Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Methodology for Intensive Larviculture of Atlantic Croakers

AbstractThe Atlantic Croaker Micropogonias undulatus (Sciaenidae) is a candidate species for marine baitfish aquaculture in the southeastern United States because of its high value and common use as live bait by recreational fishers. However, an efficient larviculture procedure has not been reported to date; development of such a procedure was the impetus for this study. Embryos were obtained from captive broodstock that were induced to spawn volitionally by using a single injection of a luteinizing hormone releasing hormone agonist. Larvae were cultured at low density (initial density = 6.4 larvae/L) via intensive culture methods, including the use of recirculating filtration systems and of rotifers, brine shrimp Artemia spp. , and micropellets as larval foods. The trial was performed in six 1,100-L tanks at a salinity of 14–29‰, with average rearing temperatures of 23.6°C and 24.6°C. At the completion of the study (39 d posthatch), mean SLs were 24.7 mm (SE = 0.738) for larvae cultured at 24.6°C and 23.0.
How primordial magnetic fields shrink galaxies

As one of the prime contributors to the interstellar medium energy budget, magnetic fields naturally play a part in shaping the evolution of galaxies. Galactic magnetic fields can originate from strong primordial magnetic fields provided these latter remain below current observational upper limits. To understand how such magnetic fields would affect the global morphological and dynamical properties of galaxies, we use a suite of high-resolution constrained transport magneto-hydrodynamic cosmological zoom simulations where we vary the initial magnetic field strength and configuration along with the prescription for stellar feedback. We find that strong primordial magnetic fields delay the onset of star formation and drain the rotational support of the galaxy, diminishing the radial size of the galactic disk and driving a higher amount of gas towards the centre. This is also reflected in mock UVJ observations by an increase in the light profile concentration of the galaxy. We explore the possible mechanisms behind such a reduction in angular momentum, focusing on magnetic braking. Finally, noticing that the effects of primordial magnetic fields are amplified in the presence of stellar feedback, we briefly discuss whether the changes we measure would also be expected for galactic magnetic fields of non-primordial origin.
Tidal evolution of exoplanetary systems hosting potentially habitable exoplanets. The cases of LHS-1140 b-c and K2-18 b-c

We present a model to study secularly and tidally evolving three-body systems composed by two low-mass planets orbiting a star, in the case where the bodies rotation axes are always perpendicular to the orbital plane. The tidal theory allows us to study the spin and orbit evolution of both stiff Earth-like planets and predominantly gaseous Neptune-like planets. The model is applied to study two recently-discovered exoplanetary systems containing potentially habitable exoplanets (PHE): LHS-1140 b-c and K2-18 b-c. For the former system, we show that both LHS-1140 b and c must be in nearly-circular orbits. For K2-18 b-c, the combined analysis of orbital evolution timescales with the current eccentricity estimation of K2-18 b allows us to conclude that the inner planet (K2-18 c) must be a Neptune-like gaseous body. Only this would allow for the eccentricity of K2-18 b to be in the range of values estimated in recent works ($e=0.20 \pm 0.08$), provided that the uniform viscosity coefficient of K2-18 b is greater than $2.4 \times 10^{19} \ \textrm{Pa s}$ (which is a value characteristic of stiff bodies) and supposing that such system has an age of some Gyr.
Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit

Social media discussion of COVID-19 provides a rich source of information into how the virus affects people's lives that is qualitatively different from traditional public health datasets. In particular, when individuals self-report their experiences over the course of the virus on social media, it can allow for identification of the emotions each stage of symptoms engenders in the patient. Posts to the Reddit forum r/COVID19Positive contain first-hand accounts from COVID-19 positive patients, giving insight into personal struggles with the virus. These posts often feature a temporal structure indicating the number of days after developing symptoms the text refers to. Using topic modelling and sentiment analysis, we quantify the change in discussion of COVID-19 throughout individuals' experiences for the first 14 days since symptom onset. Discourse on early symptoms such as fever, cough, and sore throat was concentrated towards the beginning of the posts, while language indicating breathing issues peaked around ten days. Some conversation around critical cases was also identified and appeared at a roughly constant rate. We identified two clear clusters of positive and negative emotions associated with the evolution of these symptoms and mapped their relationships. Our results provide a perspective on the patient experience of COVID-19 that complements other medical data streams and can potentially reveal when mental health issues might appear.
The Frankfurt Latin Lexicon: From Morphological Expansion and Word Embeddings to SemioGraphs

