing, adhesion at the wound site and the resulting delayed healing have been a problem. As a new wound dressing material, crystalline wound dressings produced from glucose/mannose were used in this study, which aimed to clarify the properties, adhesion reduction, and wound healing performance of a new wound dressing. Crystalline glucose/mannose films were obtained via alkali treatment using the solution casting method. The structure of the crystalline glucose/mannose films was analogous to the cellulose II polymorph, and the crystallinity decreased with time in hydrated conditions. The crystalline glucose/mannose films had adequate water absorption of 34 × 10−4 g/mm3 for 5 min. These allowed crystalline glucose/mannose films to remove excess wound exudates while maintaining a moist wound healing condition. This in vivo study demonstrated the healing effects of three groups, which were crystalline glucose/mannose group > alginate group > hydrocolloid group. At 1 week, the crystalline glucose/mannose group was also found to be non-adhesive to the portion of wound healing. This was evidenced by the earlier onset of the healing process, which assisted in re-epithelization and promotion of collagen formation and maturation. These results implied that crystalline glucose/mannose films were a promising candidate that could accelerate the wound healing process, compared with medical-grade wound dressing and alginate.
Evaluating the Prognostic Efficacy of Scoring Systems in Neurocritical and Neurosurgical Care: An Insight into APACHE II, SOFA, and GCS

Background: To investigate the utility of established prognostic scoring systems, such as the Acute Physiology and Chronic Health Evaluation (APACHE) II score, Sequential Organ Failure Assessment (SOFA) score, and Glasgow Coma Scale (GCS), for patients admitted to a neurosurgical intensive care unit (ICU). Methods: Among neurosurgical patients admitted to the neurosurgical ICU in a tertiary hospital from January 2015 and December 2022, only patients who had an ICU stay exceeding three days were included. The primary endpoint was in-hospital mortality. Results: In this study, a total of 3,417 patients were enrolled in the study. Of these, 3,052 (89.3%) survived until hospital discharge. Both the APACHE II and SOFA scores were significantly higher in non-survivors than in survivors (p<0.001 for both). Conversely, GCS and GCS motor score (GCS M) were substantially lower in non-survivors (p<0.001). Among the commonly used scoring systems, the APACHE II score emerged as the most effective predictor of in-hospital mortality (C-statistic of 0.887, 95% confidence interval: 0.869–0.887). Remarkably, the GCS M proved to be equally effective as the SOFA score in predicting in-hospital mortality (p=0.435) and offered the additional advantage of being simpler to use.Conclusions: Our findings indicate that these scoring systems offer valuable insights into the clinical prognosis of patients in the neurosurgical ICU. Moreover, the GCS M stands out as a feasible and reliable metric for predicting in-hospital mortality among neurosurgical ICU patients.
Penanaman Akhlak dan Tasawuf Sebagai Upaya Penguatan Super Ego bagi Santri di Pondok Pesantren Babakan Jamanis Parigi Pangandaran

Mendidik generasi muda, menjaga rasa aman lingkungan, dan menjaga kesatuan bangsa merupakan tanggung jawab semua pihak. Tanggung jawab ini juga diemban oleh lembaga pendidikan sekolah dan memiliki peran yang sangat penting. Penelitian ini bertujuan buat mengetahui perencanaan, implementasi, dan implikasi taktik penanaman akhlak dan tasawuf sebagai upaya penguatan super ego bagi santri-santriwati Pondok Pesantren Babakan Jamanis Parigi. Penelitian yang dilakukan pada Pondok Pesantren Babakan Jamanis Parigi ialah penelitian kualitatif. Teknik pengumpulan data memakai wawancara, observasi, dan dokumentasi. Beberapa penguatan karakter santri yang biasa diterapkan sehari-hari ialah sholat tahajjud bersama, sholat dhuha bersama, sholat fardhu 5 waktu berjamaah dan menghafal bersama. Kegiatan keagamaan ini menjadi agenda wajib yang diikuti oleh seluruh santri setiap harinya. Di luar aktivitas peribadatan, santri dan santriwati Pondok Pesantren Babakan Jamanis juga dibiasakan untuk melakukan bersih-bersih lingkungan bersama, menghafal bersama dan berolahraga bersama. Sebagai lembaga pendidikan Islam, Pondok Pesantren Babakan Jamanis benar-benar mengaktualisasikan nilai-nilai akhlak dan tasawuf kepada para santrinya.