Miracle-dz/UniFAR-FTUs huggingface dataset:
APPLICATION OF JITTER RADIATION: GAMMA-RAY BURST PROMPT POLARIZATION

A high degree of polarization of gamma-ray burst (GRB) prompt emission has been confirmed in recent years. In this paper, we apply jitter radiation to study the polarization feature of GRB prompt emission. In our framework, relativistic electrons are accelerated by turbulent acceleration. Random and small-scale magnetic fields are generated by turbulence. We further determine that the polarization property of GRB prompt emission is governed by the configuration of the random and small-scale magnetic fields. A two-dimensional compressed slab, which contains a stochastic magnetic field, is applied in our model. If the jitter condition is satisfied, the electron deflection angle in the magnetic field is very small and the electron trajectory can be treated as a straight line. A high degree of polarization can be achieved when the angle between the line of sight and the slab plane is small. Moreover, micro-emitters with mini-jet structures are considered to be within a bulk GRB jet. The jet “off-axis” effect is intensely sensitive to the observed polarization degree. We discuss the depolarization effect on GRB prompt emission and afterglow. We also speculate that the rapid variability of GRB prompt polarization may be correlated with the stochastic variability of the turbulent dynamo or the magnetic reconnection of plasmas.
Impact of accretion disk on the gravitational wave profile emitted from binary merger

In the present work, we study the emission of the gravitational wave from the extreme mass ratio inspiral containing an accretion disk around the central compact object. The disk exerts a hydrodynamic drag on the companion black hole. The emitted gravitational wave from such systems may be modified due to the modified motion of the companion by the influence of the hydrodynamic drag of the disk. In the present work, we investigate this issue using full general relativistic formalism and calculate the possible error introduced in the estimation of the mass of the black holes.
Do floating orbits in extreme mass ratio binary black holes exist

This paper examines the possibility of floating or non-decaying orbits for extreme mass ratio binary black holes. In the adiabatic approximation, valid in the extreme mass ratio case, if the orbital flux lost due to gravitational radiation reaction is compensated for by the orbital flux gained from the spins of the black holes via superradiant scattering (or, equivalently, tidal acceleration) the orbital decay would be stalled, causing the binary to "float". We show that this flux balance is not, in practice, possible for extreme mass ratio binary black holes with circular equatorial orbits; furthermore, adding eccentricity and inclination to the orbits will not significantly change this null result, thus ruling out the possibility of floating orbits for extreme mass ratio binary black holes. We also argue that binaries consisting of material bodies dense and massive enough to generate gravitational waves detectable by any kind of gravitational wave detector are also unlikely to float. Using a multipolar analysis, we show that a non-Kerr spacetime which could produce a floating orbit (given the same amount of tidal acceleration as in the case of a Kerr black hole) would need to be rapidly rotating prolate spheroid, which would be an exotic object indeed.
Representation Theory of Semisimple Groups: An Overview Based on Examples. .

In this classic work, Anthony W. Knapp offers a survey of representation theory of semisimple Lie groups in a way that reflects the spirit of the subject and corresponds to the natural learning process. This book is a model of exposition and an invaluable resource for both graduate students and researchers. Although theorems are always stated precisely, many illustrative examples or classes of examples are given. To support this unique approach, the author includes for the reader a useful 300-item bibliography and an extensive section of notes.
Traumatic Hemothorax Caused by Thoracic Wall and Intrathoracic Injuries: Clinical Outcomes of Transcatheter Systemic Artery Embolization

