Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining theoretical Corrections to Gamow-Teller Transition Rates

We propose two novel constraints for the theoretical corrections to Gamow-Teller transition rates. The first, derived from a two-level model, predicts forbidden regions within the plane defined by the isospin-mixing correction, $\delta_{C1}$, and the ratio, $\eta$, of the isospin-symmetry Gamow-Teller matrix elements between the upper and lower admixed states. It serves as a filter for the theoretical calculations, particularly effective for small values of $|\eta|$. The other employs experimental $ft$ values, incorporating the upper admixed states, and exploits mirror symmetry to eliminate isospin-invariant and nuclear structure-independent quantities. This approach not only offers an alternative mean for collectively testing theoretical corrections but also, as a byproduct, enables the extraction of $\eta^2$. This provides another sensitive test for the isospin-conserving component of nuclear Hamiltonian. Our investigation reveals a substantial cancellation of among radiative correction contributions in these tests.
N=2 supersymmetry in the twistor description of higher-spin holography

We study the holographic duality between higher-spin (HS) gravity in 4d and free vector models in 3d, with special attention to the role of N=2 supersymmetry (SUSY). For the type-A bosonic bulk theory, dual to spin-0 fields on the boundary, there exists a twistor-space description; this maps both single-trace boundary operators and linearized bulk fields to spacetime-independent twistor functions, whose HS-algebra products compute all boundary correlators. Here, we extend this description to the type-B bosonic theory (dual to spin-1/2 fields on the boundary), and to the supersymmetric theory containing both. A key role is played by boundary bilocals, which in type-A are dual to the Didenko-Vasiliev 1/2-BPS"black hole". We extend this to an infinite family of linearized 1/2-BPS"black hole"solutions. Remarkably, the full supersymmetric theory (along with the SUSY generators) fits in the same space of twistor functions as the type-A theory. Instead of two sets of bosonic bulk fields, the formalism sees one set of linearized fields, but with both types of boundary data allowed.
Efficient Prompt Caching via Embedding Similarity

Large language models (LLMs) have achieved huge success in numerous natural language process (NLP) tasks. However, it faces the challenge of significant resource consumption during inference. In this paper, we aim to improve the inference efficiency of LLMs by prompt caching, i.e., if the current prompt can be answered by the same response of a previous prompt, one can directly utilize that previous response without calling the LLM. Specifically, we focus on the prediction accuracy of prompt caching for single-round question-answering tasks via embedding similarity. The existing embeddings of prompts mostly focus on whether two prompts are semantically similar, which is not necessarily equivalent to whether the same response can answer them. Therefore, we propose a distillation-based method to fine-tune the existing embeddings for better caching prediction. Theoretically, we provide finite-sample guarantees for the convergence of our method under different types of loss functions. Empirically, we carefully construct a hard dataset based on Kwiatkowski et al. (2019) where the existing embedding model (Wang et al., 2022) only achieves an AUC of 0.51. We then fine-tune the above embedding model, which significantly improves the AUC of caching prediction from 0.51 to 0.81. We also conduct simulations demonstrating that our trained models achieve better caching efficiency than the previous embedding model.
Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers

Despite their remarkable effectiveness and broad application, the drivers of success underlying ensembles of trees are still not fully understood. In this paper, we highlight how interpreting tree ensembles as adaptive and self-regularizing smoothers can provide new intuition and deeper insight to this topic. We use this perspective to show that, when studied as smoothers, randomized tree ensembles not only make predictions that are quantifiably more smooth than the predictions of the individual trees they consist of, but also further regulate their smoothness at test-time based on the dissimilarity between testing and training inputs. First, we use this insight to revisit, refine and reconcile two recent explanations of forest success by providing a new way of quantifying the conjectured behaviors of tree ensembles objectively by measuring the effective degree of smoothing they imply. Then, we move beyond existing explanations for the mechanisms by which tree ensembles improve upon individual trees and challenge the popular wisdom that the superior performance of forests should be understood as a consequence of variance reduction alone. We argue that the current high-level dichotomy into bias- and variance-reduction prevalent in statistics is insufficient to understand tree ensembles -- because the prevailing definition of bias does not capture differences in the expressivity of the hypothesis classes formed by trees and forests. Instead, we show that forests can improve upon trees by three distinct mechanisms that are usually implicitly entangled. In particular, we demonstrate that the smoothing effect of ensembling can reduce variance in predictions due to noise in outcome generation, reduce variability in the quality of the learned function given fixed input data and reduce potential bias in learnable functions by enriching the available hypothesis space.
Big Bang Nucleosynthesis constraints on the Energy-Momentum Squared Gravity: The $\mathbb{T}^{2}$ model

A scale-independent energy-momentum squared gravity (EMSG) allows different gravitational couplings for different types of sources and has been proven to have interesting implications in cosmology. In this paper, the Big Bang Nucleosynthesis (BBN) formalism and the latest observational constraints are being used in order to extract constraints on this class of modified gravity models. The model has been constrained using the light element reaction rates. Using the tight constraint from BBN on the correction term in the Friedman equation due to EMSG, we find a significant deviation in the matter power spectrum of the cosmic microwave background (CMB) even for a very small allowed value of the modification parameter, thus pointing out the sensitivity of the matter power spectrum at the small scales.
Intermediate-Task Transfer Learning for Peer Review Score Prediction

Peer review is a fundamental component of the academic publishing process, ensuring the quality and validity of research findings. However, predicting peer-review aspect scores accurately can be challenging due to the small size of publically available datasets on the target aspect of scores. To address this issue, we propose an intermediate-task transfer learning method to further improve the performance of pre-trained models. The method assumes an intermediate task that is related to the target task to learn beneficial features before fine-tuning it on a target task. Our experiments demonstrate that intermediate-task transfer learning helps improve the performance of the pre-trained model on peer review score prediction. Our code is available at https://github.com/panitan-m/ peerreview-intermediate-trans .
Rethinking the Role of Entity Type in Relation Classification

Relation Classification (RC)—the task of identifying the relation between a pair of target entities—is a fundamental sub-task of information extraction. RC models built on top of entity information are prevalent, with different variants using entity information, especially entity type information, differently. However, RC models are often benchmarked on datasets that human annotators provide near-perfect entity information, and, state-of-the-art results are reported using gold entity type information. We believe there is a need to understand how the effectiveness of RC models is affected by the correctness of entity type information because in practice this information is provided by imperfect entity recognition models. Our re-sults on six datasets across four domains show that although using gold entity type improves the effectiveness of RC models, incorrect entity types may cause large effectiveness drops on some (but not all) datasets. We propose using Pointwise Mutual Information (PMI) to identify datasets on which RC models may be negatively impacted by incorrect entity type information.
Speech Synthesis Model Based on Face Landmarks

Lip reading recognition aims to predict what people are saying based on the movements of their lips. Most previous works used continuous images to represent lip movements and predict the corresponding textual contents, which does not achieve good performance. In this work, we explore a new approach to synthesizing audio through lip movements by introducing face landmarks for representing the motion features of the face in 3D space and synthesizing the corresponding audio results directly. We propose the FaceLandmarks2Wav model for a preliminary implementation of the above idea. The experimental results confirm that face landmarks can adequately represent facial movement features, and the structure of Face-Landmarks2Wav could synthesize speech re-sults close to natural human voices, only using the face landmarks sequence.
Valla: Standardizing and Benchmarking Authorship Attribution and Verification Through Empirical Evaluation and Comparative Analysis