In this article we present the Frankfurt Latin Lexicon (FLL), a lexical resource for Medieval Latin that is used both for the lemmatization of Latin texts and for the post-editing of lemmatizations. We describe recent advances in the development of lemmatizers and test them against the Capitularies corpus (comprising Frankish royal edicts, mid-6th to mid-9th century), a corpus created as a reference for processing Medieval Latin. We also consider the post-correction of lemmatizations using a limited crowdsourcing process aimed at continuous review and updating of the FLL. Starting from the texts resulting from this lemmatization process, we describe the extension of the FLL by means of word embeddings, whose interactive traversing by means of SemioGraphs completes the digital enhanced hermeneutic circle. In this way, the article argues for a more comprehensive understanding of lemmatization, encompassing classical machine learning as well as intellectual post-corrections and, in particular, human computation in the form of interpretation processes based on graph representations of the underlying lexical resources.
$S$-packing colorings of distance graphs $G(\mathbb{Z},\{2,t\})$

Given a graph $G$ and a non-decreasing sequence $S=(a_1,a_2,\ldots)$ of positive integers, the mapping $f:V(G) \rightarrow \{1,\ldots,k\}$ is an $S$-packing $k$-coloring of $G$ if for any distinct vertices $u,v\in V(G)$ with $f(u)=f(v)=i$ the distance between $u$ and $v$ in $G$ is greater than $a_i$. The smallest $k$ such that $G$ has an $S$-packing $k$-coloring is the $S$-packing chromatic number, $\chi_S(G)$, of $G$. In this paper, we consider the distance graphs $G(\mathbb{Z},\{2,t\})$, where $t>1$ is an odd integer, which has $\mathbb{Z}$ as its vertex set, and $i,j\in\mathbb{Z}$ are adjacent if $|i-j|\in\{2,t\}$. We determine the $S$-packing chromatic numbers of the graphs $G(\mathbb{Z},\{2,t\})$, where $S$ is any sequence with $a_i\in\{1,2\}$ for all $i$. In addition, we give lower and upper bounds for the $d$-distance chromatic numbers of the distance graphs $G(\mathbb{Z},\{2,t\})$, which in the cases $d\ge t-3$ give the exact values. Implications for the corresponding $S$-packing chromatic numbers of the circulant graphs are also discussed.
Self-calibration and antenna grouping for bistatic oceanographic High-Frequency Radars

We propose two concepts for the significant improvement of surface current mapping with bistatic oceanographic High-Frequency Radars. These ameliorations pertain to the azimuthal processing of radar data with linear or quasi-linear antenna arrays. The first idea is to take advantage of the remote transmitter to perform an automatic correction of the complex gains of the receiving antennas based on the analysis of the signal received in the direct path. This direct signal can be found at the zero-Doppler and minimal range cell in the Range-Doppler representation. We term this adjustment as "self-calibration" of the receiving array, as it can be performed in real-time without any specific action from the operator. The second idea consists in applying a Direction Finding technique (instead of traditional Beam Forming) not only to the full array of antenna but also to subarrays made of a smaller number of sequential antennas, a method which we refer to as "antenna grouping". The combination of self-calibration and antenna grouping makes it possible to obtain high-resolution maps with full coverage, thereby combining the respective merits of Direction Finding and Beam Forming techniques. In addition the method is found robust to missing antennas in the array. These techniques are applied to and illustrated with the multistatic High-Frequency Radar network in Toulon.
Computationally efficient sparse clustering

