Miracle-dz/UniFAR-FTUs huggingface dataset:
Main nutritional deficiencies

Summary Nutrition is the source of energy that is required to carry out all the processes of human body. A balanced diet is a combination of both macro- and micronutrients. “Nutritional inadequacy” involves an intake of nutrients that is lower than the estimated average requirement, whereas “nutritional deficiency” consists of severely reduced levels of one or more nutrients, making the body unable to normally perform its functions and thus leading to an increased risk of several diseases like cancer, diabetes, and heart disease. Malnutrition could be caused by environmental factors, like food scarcity, as well as disease conditions, like anorexia nervosa, fasting, swallowing inability, persistent vomiting, impaired digestion, intestinal malabsorption, or other chronic diseases. Nutritional biomarkers – like serum or plasma levels of nutrients such as folate, vitamin C, B vitamins, vitamin D, selenium, copper, zinc – could be used for the evaluation of nutrient intake and dietary exposure. Macronutrients deficiencies could cause kwashiorkor, marasmus, ketosis, growth retardation, wound healing, and increased infection susceptibility, whereas micronutrient – like iron, folate, zinc, iodine, and vitamin A – deficiencies lead to intellectual impairment, poor growth, perinatal complications, degenerative diseases associated with aging and higher morbidity and mortality. Preventing macro- and micronutrient deficiency is crucial and this could be achieved through supplementation and food-based approaches.
Transportation of Water Vapor into the Tibetan Plateau in the Case of a Passing Synoptic-Scale Trough

The passing of a synoptic trough is expected to contribute strongly to water vapor (WV) transport processes from the Indian Ocean to the Tibetan Plateau (TP) during the monsoon season. However, the pathway of the WV into the plateau associated with passing a trough and its contribution to the WV budget over the TP are not clear. In this study, the processes of WV transport in the case of a passing trough in 1998 were analyzed using GAME reanalysis data and the numerical model, focusing especially on the WV transportation pattern in the Indian Monsoon region and the diurnal variation of WV intrusion from south of the Himalayas to the TP. WV advection into the TP was larger in the case of a passing synoptic trough than in the case of a prevailing Tibetan High. Sub-continental scale circulation for the two synoptic types corresponded with active/break phase of the Indian monsoon. In case of prevailing Tibetan High, a cyclonic circulation with a low-pressure area over India, associated with active Indian monsoon phase, prevented the WV intrusion into the TP in the middle troposphere. However, in the case of the trough that corresponded with break phase of Indian Monsoon, WV was transported directly from Arabian Sea to the southern foot of the Himalayas with a northward shift of the low-level monsoon westerly and it intruded into the southeastern TP. Numerical experiments showed that the WV transport process was composed of multiple steps in the case of the passing trough. WV was transported by the monsoon westerly to the southern foot of the Himalayas at 1500 m above sea level (a.s.1.). The moist air mass reached south of the TP with at about 5500 m a.s. 1. during the noon to evening because of the development of a mixing layer and the enhancement of an upslope wind in southern slope of the Himalayas. A moist southwesterly flow was converged latitudinally in the southeastern TP because of the dry northwesterly flow prevailing in the rear of the passing trough over the TP. This convergence area expanded northward from mid-afternoon into the night and disappeared early next morning.
Dynamic Yield Strengths of B4C, BeO, and Al2O3 Ceramics

The dynamic yield strengths for several ceramics were measured under shock‐wave compression. The Hugoniot elastic limit (HEL), taken as the amplitude of the observed elastic precursor traveling ahead of the compressional wave, was 154 and 82 kbar for B4C and BeO ceramics, respectively, and ranged from 61 to 134 kbar for several Al2O3 ceramics. These values apply to 6‐mm‐thick samples; in some cases there were variations with sample thickness. The offset of the Hugoniot from the isotropic compression curve in the stress‐volume plane indicates a nonzero yield strength above the HEL for Al2O3. This yield strength is somewhat less than the yield strength at the HEL. The compaction of porous ceramics is found to follow a quadratic variation with stress, full compaction being reached at about four times the HEL.
The Degree and Codegree Threshold for Linear Triangle Covering in 3-Graphs

