Miracle-dz/UniFAR-FTUs huggingface dataset:
Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network

Federated Learning (FL) enables many resource-limited devices to train a model collaboratively without data sharing. However, many existing works focus on model-homogeneous FL, where the global and local models are the same size, ignoring the inherently heterogeneous computational capabilities of different devices and restricting resource-constrained devices from contributing to FL. In this paper, we consider model-heterogeneous FL and propose Federated Partial Model Training (FedPMT), where devices with smaller computational capabilities work on partial models (subsets of the global model) and contribute to the global model. Different from Dropout-based partial model generation, which removes neurons in hidden layers at random, model training in FedPMT is achieved from the back-propagation perspective. As such, all devices in FedPMT prioritize the most crucial parts of the global model. Theoretical analysis shows that the proposed partial model training design has a similar convergence rate to the widely adopted Federated Averaging (FedAvg) algorithm, $\mathcal{O}(1/T)$, with the sub-optimality gap enlarged by a constant factor related to the model splitting design in FedPMT. Empirical results show that FedPMT significantly outperforms the existing benchmark FedDrop. Meanwhile, compared to the popular model-homogeneous benchmark, FedAvg, FedPMT reaches the learning target in a shorter completion time, thus achieving a better trade-off between learning accuracy and completion time.
Multi-View Spectrogram Transformer for Respiratory Sound Classification

Deep neural networks have been applied to audio spectrograms for respiratory sound classification. Existing models often treat the spectrogram as a synthetic image while overlooking its physical characteristics. In this paper, a Multi-View Spectrogram Transformer (MVST) is proposed to embed different views of time-frequency characteristics into the vision transformer. Specifically, the proposed MVST splits the mel-spectrogram into different sized patches, representing the multi-view acoustic elements of a respiratory sound. These patches and positional embeddings are then fed into transformer encoders to extract the attentional information among patches through a self-attention mechanism. Finally, a gated fusion scheme is designed to automatically weigh the multi-view features to highlight the best one in a specific scenario. Experimental results on the ICBHI dataset demonstrate that the proposed MVST significantly outperforms state-of-the-art methods for classifying respiratory sounds.
Machine Learning for Polaritonic Chemistry: Accessing Chemical Kinetics

Altering chemical reactivity and material structure in confined optical environments is on the rise, and yet, a conclusive understanding of the microscopic mechanisms remains elusive. This originates mostly from the fact that accurately predicting vibrational and reactive dynamics for soluted ensembles of realistic molecules is no small endeavor, and adding (collective) strong light–matter interaction does not simplify matters. Here, we establish a framework based on a combination of machine learning (ML) models, trained using density-functional theory calculations and molecular dynamics to accelerate such simulations. We then apply this approach to evaluate strong coupling, changes in reaction rate constant, and their influence on enthalpy and entropy for the deprotection reaction of 1-phenyl-2-trimethylsilylacetylene, which has been studied previously both experimentally and using ab initio simulations. While we find qualitative agreement with critical experimental observations, especially with regard to the changes in kinetics, we also find differences in comparison with previous theoretical predictions. The features for which the ML-accelerated and ab initio simulations agree show the experimentally estimated kinetic behavior. Conflicting features indicate that a contribution of dynamic electronic polarization to the reaction process is more relevant than currently believed. Our work demonstrates the practical use of ML for polaritonic chemistry, discusses limitations of common approximations, and paves the way for a more holistic description of polaritonic chemistry.
U(2) is Right for Leptons and Left for Quarks

We posit that the distinct patterns observed in fermion masses and mixings are due to a minimally broken $\mathrm{U}(2)_{q+e}$ flavor symmetry acting on left-handed quarks and right-handed charged leptons, giving rise to an accidental $\mathrm{U}(2)^5$ symmetry at the renormalizable level without imposing selection rules on the Weinberg operator. We show that the symmetry can be consistently gauged by explicit examples and comment on realizations in $\mathrm{SU}(5)$ unification. Via a model-independent SMEFT analysis, we find that selection rules due to $\mathrm{U}(2)_{q+e}$ enhance the importance of charged lepton flavor violation as a probe, where significant experimental progress is expected in the near future.
A Field Of One's Own