Despite decades of research on authorship attri-bution (AA) and authorship verification (AV), inconsistent dataset splits/filtering and mis-matched evaluation methods make it difficult to assess the state of the art. In this paper, we present a survey of the fields, resolve points of confusion, introduce V ALLA that standard-izes and benchmarks AA/AV datasets and metrics, provide a large-scale empirical evaluation, and provide apples-to-apples comparisons be-tween existing methods. We evaluate eight promising methods on fifteen datasets (including distribution shifted challenge sets) and introduce a new dataset based on texts archived by Project Gutenberg. Surprisingly, we find that a traditional Ngram-based model performs best on 5 (of 7) AA tasks, achieving an average macro-accuracy of 76 . 50% (compared to 66 . 71% for a BERT-based model). However, on the two AA datasets with the greatest number of words per author, as well as on the AV datasets, BERT-based models perform best. While AV methods are easily applied to AA, they are seldom included as baselines in AA papers. We show that through the application of hard-negative mining, AV methods are competitive alternatives to AA methods. V ALLA and all experiment code can be found here: https:/
A dendritic mechanism for balancing synaptic flexibility and stability

Biological and artificial neural networks learn by modifying synaptic weights, but it is unclear how these systems can retain previous knowledge and also acquire new information. Here we show that cortical pyramidal neurons can solve this plasticity-versus-stability dilemma by differentially regulating synaptic plasticity at distinct dendritic compartments. Oblique dendrites of adult mouse layer 5b cortical pyramidal neurons selectively received monosynaptic thalamic input, integrated linearly, and— surprisingly—lacked synaptic potentiation. In contrast, basal dendrites, which do not receive thalamic input, exhibited conventional NMDA receptor-mediated supralinear integration and synaptic potentiation. Congruently, spiny synapses on oblique branches showed low structural plasticity in vivo. A selective decline in NMDAR activity and expression at synapses on oblique dendrites was controlled by an experience-dependent critical period. Our results demonstrate a new biological mechanism for how single neurons can safeguard a set of inputs from ongoing plasticity by altering synaptic properties at distinct dendritic domains.
Dissociating endogenous and exogenous delta activity during natural speech comprehension

Decoding human speech requires the brain to segment the incoming acoustic signal into meaningful linguistic units, ranging from syllables and words to phrases. Integrating these linguistic constituents into a coherent percept sets the root of compositional meaning and hence understanding. One important cue for segmentation in natural speech are prosodic cues, such as pauses, but their interplay with higher-level linguistic processing is still unknown. Here we dissociate the neural tracking of prosodic pauses from the segmentation of multi-word chunks using magnetoencephalography (MEG). We find that manipulating the regularity of pauses disrupts slow speech-brain tracking bilaterally in auditory areas (below 2 Hz) and in turn increases left-lateralized coherence of higher frequency auditory activity at speech onsets (around 25 - 45 Hz). Critically, we also find that multi-word chunks—defined as short, coherent bundles of inter-word dependencies—are processed through the rhythmic fluctuations of low frequency activity (below 2 Hz) bilaterally and independently of prosodic cues. Importantly, low-frequency alignment at chunk onsets increases the accuracy of an encoding model in bilateral auditory and frontal areas, while controlling for the effect of acoustics. Our findings provide novel insights into the neural basis of speech perception, demonstrating that both acoustic features (prosodic cues) and abstract processing at the multi-word timescale are underpinned independently by low-frequency electrophysiological brain activity.
Influence of Rising Costs of Energy on the Energy Consumer Behavior of International Students in Scotland