We study statistical and computational limits of clustering when the means of the centres are sparse and their dimension is possibly much larger than the sample size. Our theoretical analysis focuses on the model $X_i=z_i \theta +\varepsilon _{i}, \ z_i \in \{-1,1\}, \ \varepsilon _i \thicksim \mathcal{N}(0, I)$, which has two clusters with centres $\theta $ and $-\theta $. We provide a finite sample analysis of a new sparse clustering algorithm based on sparse Principal Component Analysis (PCA) and show that it achieves the minimax optimal misclustering rate in the regime $\|\theta \| \rightarrow \infty $. Our results require the sparsity to grow slower than the square root of the sample size. Using a recent framework for computational lower bounds—the low-degree likelihood ratio—we give evidence that this condition is necessary for any polynomial-time clustering algorithm to succeed below the Baik-Ben Arous-Péché (BBP) threshold. This complements the existing evidence based on reductions and statistical query lower bounds. Compared with these existing results, we cover a wider set of parameter regimes and give a more precise understanding of the runtime required and the misclustering error achievable. Our results imply that a large class of tests based on low-degree polynomials fail to solve even the weak testing task.
Near Instance-Optimality in Differential Privacy

We develop two notions of instance optimality in differential privacy, inspired by classical statistical theory: one by defining a local minimax risk and the other by considering unbiased mechanisms and analogizing the Cramer-Rao bound, and we show that the local modulus of continuity of the estimand of interest completely determines these quantities. We also develop a complementary collection mechanisms, which we term the inverse sensitivity mechanisms, which are instance optimal (or nearly instance optimal) for a large class of estimands. Moreover, these mechanisms uniformly outperform the smooth sensitivity framework on each instance for several function classes of interest, including real-valued continuous functions. We carefully present two instantiations of the mechanisms for median and robust regression estimation with corresponding experiments.
Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation

Recent studies have revealed a number of pathologies of neural machine translation (NMT) systems. Hypotheses explaining these mostly suggest there is something fundamentally wrong with NMT as a model or its training algorithm, maximum likelihood estimation (MLE). Most of this evidence was gathered using maximum a posteriori (MAP) decoding, a decision rule aimed at identifying the highest-scoring translation, i.e. the mode. We argue that the evidence corroborates the inadequacy of MAP decoding more than casts doubt on the model and its training algorithm. In this work, we show that translation distributions do reproduce various statistics of the data well, but that beam search strays from such statistics. We show that some of the known pathologies and biases of NMT are due to MAP decoding and not to NMT’s statistical assumptions nor MLE. In particular, we show that the most likely translations under the model accumulate so little probability mass that the mode can be considered essentially arbitrary. We therefore advocate for the use of decision rules that take into account the translation distribution holistically. We show that an approximation to minimum Bayes risk decoding gives competitive results confirming that NMT models do capture important aspects of translation well in expectation.
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning

Performing ML computation on private data while maintaining data privacy aka Privacy-preserving Machine Learning (PPML) is an emergent field of research. Recently, PPML has seen a visible shift towards the adoption of Secure Outsourced Computation (SOC) paradigm, due to the heavy computation that it entails. In the SOC paradigm, computation is outsourced to a set of powerful and specially equipped servers that provide service on a pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for a range of ML algorithms in SOC setting, that guarantees output delivery to the users irrespective of any adversarial behaviour. Robustness, a highly desirable feature, evokes user participation without the fear of denial of service. At the heart of our framework lies a highly-efficient, maliciously-secure, three-party computation (3PC) over rings that provides guaranteed output delivery (GOD) in the honest-majority setting. To the best of our knowledge, SWIFT is the first robust and efficient PPML framework in the 3PC setting. SWIFT is as fast as the best-known 3PC framework BLAZE (Patra et al. NDSS'20) which only achieves fairness. Fairness ensures either all or none receive the output, whereas GOD ensures guaranteed output delivery no matter what. We extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20) and twice faster than the best-known robust 4PC framework FLASH (Byali et al. PETS'20). We demonstrate the practical relevance of our framework by benchmarking two important applications-- i) ML algorithms: Logistic Regression and Neural Network, and ii) Biometric matching, both over a 64-bit ring in WAN setting. Our readings reflect our claims as above.
Beware of commonly used approximations. Part I. Errors in forecasts