This is the first major study of gender and property in South Asia. In a pioneering and comprehensive analysis Bina Agarwal argues that the single most important economic factor affecting women's situation is the gender gap in command over property. In rural South Asia, the most significant form of property is arable land, a critical determinant of economic well-being, social status, and empowerment. But few women own land; fewer control it. Drawing on a vast range of interdisciplinary sources and her own field research, and tracing regional variations across five countries, the author investigates the complex barriers to women's land ownership and control, and how they might be overcome. The book makes significant and original contributions to theory and policy concerning land reforms, 'bargaining' and gender relations, women's status, and the nature of resistance.
Robust Contrastive Learning With Theory Guarantee

Contrastive learning (CL) is a self-supervised training paradigm that allows us to extract meaningful features without any label information. A typical CL framework is divided into two phases, where it first tries to learn the features from unlabelled data, and then uses those features to train a linear classifier with the labeled data. While a fair amount of existing theoretical works have analyzed how the unsupervised loss in the first phase can support the supervised loss in the second phase, none has examined the connection between the unsupervised loss and the robust supervised loss, which can shed light on how to construct an effective unsupervised loss for the first phase of CL. To fill this gap, our work develops rigorous theories to dissect and identify which components in the unsupervised loss can help improve the robust supervised loss and conduct proper experiments to verify our findings.
On graded u-nil clean rings

In this paper we introduce and study the class of graded U-nil clean rings, as a generalization of graded nil-good class defined in [3]. We also investigate the transfer of the graded U-nil cleaness to matrix rings, and to graded group rings as well. The question of when trivial ring extensions are graded U-nil clean is also investigated.
An Empathetic User-Centric Chatbot for Emotional Support

This paper explores the intersection of Otome Culture and artificial intelligence, particularly focusing on how Otome-oriented games fulfill the emotional needs of young women. These games, which are deeply rooted in a subcultural understanding of love, provide players with feelings of satisfaction, companionship, and protection through carefully crafted narrative structures and character development. With the proliferation of Large Language Models (LLMs), there is an opportunity to transcend traditional static game narratives and create dynamic, emotionally responsive interactions. We present a case study of Tears of Themis, where we have integrated LLM technology to enhance the interactive experience. Our approach involves augmenting existing game narratives with a Question and Answer (QA) system, enriched through data augmentation and emotional enhancement techniques, resulting in a chatbot that offers realistic and supportive companionship.
The efficacy of environmental impact assessment on development proposals with respect to cost and time in South Africa: A three case study review

3 ACKNOWLEDGEMENT 4 CHAPTER 1: INTRODUCTION 11 1.1 Background 11 1.2 Objectives 15 1.3 Research Question 15 1.4 Methodology 16 1.5 Limitations and Scope of Study 17 1.6 Structure of the Report 17 CHAPTER 2: LITERATURE REVIEW 19 2.
Breakthrough made in dating of the geological record

Sedimentary cycles reflect climatic oscillations that are ultimately controlled by the Earth's orbital cycles. Therefore, sedimentary cycles can be used to construct astronomical timescales. Using this method, astronomical timescales have been established for the last 12 million years that are more acurate and have a much higher resolution than previous geological timescales. Such timescales are fundamental to an increasing number of applications in many disciplines of Earth science. Researchers are now working to extend the astronomical timescale to earlier time intervals and establish similar time-scales for the continental record. Time is an indispensable tool for Earth scientists striving to understand all kinds of processes and determine rates of change. More than a century ago, just before the invention of radiometric dating, G.K. Gilbert realized that astronomically forced cyclicity in marine sedimentary archives can be used to estimate the duration of parts of the geological record. His estimates suggested that our planet was much older than the 100 million years (or even 20 m.y., by some accounts) that had been calculated by a thermodynamic cooling model of the Earth.
TRANSITION FROM CLINICAL PRACTITIONER TO ACADEMIC EDUCATOR: THE EXPERIENCES OF RADIOGRAPHY LECTURERS IN NIGERIA.