Given two $k$-uniform hypergraphs $F$ and $G$, we say that $G$ has an $F$-covering if every vertex in $G$ is contained in a copy of $F$. For $1\le i \le k-1$, let $c_i(n,F)$ be the least integer such that every $n$-vertex $k$-uniform hypergraph $G$ with $\delta_i(G)> c_i(n,F)$ has an $F$-covering. The covering problem has been systematically studied by Falgas-Ravry and Zhao [Codegree thresholds for covering 3-uniform hypergraphs, [SIAM J. Discrete Math., 2016]. Last year, Falgas-Ravry, Markström, and Zhao [Triangle-degrees in graphs and tetrahedron coverings in 3-graphs, Combinatorics, Probability and Computing, 2021] asymptotically determined $c_1(n, F)$ when $F$ is the generalized triangle. In this note, we give the exact value of $c_2(n, F)$ and asymptotically determine $c_1(n, F)$ when $F$ is the linear triangle $C_6^3$, where $C_6^3$ is the 3-uniform hypergraph with vertex set $\{v_1,v_2,v_3,v_4,v_5,v_6\}$ and edge set $\{v_1v_2v_3,v_3v_4v_5,v_5v_6v_1\}$.
Barium stars as tracers of s-process nucleosynthesis in AGB stars

Context. Barium (Ba) stars are characterised by an abundance of heavy elements made by the slow neutron capture process (s-process). This peculiar observed signature is due to the mass transfer from a stellar companion, bound in a binary stellar system, to the Ba star observed today. The signature is created when the stellar companion is an asymptotic giant branch (AGB) star. Aims. We aim to analyse the abundance pattern of 169 Ba stars using machine learning techniques and the AGB final surface abundances predicted by the FRUITY and Monash stellar models. Methods. We developed machine learning algorithms that use the abundance pattern of Ba stars as input to classify the initial mass and metallicity of each Ba star’s companion star using stellar model predictions. We used two algorithms. The first exploits neural networks to recognise patterns, and the second is a nearest-neighbour algorithm that focuses on finding the AGB model that predicts the final surface abundances closest to the observed Ba star values. In the second algorithm, we included the error bars and observational uncertainties in order to find the best-fit model. The classification process was based on the abundances of Fe, Rb, Sr, Zr, Ru, Nd, Ce, Sm, and Eu. We selected these elements by systematically removing s-process elements from our AGB model abundance distributions and identifying the elements whose removal had the biggest positive effect on the classification. We excluded Nb, Y, Mo, and La. Our final classification combined the output of both algorithms to identify an initial mass and metallicity range for each Ba star companion. Results. With our analysis tools, we identified the main properties for 166 of the 169 Ba stars in the stellar sample. The classifications based on both stellar sets of AGB final abundances show similar distributions, with an average initial mass of M = 2.23 M⊙ and 2.34 M⊙ and an average [Fe/H] = −0.21 and −0.11, respectively. We investigated why the removal of Nb, Y, Mo, and La improves our classification and identified 43 stars for which the exclusion had the biggest effect. We found that these stars have statistically significant and different abundances for these elements compared to the other Ba stars in our sample. We discuss the possible reasons for these differences in the abundance patterns.
Open boundary conditions for forced waves in a rotating fluid

Previous studies on open boundary conditions in unbounded rotating fluid flows have concentrated on how to accurately simulate the outflow of free waves through an open boundary. In most limited area integrations of rotating fluids, however, the generated waves are forced rather than free, in which case the Sommerfeld radiation condition, applied in previous studies, is not valid. A new open boundary condition applicable to rotating stratified fluid flows is suggested below, which allows the fluid to be forced everywhere, including the open boundaries. To prove the applicability of the suggested open boundary conditions, some numerical experiments with a fluid contained within an infinitely long channel are considered. The new open boundary conditions are applied at two cross sections along the channel.
Intent Recognition in Conversational Recommender Systems

Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, these systems have used Machine Learning (ML) and Natural Language Processing (NLP) to analyze large volumes of customer feedback and engagement data. The goal is to understand customers in context and provide meaningful answers across various channels. Despite multiple advances in Conversational Artificial Intelligence (AI) and Recommender Systems (RS), it is still challenging to understand the intent behind customer questions during the customer journey. To address this challenge, in this paper, we study and analyze the recent work in Conversational Recommender Systems (CRS) in general and, more specifically, in chatbot-based CRS. We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition. Since performance evaluation is achieved based on different ML models, we use transformer base models to evaluate the proposed approach using a labelled dialogue dataset (MSDialogue) of question-answering interactions between information seekers and answer providers.
Black hole greybody factors from Korteweg–de Vries integrals: Theory

The dynamics of perturbed non-rotating black holes (BHs) can be described in terms of master equations of the wave type with a potential. In the frequency domain, the master equations become time-independent Schr\"odinger equations with no discrete spectrum. It has been recently shown that these wave equations possess an infinite number of symmetries that correspond to the flow of the infinite hierarchy of Korteweg-de Vries (KdV) equations. As a consequence, the infinite set of associated conserved quantities, the KdV integrals, are the same for all the different master equations that we can consider. In this paper we show that the BH scattering reflection and transmission coefficients characterizing the continuous spectrum can be fully determined via a moment problem, in such a way that the KdV integrals provide the momenta of a distribution function depending only on the reflection coefficient. We also discuss the existence and uniqueness of solutions, strategies to solve the moment problem, and finally show the case of the P\"oschl-Teller potential where all the steps can be carried out analytically.
An automated approach to extracting positive and negative clinical research results

Failure is common in clinical trials since the successful failures presented in negative results always indicate the ways that should not be taken. In this paper, we proposed an automated approach to extracting positive and negative clinical research results by introducing a PICOE (Population, Intervention, Comparation, Outcome, and Effect) framework to represent randomized controlled trials (RCT) reports, where E indicates the effect between a specific I and O. We developed a pipeline to extract and assign the corresponding statistical effect to a specific I-O pair from natural language RCT reports. The extraction models achieved a high degree of accuracy for ICO and E descriptive words extraction through two rounds of training. By defining a threshold of p-value, we find in all Covid-19 related intervention-outcomes pairs with statistical tests, negative results account for nearly 40%. We believe that this observation is noteworthy since they are extracted from the published literature, in which there is an inherent risk of reporting bias, preferring to report positive results rather than negative results. We provided a tool to systematically understand the current level of clinical evidence by distinguishing negative results from the positive results.
X-ray spectral evolution in the ultraluminous X-ray source M33 X-8

The bright ultraluminous X-ray source (ULX), M33 X-8, has been observed several times by XMM–Newton, providing us with a rare opportunity to ‘flux bin’ the spectral data and search for changes in the average X-ray spectrum with flux level. The aggregated X-ray spectra appear unlike standard sub-Eddington accretion state spectra which, alongside the lack of discernible variability at any energy, argues strongly against conventional two-component, sub-Eddington models. Although the lack of variability could be consistent with disc-dominated spectra, sub-Eddington disc models are not sufficiently broad to explain the observed spectra. Fits with a ∼ Eddington accretion rate slim disc model are acceptable, but the fits show that the temperature decreases with flux, contrary to expectations, and this is accompanied by the appearance of a harder tail to the spectrum. Applying a suitable two-component model reveals that the disc becomes cooler and less advection dominated as the X-ray flux increases, and this is allied to the emergence of an optically thick Comptonization medium. We present a scenario in which this is explained by the onset of a radiatively driven wind from the innermost regions of the accretion disc, as M33 X-8 exceeds the Eddington limit. Furthermore, we argue that the direct evolution of this spectrum with increasing luminosity (and hence radiation pressure) leads naturally to the two-component spectra seen in more luminous ULXs.
Semisimple elements and the little Weyl group of real semisimple $Z_m$-graded Lie algebras