In the era of precision cosmology, establishing the correct magnitude of statistical errors in cosmological parameters is of crucial importance. However, widely used approximations in galaxy surveys analyses can lead to parameter uncertainties that are grossly mis-estimated, even in a regime where the theory is well understood (e.g., linear scales). These approximations can be introduced at three different levels: in the form of the likelihood, in the theoretical modelling of the observable and in the numerical computation of the observable. Their consequences are important both in data analysis through e.g., Markov Chain Monte Carlo parameter inference, and when survey instrument and strategy are designed and their constraining power on cosmological parameters is forecasted, for instance using Fisher matrix analyses. In this work, considering the galaxy angular power spectrum as the target observable, we report one example of approximation for each of such three categories: neglecting off-diagonal terms in the covariance matrix, neglecting cosmic magnification and using the Limber approximation on large scales. We show that these commonly used approximations affect the robustness of the analysis and lead, perhaps counter-intuitively, to unacceptably large mis-estimates of parameters errors (from few 10% up to few 100%) and correlations. Furthermore, these approximations might even spoil the benefits of the nascent multi-tracer and multi-messenger cosmology. Hence we recommend that the type of analysis presented here should be repeated for every approximation adopted in survey design or data analysis, to quantify how it may affect the results. To this aim, we have developed Multi_CLASS, a new extension of CLASS that includes the angular power spectrum for multiple (galaxy and other tracers such as gravitational waves) populations. The public release of Multi_CLASS is associated with this paper.
Blood Levels of Hemoglobin and Lactic Acid in some Freshwater Fishes Following Exercise

During the summers of 1953 and 1954 the effects of forced activity on two blood constituents were investigated in seven species of fresh-water fishes, representing six families. The fish were hatchery-raised or captured from lakes in the southern Okanagan Valley, and the experiments were carried out in the Summerland Trout Hatchery, Summerland, B.C. Following forced exercise for 15 minutes in water at 11.5 °C. the hemoglobin level increased significantly in only one species and the lactic acid increased significantly in all species. There appears to be a correlation between the average value of lactic acid following exercise and the activity of the fish, and also with the position of the species with respect to summer water temperature.
Emergent magnetic behavior in the frustrated 
Yb3Ga5O12
 garnet

We report neutron scattering, magnetic susceptibility and Monte Carlo theoretical analysis to verify the short range nature of the magnetic structure and spin-spin correlations in a Yb$_3$Ga$_5$O$_{12}$ single crystal. The quantum spin state of Yb$^{3+}$ in Yb$_3$Ga$_5$O$_{12}$ is verified. The quantum spins organise into a short ranged emergent director state for T $<$ 0.4 K derived from anisotropy and near neighbour exchange. We derive the magnitude of the near neighbour exchange interactions $0.6\; {\rm K} < J_1 < 0.7\; {\rm K}, J_2 = 0.12$~K and the magnitude of the dipolar exchange interaction, $D$, in the range $0.18 < D < 0.21$ K. Certain aspects of the broad experimental dataset can be modelled using a $J_1D$ model with ferromagnetic near neighbour spin-spin correlations while other aspects of the data can be accurately reproduced using a $J_1J_2D$ model with antiferromagnetic near neighbour spin-spin correlation. As such, although we do not quantify all the relevant exchange interactions we nevertheless provide a strong basis for the understanding of the complex Hamiltonian required to fully describe the magnetic state of Yb$_3$Ga$_5$O$_{12}$.
Paternal genetic contribution to offspring condition predicted by size of male secondary sexual character