Background: Clinical educators are essential in radiography education programmes. Transitioning from clinical practicing radiographers to academic educators is a rewarding journey. This study was designed to examine the experience of Radiography Lecturers in Nigerian universities who converted from radiography practitioners to academic educators. Materials and methods: A Google form questionnaire-based study design was conducted among 35 radiography lecturers in Nigeria. Only lecturers in Nigerian Universities who had worked as radiography clinical practitioners before transitioning to academics and consented to participate in this study were included. The questionnaire captured responses on socio-demographic variables, challenges, barriers, and successes of transition from practitioners to academic educators. Results: The majority 17(48.6%) of the respondents earned an income of above #251,000.00 as practicing radiographers. Of the 35 respondents, the majority 12(34.3%) each respectively earned #101,000.00 -#150,000.00 and #151,000.00 - #200,000.00 as an academic radiographers. The majority 17(48.6%) of the respondents agreed that the main challenge they are encountering as academic educators is rigorous research activities. Family issues affected the smooth transition process as 23(65.7%) of the respondents agreed to that. The majority 18(51.4%) perceived mentorship as the key factor responsible for a smooth transition from practice to academic. There was a statistically significant relationship between gender and challenges encountered by the responders ({chi}2 = 28.194, p = 0.00). Conclusion: The respondents experienced different challenges as they transitioned from clinical practice to academia. Mentorship is the key factor that militated against the smooth transition process. There was a statistically significant relationship between gender and challenges encountered by the responders
MicrobeMod: A computational toolkit for identifying prokaryotic methylation and restriction-modification with nanopore sequencing

Bacteria and archaea use restriction-modification (R-M) systems to distinguish self from foreign DNA by methylating their genomes with DNA methyltransferases with diverse sequence specificities, and these immunity systems often vary at the strain level. Identifying active methylation patterns and R-M systems can reveal barriers to the introduction of recombinant DNA or phage infection. Here, we present the computational MicrobeMod toolkit for identifying 5mC and 6mA methylation sequence motifs and R-M systems in bacterial genomes using nanopore sequencing of native DNA. We benchmark this approach on a set of reference E. coli strains expressing methyltransferases with known specificities. We then applied these analyses to 31 diverse bacterial and archaeal organisms to reveal the methylation patterns of strains with previously unexplored epigenetics, finding that prokaryotic 5-methylcytosine may be more common than previously reported. In summary, MicrobeMod can rapidly reveal new epigenetics within a prokaryotic genome sequenced with Oxford Nanopore R10.4. 1 flow cells at sequencing depths as low as 10x and only requires native DNA. This toolkit can be used to advance fundamental knowledge of bacterial methylation and guide strategies to overcome R-M barriers of genetic tractability in non-model microbes.
Continuous evolution of user-defined genes at 1-million-times the genomic mutation rate