In this paper we introduce the notion of the algebraic θ -group of a semisimple Z m -graded Lie algebra over a ﬁeld k of characteristic 0 whose identity component is the θ -group. Using this, we extend a number of results concerning structure of semisimple elements in complex semisimple Z m -graded Lie algebras and some results concerning real (and complex) semisimple Lie algebras to the case of real (and complex) semisimple Z m -graded Lie algebras, using Galois cohomology theory and ad hoc techniques. In particular, we parameterize the conjugacy classes of Cartan subspaces in a real semisimple Z m -graded Lie algebra g in terms of Galois cohomology. We prove that if m is a prime number or g C is a complex semisimple Z m -graded Lie algebra of maximal rank then the centralizer Z g C ( p ) of a homogeneous semisimple element p and the stabilizer W p of p under the Weyl group action deﬁne each other. As a result, we obtain a number of consequences on the conjugacy classes of homogeneous semisimple elements in g C and in its real forms.
Precision pulse shape simulation for proton detection at the Nab experiment

The Nab experiment at Oak Ridge National Laboratory, USA, aims to measure the beta-antineutrino angular correlation following neutron $\beta$ decay to an anticipated precision of approximately 0.1\%. The proton momentum is reconstructed through proton time-of-flight measurements, and potential systematic biases in the timing reconstruction due to detector effects must be controlled at the nanosecond level. We present a thorough and detailed semiconductor and quasiparticle transport simulation effort to provide precise pulse shapes, and report on relevant systematic effects and potential measurement schemes.
X-Paste: Revisit Copy-Paste at Scale with CLIP and StableDiffusion

Copy-Paste is a simple and effective data augmentation strategy for instance segmentation. By randomly pasting object instances onto new background images, it creates new training data for free and signiﬁcantly boosts the segmentation performance, especially for rare object categories. Although diverse, high-quality object instances used in Copy-Paste result in more performance gain, previous works utilize object instances either from human-annotated instance segmentation datasets or rendered from 3D object models, and both approaches are too expensive to scale up to obtain good diversity. In this paper, we revisit Copy-Paste at scale with the power of newly emerged zero-shot recognition models (e.g., CLIP) and text2image models (e.g., Sta-bleDiffusion). We demonstrate for the ﬁrst time that using a text2image model to generate images or zero-shot recognition model to ﬁlter noisily crawled images for different object categories is a feasible way to make Copy-Paste truly scalable. To make such success happen, we design a data acquisition and processing framework, dubbed “X-Paste”, upon which a systematic study is conducted. On the LVIS dataset, X-Paste provides impressive improvements over the strong baseline CenterNet2 with Swin-L as the backbone. Speciﬁcally, it archives +2.6 box AP and +2.1 mask AP gains on all classes and even more signiﬁcant gains with +6.8 box AP +6.5 mask AP on long-tail classes.
Kappa vacua: Infinite number of new vacua in two-dimensional quantum field theory

We uncover an infinite number of vacua in two-dimensional quantum field theory, the Klein-Gordon field for simplicity, by conceiving a new mode that is classified by a real positive parameter $\kappa$. We show each mode has a distinct vacuum, say $\kappa$-vacuum. This new mode is a generalization of the Unruh-Minkowski mode. Moreover, the Minkowski and Rindler vacua are special cases of the $\kappa$-vacuum for $\kappa = 1$ and $\kappa \rightarrow \infty$, respectively.
Statistical analysis of the ionospheric response during geomagnetic storm conditions over South Africa using ionosonde and GPS data