The rise in energy prices across Scotland and globally has impacted households in several ways including economically, socially and materially. However, little is known on the effects of high energy tariffs on a unique growing segment of the Scottish population – international university students. Therefore, this quantitative study investigates the impact of rising energy costs on consumer behaviour of international university students in Scotland. Hinged on the consumer behaviour theory, this research analyses the effects of energy costs on curtailment and efficiency behaviours evident among international students in Scotland. Factors such as personal, cultural, social, economic and pro-environmental perception were taken into cognizance in the design of the research questionnaire and data collection from220 participants. Descriptive statistics, multivariable linear regression, and ANOVA were used for data analysis. A pivotal revelation was that all participants experienced an elevation in energy costs post-immigration, with 70.5% observing a significant surge. In exploring energy consumption habits, the data underscored a compelling inclination towards energy-saving practices, with 72.73% consistently turning off lights when exiting rooms. Also, the linear regression analysis illuminated the profound implications of rising energy costs on consumption behaviours, revealing an unexpected positive correlation (p < 0.0001). "Much" energy-saving changes are linked to significantly lower curtailment in consumption patterns (p < 0.0001) compared to "little/nothing" changes (p = 1). Furthermore, the ANOVA indicates that households relying solely on gas as an energy source exhibited significantly (p<0.05) distinct energy costs compared to those utilising both gas and electricity. Therefore, this research not only underscores the complexities of energy consumption among international students but also offers actionable insights for economically prudent energy decisions. Likewise, the results necessitate the need for enhanced energy conservation education and awareness for this demographic. Hence, these findings contribute to our understanding of the relationship between energy consumption andrising costs among the international student communities.
Exploring Motivational Factors Among High Performing Real Estate Brokers: The Swedish Case

The purpose of this exploratory case study is to analyze the role of relational motivation for high-performing real estate brokers in Sweden. The concept of relational motivation, or relatedness, is explored through both affective and calculative commitment. The data in this study come from in-depth interviews with real estate brokers. The results show that the brokers are committed to their clients, in an affective and a calculative way. All of the participants expressed signs of being emotionally involved in their customer relations, often in terms of genuine interest in, and caring for, their clients. The existence of relatedness toward customers in a brokerage context contributes to the discussion on what motivates high-performing real estate brokers. By viewing relatedness as a trigger to engage in more customer relationships, the intrinsic motivation can be seen as a strong antecedent to individual performance.
Modelling evapotranspiration using the modified Penman-Monteith equation and MODIS data over the Albany Thicket in South Africa

Evapotranspiration (ET) is one of the least understood components of the water cycle, particularly in data scarce areas. In a context of climate change, evaluating water vapour fluxes of a particular area is crucial to help understand dynamics in water balance. In data scarce areas, ET modelling becomes vital. The study modelled ET using the Penman-Monteith- Leuning (PML) equation forced by Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) and MODIS albedo with ancillary meteorological data from an automatic weather station. The study area is located on the Albany Thicket (AT) biome of South Africa and the dominant plant species is Portulacaria afra. The biggest challenge to the implementation of the PML is the parameterisation of surface and stomatal conductance. We tested the use of volumetric soil water content (fswc), precipitation and equilibrium evaporation ratio (fzhang) and soil drying after precipitation (f) approaches to account for the fraction (f) of evaporation from the soil. ET from the model was validated using an eddy covariance system (EC). Post processing of eddy covariance data was implement using EddyPro software. The fdrying method performed better with a root mean square observations standard deviation ratio (RSR) of 0.97. The results suggest that modelling ET over the AT vegetation is delicate owing to strong vegetation phenological control of the ET process. The convergent evolution of the vegetation has resulted in high plant available water than the model can detect. It is vital to quantify plant available water in order to improve ET modelling in thicket vegetation.
STUDY SOME VIRULENCE FACTORS AND ANTIBIOTIC SUSCEPTIBILITY OF SOME CANDIDA SPP. ISOLATED FROM DIFFERENT CLINICAL SITES