When nature maintains or evolves a gene’s function over millions of years at scale, it produces a diversity of homologous sequences whose patterns of conservation and change contain rich structural, functional, and historical information about the gene. However, natural gene diversity likely excludes vast regions of functional sequence space and includes phylogenetic and evolutionary eccentricities, limiting what information we can extract. We introduce an accessible experimental approach for compressing long-term gene evolution to laboratory timescales, allowing for the direct observation of extensive adaptation and divergence followed by inference of structural, functional, and environmental constraints for any selectable gene. To enable this approach, we developed a new orthogonal DNA replication (OrthoRep) system that durably hypermutates chosen genes at a rate of >10−4 substitutions per base in vivo. When OrthoRep was used to evolve a conditionally essential maladapted enzyme, we obtained thousands of unique multi-mutation sequences with many pairs >60 amino acids apart (>15% divergence), revealing known and new factors influencing enzyme adaptation. The fitness of evolved sequences was not predictable by advanced machine learning models trained on natural variation. We suggest that OrthoRep supports the prospective and systematic discovery of constraints shaping gene evolution, uncovering of new regions in fitness landscapes, and general applications in biomolecular engineering.
DIAGENETIC HISTORY OF WOOLLY MAMMOTH (MAMMUTHUS PRIMIGENIUS) SKELETAL REMAINS FROM THE ARCHAEOLOGICAL SITE CRACOW SPADZISTA STREET (B), SOUTHERN POLAND

ABSTRACT Skeletal remains of woolly mammoths have been studied using polarizing microscopy, SEM, XRD, and FTIR to characterize their diagenetic history. Formation of different secondary minerals in the bones is related to changing conditions of chemical diagenesis, both in the sediment and in the bone itself. Bone voids are commonly infilled with calcite and/or carbonate sediment, and dentinal tubules are coated or infilled with secondary apatite. The latter may have formed during the life of the organism. Some osteocyte lacunae were observed to be coated with Fe-Mn (hydroxy)oxides. The average hydroxylapatite Ca/P ratios are higher (1.78–2.10) than in stoichiometric hydroxylapatite. Hydroxylapatite crystallinity indices are generally low at 0.06–0.12, as expected for the young bones. Some of the bones are partly altered by microbial attack. Different postdepositional events affecting the bones (recrystallization of apatite, bacterial alterations, mineral and sediment infillings and cracking) were distinguished and their succession proposed.
Estimates of the valuation of travel time savings in Switzerland obtained from pooled data

Reliable measures of the valuation of travel time savings (VTTS) are a crucial input into policy planning in any regional or national context. However, the evidence from secondary or parallel VTTS studies often differs from those of official or national studies. In this paper, we present evidence from a study aimed at merging evidence from four separate VTTS studies conducted in Switzerland. The analysis shows the potential of models estimated jointly on the combined data, and produces very stable results that should be more representative of the overall population. Hess, S. , Erath, A. and Axhausen, K.W. 3
GexMolGen: Cross-modal Generation of Hit-like Molecules via Large Language Model Encoding of Gene Expression Signatures

The design of custom drugs with specific biological activity is a extremely difficult task, but it holds the potential to generate molecules without the discovery of target genes, beyond the modern paradigm of drug discovery. Traditional search methods rely heavily on existing recorded perturbation experiments, which is costly and lacks generalization. To overcome this limitation, we propose GexMol-Gen (Gene Expression-based Molecule Generator), a novel model that generates hit-like molecules using gene expression signatures derived from both the initial and desired states of gene expression. Our approach follows a “first-align-then-generate” strategy where we align the gene expression signatures and molecules within a mapping space, enabling a smooth transition from the former to the latter. The transformed molecular embeddings are then decoded to molecule graphs. In this framework, we employ an advanced single-cell large language model to allow flexibility in genetic modal input. We also pre-train a scaffold-based molecule model to improve efficiency, by guaranteeing all generated molecules are 100% valid. Empirical studies demonstrate that our model outperforms traditional search methods and offers unique advantages over existing deep learning methods. Overall, our model attempts to explore the chemical and biological correlations in order to facilitate precision medicine. The usage code for GexMolGen have been released on https://github.com/Bunnybeibei/GexMolGen.
Therapeutic targeting of immunometabolism in Alzheimer’s disease reveals a critical reliance on Hexokinase 2 dosage on microglial activation and disease progression