This paper presents a statistical analysis of ionospheric response over ionosonde stations Grahamstown (33.3°S, 26.5°E, geographic) and Madimbo (22.4°S, 30.9°E, geographic), South Africa, during geomagnetic storm conditions which occurred during the period 1996–2011. Such a climatological study is important in establishing local ionospheric behavior trend which later forms a basis for accurate modeling and forecasting electron density and critical frequency of the F2 layer (foF2) useful for high‐frequency communication. The analysis was done using foF2 and total electron content (TEC), and to identify the geomagnetically disturbed conditions, the Dst index with a storm criterion of Dst ≤−50 nT was used. Results show a strong solar cycle dependence with negative ionospheric storm effects following the solar cycle and positive ionospheric storm effects occurring most frequently during solar minimum. Seasonally, negative and positive ionospheric storm effects occurred most in summer (63.24%) and in winter (53.62%), respectively. An important finding is that only negative ionospheric storms were observed during great geomagnetic storm activity (Dst ≤−350 nT). For periods when both foF2 and TEC data (from colocated ionosonde and GPS receiver stations) were available, a similar response in terms of variational trend was observed. Hence, GPS data can be used to effectively identify the ionospheric response in the absence of ionosonde data.
DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing

Recent advances on deep learning models come at the price of formidable training cost. The increasing model size is one of the root causes, but another less-emphasized fact is that data scale is actually increasing at a similar speed as model scale, and the training cost is proportional to both of them. Compared to the rapidly evolving model architecture, how to efficiently use the training data (especially for the expensive foundation model pretraining) is both less explored and difficult to realize due to the lack of a convenient framework that focus on data efficiency capabilities. To this end, we present DeepSpeed Data Efficiency, a framework that makes better use of data, increases training efficiency, and improves model quality. Specifically, we propose and combine two data efficiency techniques: efficient data sampling via a general curriculum learning library, and efficient data routing via a novel random layerwise token dropping technique. For GPT-3 1.3B language model pretraining, our work achieves 12.5x less data/time/cost ($3.7K if rent on Azure), while still maintaining 95% of model quality compared to baseline with full data and cost ($46.3K). For GPT-3 1.3B and BERT-large pretraining, our work can also achieve the same model quality with up to 2x less data/time/cost, or achieve better model quality under same data/time/cost. DeepSpeed Data Efficiency is easy to use and tune, enabling us to easily apply it and verify its benefit on additional tasks including GPT-3 MoE model pretraining and small-scale GPT-2/ViT finetuning.
Almost finite modules

Assume that ϕ(R, m, k) → (S, n, l) is a local homomorphism between commutative noetherian local rings R and S. We say that an S-module M is almost finite over R if it is finitely generated over S (the R-structure on M is induced by ϕ). We investigate the homological behaviour of such modules, as well as various properties of the rings R and S in the presence of an almost finite module of finite flat dimension over R.
Curiosity Creates Diversity in Policy Search

When searching for policies, reward-sparse environments often lack sufficient information about which behaviors to improve upon or avoid. In such environments, the policy search process is bound to blindly search for reward-yielding transitions and no early reward can bias this search in one direction or another. A way to overcome this is to use intrinsic motivation in order to explore new transitions until a reward is found. In this work, we use a recently proposed definition of intrinsic motivation, Curiosity, in an evolutionary policy search method. We propose Curiosity-ES,1 an evolutionary strategy adapted to use Curiosity as a fitness metric. We compare Curiosity-ES with other evolutionary algorithms intended for exploration, as well as with Curiosity-based reinforcement learning, and find that Curiosity-ES can generate higher diversity without the need for an explicit diversity criterion and leads to more policies which find reward.
Decidable objects and molecular toposes

We study several suﬃcient conditions for the molecularity/local-connectedness of geometric morphisms. In particuar, we show that if S is a Boolean topos then, for every hyperconnected essential geometric morphism p : E → S such that the leftmost adjoint p ! preserves ﬁnite products, p is molecular and p ∗ : S → E coincides with the full subcategory of decidable objects in E . We also characterize the reﬂections between categories with ﬁnite limits that induce molecular maps between the respective presheaf toposes. As a corollary we establish the molecularity of certain geometric morphisms between Gaeta toposes.