Purpose We aimed to assess the clinical efficacy of transcatheter arterial embolization (TAE) for treating hemothorax caused by chest trauma. Materials and Methods Between 2015 and 2019, 68 patients (56 male; mean age, 58.2 years) were transferred to our interventional unit for selective TAE to treat thoracic bleeding. We retrospectively investigated their demographics, angiographic findings, embolization techniques, technical and clinical success rates, and complications. Results Bleeding occurred mostly from the intercostal arteries (50%) and the internal mammary arteries (29.5%). Except one patient, TAE achieved technical success, defined as the immediate cessation of bleeding, in all the other patients. Four patients successfully underwent repeated TAE for delayed bleeding or increasing hematoma after the initial TAE. The clinical success rate, defined as no need for thoracotomy for hemostasis after TAE, was 92.6%. Five patients underwent post-embolization thoracotomy for hemostasis. No patient developed major TAE-related complications, such as cerebral infarction or quadriplegia. Conclusion TAE is a safe, effective and minimally invasive method for controlling thoracic wall and intrathoracic systemic arterial hemorrhage after thoracic trauma. TAE may be considered for patients with hemothorax without other concomitant injuries which require emergency surgery, or those who undergoing emergency TAE for abdominal or pelvic hemostasis.
AN ALGEBRAIC LINEAR RELATIVISTIC EQUATION OVER TWISTOR FIELDS WITH CONFINED STATES

As an example of a relativistic model of confinement, we build the Dirac equation for twistor fields with five-dimensional momentum belonging to a de-Sitter surface. The components of five- dimensional momentum are constructed as bilinear combinations of twistor variables and quantized via the Jordan map. The theory contains a universal scale parameter R of distance. The correspondence with an ordinary massless equation is established by making the parameter R infinite. In the quantum framework the equation is transformed to an eigenvalue problem, which holds a discrete spectrum of energy mc 2 < E < mc 2 . The particles obeying this equation are in algebraically confined bound states. Due to the internal structure of momentum, besides spin-angular momentum, a spin-energy-momentum appears. The explicit expressions for the non-vanishing tensor elements of momentum and angular momentum are obtained.
EXTENDED MAIN SEQUENCE TURNOFFS IN INTERMEDIATE-AGE STAR CLUSTERS: A CORRELATION BETWEEN TURNOFF WIDTH AND EARLY ESCAPE VELOCITY

We present a color–magnitude diagram analysis of deep Hubble Space Telescope imaging of a mass-limited sample of 18 intermediate-age (1–2 Gyr old) star clusters in the Magellanic Clouds, including eight clusters for which new data were obtained. We find that all star clusters in our sample feature extended main-sequence turnoff (eMSTO) regions that are wider than can be accounted for by a simple stellar population (including unresolved binary stars). FWHM widths of the MSTOs indicate age spreads of 200–550 Myr. We evaluate the dynamical evolution of clusters with and without initial mass segregation. Our main results are (1) the fraction of red clump (RC) stars in secondary RCs in eMSTO clusters scales with the fraction of MSTO stars having pseudo-ages of ≲1.35 Gyr; (2) the width of the pseudo-age distributions of eMSTO clusters is correlated with their central escape velocity vesc, both currently and at an age of 10 Myr. We find that these two results are unlikely to be reproduced by the effects of interactive binary stars or a range of stellar rotation velocities. We therefore argue that the eMSTO phenomenon is mainly caused by extended star formation within the clusters; and (3) we find that vesc ⩾ 15 km s−1 out to ages of at least 100 Myr for all clusters featuring eMSTOs, and vesc ⩽ 12 km s−1 at all ages for two lower-mass clusters in the same age range that do not show eMSTOs. We argue that eMSTOs only occur for clusters whose early escape velocities are higher than the wind velocities of stars that provide material from which second-generation stars can form. The threshold of 12–15 km s−1 is consistent with wind velocities of intermediate-mass asymptotic giant branch stars and massive binary stars in the literature.
Safety and Efficacy of Ultrasound-Guided Percutaneous Core Needle Biopsy of Pancreatic and Peripancreatic Lesions Adjacent to Critical Vessels