Microgliosis and neuroinflammation are prominent features of Alzheimer’s disease (AD). Disease-responsive microglia meet their increased energy demand by reprogramming metabolism, specifically, switching to favor glycolysis over oxidative phosphorylation. Thus, targeting of microglial immunometabolism might be of therapeutic benefit for treating AD, providing novel and often well understood immune pathways and their newly recognized actions in AD. We report that in the brains of 5xFAD mice and postmortem brains of AD patients, we found a significant increase in the levels of Hexokinase 2 (HK2), an enzyme that supports inflammatory responses by rapidly increasing glycolysis. Moreover, binding of HK2 to mitochondria has been reported to regulate inflammation by preventing mitochondrial dysfunction and NLRP3 inflammasome activation, suggesting that its inflammatory role extends beyond its glycolytic activity. Here we report, that HK2 antagonism selectively affects microglial phenotypes and disease progression in a gene-dose dependent manner. Paradoxically, complete loss of HK2 fails to improve AD progression by exacerbating inflammasome activity while its haploinsufficiency results in reduced pathology and improved cognition in the 5XFAD mice. We propose that the partial antagonism of HK2, is effective in slowed disease progression and inflammation through a non-metabolic mechanism associated with the modulation of NFKβ signaling, through its cytosolic target IKBα. The complete loss of HK2 affects additional inflammatory mechanisms associated to mitochondrial dysfunction. Highlights Hexokinase 2, the first and rate-limiting enzyme of glycolysis, is specifically upregulated in plaque-associated microglia of AD mice models and in the postmortem cortex of human AD patients. Microglia haploinsufficient in HK2 exhibit reduced amyloid burden and inflammation as well as improved cognition in a mouse model of AD. Paradoxically, the complete loss of HK2 results in opposite effects, by exacerbating inflammation. Lonidamine, an anticancer drug that inhibits HK2, mimics the salutary effects of HK2 haploinsufficiency in the 5xFAD mice, but only in males during the early stages of disease. HK2 deletion induced mitochondrial dysfunction associated to increased expression of inflammasome elements and IL-1β. HK2 partial antagonism exerts beneficial effects independent of its energetic or mitochondrial role, likely through cytosolic stabilization of IκBα and inhibition of the NF-κB pathway, leading to reduced proinflammatory gene expression.
Exploring priority and year effects on plant diversity, productivity and vertical root distribution: first insights from a grassland field experiment

The order of arrival of plant species during community assembly can affect how species interact with each other. These so-called priority effects can have strong implications for the structure and functioning of plant communities. However, the extent to which the strength, direction, and persistence of priority effects are modulated by weather conditions during plant establishment (‘year effects’) is not well known. Here we present the first results from a long-term field experiment (POEM: PriOrity Effects Mechanisms) initiated in 2020 in Northern Germany to test how plant functional group (PFG) order of arrival and the year of initiation of an experiment interactively affect the structure and functioning of nutrient-poor dry acidic grasslands, both above and belowground. To do this, we established the same experiment, manipulating the order of arrival of forbs, grasses and legumes on the same site, but in different years. We found that time since establishment was a stronger driver of plant community composition than PFG order of arrival and year of initiation. These three factors interactively affected plant species diversity, with the effect of PFG order of arrival on plant species richness depending on time since establishment. Year of initiation, not PFG order of arrival, was the strongest driver of aboveground community productivity. Although we did not find any effect of PFG order of arrival on root productivity, it had a strong impact on the vertical distribution of roots. Communities where grasses were sown first rooted more shallowly than communities in which forbs or legumes were sown first. Synthesis: Our results demonstrate that plant order of arrival and year effects jointly affect plant diversity and species composition, with time since establishment also playing an important role. While year effects were more important than plant order of arrival in modulating aboveground biomass production in our nutrient-poor grassland, we showed that plant order of arrival can strongly affect the vertical distribution of roots, with communities in which forbs or legumes were sown first rooting deeper than grasses-first communities. These results suggest that a deeper understanding of priority and year effects is needed to better predict restoration outcomes.