: The current study aimed to study some virulence factors and test the antibiotic susceptibility of some fungi isolated from different clinical sites. This study was conducted in the Postgraduate Studies Laboratory / College of Science - University of Kirkuk. (220) samples were collected from chronic wound infections for patients hospitalized in Kirkuk General Hospital and Azadi Teaching Hospital in the city of Kirkuk. Each group included (69) samples from bed sore patients and (62) Samples from surgical infections (58) samples from diabetic foot patients and (31) samples from burn patients in the period between September to November 2023. Three types of yeast were isolated and diagnosed: Candida albicans, Candida tropical, and Candida krusei, and the most frequent ones were It is the yeast C. albicans, with 28(50%), Candida tropical, 18(32%), and C. krusei, 10(18%). Regarding virulence factors, C. albicans yeast showed the ability to form germ tubes and chlamydial spores. Chlamydospore formation While C. krusei and C. tropicalis were able to form surface growth on SDB liquid medium, the types of yeast showed different colors by growing them on chromo agar, as C. albicans was light green and C. krusei was pink. Light while C. tropicalis yeast has a metallic blue color. Regarding the drug sensitivity test, the results showed that
CAPITAL STRUCTURE AND FIRM PERFORMANCE: EVIDENCE FROM CEMENT SECTOR OF DHAKA STOCK EXCHANGE LIMITED

The paper aims to investigate the impact of capital structure on firm performance in Bangladesh. The study used panel data for the period of 15 years from 2001 to 2015 and 7 listed cement companies operating in the country. The study used short term debt to total assets and long term debt to total asset, as proxy for capital structure and, return on equity (ROE) and return on asset (ROA) as measure of performance of the companies. Random effect model has been used to estimate the relationship between the firm debt and firm performance. The study documents a significant positive association between the short-term debt to total assets ratio and firm performance measured in terms of ROA and ROE.
2023 Distribution-Dependent Rates for Multi-Distribution Learning

To address the needs of modeling uncertainty in sensitive machine learning applications, the setup of distributionally robust optimization (DRO) seeks good performance uniformly across a variety of tasks. The recent multi-distribution learning (MDL) framework [AHZ23] tackles this objective in a dynamic interaction with the environment, where the learner has sampling access to each target distribution. Drawing inspiration from the ﬁeld of pure-exploration multi-armed bandits, we provide distribution-dependent guarantees in the MDL regime, that scale with suboptimality gaps and result in superior dependence on the sample size when compared to the existing distribution-independent analyses. We investigate two non-adaptive strategies, uniform and non-uniform exploration, and present non-asymptotic regret bounds using novel tools from empirical process theory. Furthermore, we devise an adaptive optimistic algorithm, LCB-DR, that showcases enhanced dependence on the gaps, mirroring the contrast between uniform and optimistic allocation in the multi-armed bandit literature.
Prevalence of HIV Infection Among Blood Donors and Patients Undergoing Surgical Procedures in Al-Hussien University Hospital

Background : HIV is a blood-borne virus that is primarily spread through sexual contact, sharing intravenous drug syringes, and mother-to-child transmission (MTCT). HIV-1 or HIV-2 infections are the primary causes of HIV illness. Aim : To determine the prevalence of HIV among blood donors and patients undergoing surgical procedures in Al-Hussien University Hospital, identify the risk factors, and predict the route of transmission. Patients and methods : This is a retrospective, record-based study, which was done during the period from June 2021 to June 2022. All people undergoing surgical procedures and all people who were submitted for blood donation at Al-Hussien University Hospital were included. HIV screening test (ELISA) was done on all people, followed by determination of HIV prevalence among blood donors and patients undergoing surgical procedures. Results : Among the total number of 12,017 blood donors, 18 (0.15%) were serologically positive for HIV infection, and of the total of 6781 patients undergoing surgical procedures 12 (0,18%) were serologically positive for HIV infection. There is a high HIV prevalence among young persons and males. There is a high HIV prevalence among unemployed persons, manual workers, and single persons. Conclusion : The prevalence of HIV infection among blood donors and patients undergoing a surgical operation in our studied group is relatively low, but still present which necessitates routine screening of donor blood for HIV to ensure safety and also a screening of patients undergoing invasive interventions for HIV is necessary to ensure the safety of both patients and health-care workers and to decrease the risk of HIV transmission.