Whether females can obtain genetic benefits from mate choice is contentious, and the main problem faced by previous studies of natural populations is that many factors other than paternal genes contribute to offspring fitness. Here, we use comparisons between sets of naturally occurring maternal half–sibling collared flycatchers, Ficedula albicollis, to control for this problem. We show, first, that there are paternal genetic effects on nestling fledging condition, a character related to fitness in this species. Further, the magnitude of the paternal genetic contribution to this character is related to the size of a condition–dependent male secondary sexual character. Our results demonstrate that genetic benefits from mate choice can be predicted by the size of a secondary sexual character, and therefore provide direct support for indicator models of sexual selection.
The True Metabolizable Energy Values of Fats and Fat Mixtures

Abstract Two experiments were made in which the true metabolizable energy (T.M.E.) values of various lipid materials were measured. Several fats had T.M. E. values greater than their gross energy values which suggests that fats may interact to allow increased utilization of the energy of other dietary components. The observed T.M. E. values of several fat mixtures were greater than the sums of the T.M. E. values of their component parts. High T.M. E. values were often associated with low levels of saturated fatty acids and high levels of dienoic acids; however, there were exceptions which lead to the conclusion that fatty acid composition as a predictor of T.M. E. may only be valid within certain groups of fats. The location of saturated fatty acids on the glycerol molecule was shown to affect T.M. E. values.
Towards Finite-State Morphology of Kurdish

Morphological analysis is the study of the formation and structure of words. It plays a crucial role in various tasks in Natural Language Processing (NLP) and Computational Linguistics (CL) such as machine translation and text and speech generation. Kurdish is a less-resourced multi-dialect Indo-European language with highly inflectional morphology. In this paper, as the first attempt of its kind, the morphology of the Kurdish language (Sorani dialect) is described from a computational point of view. We extract morphological rules which are transformed into finite-state transducers for generating and analyzing words. The result of this research assists in conducting studies on language generation for Kurdish and enhances the Information Retrieval (IR) capacity for the language while leveraging the Kurdish NLP and CL into a more advanced computational level.
INFECTION AND IMMUNITY

Limit the abstract to 250 words or fewer and concisely summarize the basic content of the paper without presenting extensive experimental details. Avoid abbreviations and references, and do not include diagrams. Include a succinct description of the microbe and its associated disease. When it is essential to include a reference, use the same format as shown for the References section but omit the article title. Because the abstract will be published separately by abstracting services, it must be complete and understandable without reference to the text. Introduction. The introduction should supply sufficient background information to allow the reader to understand and evaluate the results of the present study without referring to previous publications on the topic. The introduction 8 2008 IAI INSTRUCTIONS TO AUTHORS INFECT. IMMUN.
Cosmology-independent Estimate of the Hubble Constant and Spatial Curvature using Time-delay Lenses and Quasars

With the distance sum rule in the Friedmann–Lemaître–Robertson–Walker metric, model-independent constraints on both the Hubble constant H0 and spatial curvature can be obtained using strong lensing time-delay data and Type Ia supernovae (SNe Ia) luminosity distances. This method is limited by the relatively low redshifts of SNe Ia, however. Here, we propose using quasars as distance indicators, extending the coverage to encompass the redshift range of strong lensing systems. We provide a novel and improved method of determining H0 and simultaneously. By applying this technique to the time-delay measurements of seven strong lensing systems and the known ultraviolet versus X-ray luminosity correlation of quasars, we constrain the possible values of both H0 and , and find that km and . The measured is consistent with zero spatial curvature, indicating that there is no significant deviation from a flat universe. If we use flatness as a prior, we infer that km , representing a precision of 2.5%. If we further combine these data with the 1048 current Pantheon SNe Ia, our model-independent constraints can be further improved to km and . In every case, we find that the Hubble constant measured with this technique is strongly consistent with the value (∼74 km ) measured using the local distance ladder, as opposed to the value optimized by Planck.