Purpose To evaluate the safety and efficacy of ultrasound-guided percutaneous core needle biopsy (USPCB) of pancreatic and peripancreatic lesions adjacent to critical vessels. Materials and Methods Data were collected retrospectively from 162 patients who underwent USPCB of the pancreas (n = 98), the peripancreatic area adjacent to the portal vein, the paraaortic area adjacent to pancreatic uncinate (n = 34), and lesions on the third duodenal portion (n = 30) during a 10-year period. An automated biopsy gun with an 18-gauge needle was used for biopsies under US guidance. The USPCB results were compared with those of the final follow- up imaging performed postoperatively. The diagnostic accuracy and major complication rate of the USPCB were calculated. Multiple factors were evaluated for the prediction of successful biopsies using univariate and multivariate analyses. Results The histopathologic diagnosis from USPCB was correct in 149 (92%) patients. The major complication rate was 3%. Four cases of mesenteric hematomas and one intramural hematoma of the duodenum occurred during the study period. The following factors were significantly associated with successful biopsies: a transmesenteric biopsy route rather than a transgastric or transenteric route; good visualization of targets; and evaluation of the entire US pathway. In addition, the number of biopsies required was less when the biopsy was successful. Conclusion USPCB demonstrated high diagnostic accuracy and a low complication rate for the histopathologic diagnosis of pancreatic and peripancreatic lesions adjacent to critical vessels.
Laboratory tests of the Galileon

The Galileon model is a ghost free scalar effective field theory containing higher derivative terms that are protected by the Galileon symmetry. The presence of a Vainshtein screening mechanism allows the scalar field to couple to matter without mediating unacceptably large fifth forces in the solar system. We describe how laboratory measurements of the Casimir effect and possible deviations from Newtonian gravity can be used to search for Galileon scalar fields. Current experimental measurements are used to bound a previously unconstrained combination of Galileon parameters.
Scale r_0 and the static potential from the CLS lattices

We report on the measurement of the static potential and the scale r_0 from HYP-smeared Wilson loops in two flavour QCD. We analyse the quark mass dependence of the potential and r_0 at three lattice spacings. We also compare the QCD static potential around distance r_0 with the static potential obtained from potential models.
CS-Insights: A System for Analyzing Computer Science Research

This paper presents CS-Insights, an interactive web application to analyze computer science publications from DBLP through multiple perspectives. The dedicated interfaces allow its users to identify trends in research activity, productivity, accessibility, author's productivity, venues' statistics, topics of interest, and the impact of computer science research on other fields. CS-Insightsis publicly available, and its modular architecture can be easily adapted to domains other than computer science.
Non-uniform Berry-Esseen theorem and Edgeworth expansions with applications to transport distances for weakly dependent random variables

We obtain non-uniform Berry-Esseen type estimates and Edgeworth expansions for several classes of weakly dependent sequences of random variables, including uniformly elliptic inhomogeneous Markov chains, random and time-varying (partially) hyperbolic or expanding dynamical systems, products of random matrices and some classes of local statistics.
Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning

The automation of the medical evidence acquisition and diagnosis process has recently attracted increasing attention in order to reduce the workload of doctors and democratize access to medical care. However, most works proposed in the machine learning literature focus solely on improving the prediction accuracy of a patient's pathology. We argue that this objective is insufficient to ensure doctors' acceptability of such systems. In their initial interaction with patients, doctors do not only focus on identifying the pathology a patient is suffering from; they instead generate a differential diagnosis (in the form of a short list of plausible diseases) because the medical evidence collected from patients is often insufficient to establish a final diagnosis. Moreover, doctors explicitly explore severe pathologies before potentially ruling them out from the differential, especially in acute care settings. Finally, for doctors to trust a system's recommendations, they need to understand how the gathered evidences led to the predicted diseases. In particular, interactions between a system and a patient need to emulate the reasoning of doctors. We therefore propose to model the evidence acquisition and automatic diagnosis tasks using a deep reinforcement learning framework that considers three essential aspects of a doctor's reasoning, namely generating a differential diagnosis using an exploration-confirmation approach while prioritizing severe pathologies. We propose metrics for evaluating interaction quality based on these three aspects. We show that our approach performs better than existing models while maintaining competitive pathology prediction accuracy.
Hybrid cluster-expansion and density-functional-theory approach for optical absorption in TiO_2

A combined approach of first-principles density-functional calculations and the systematic cluster-expansion scheme is presented. The dipole, quadrupole, and Coulomb matrix elements obtained from ab initio calculations are used as an input to the microscopic many-body theory of the excitonic optical response. To demonstrate the hybrid approach for a nontrivial semiconductor system, the near-bandgap excitonic optical absorption of rutile TiO2 is computed. Comparison with experiments yields strong evidence that the observed near-bandgap features are due to a dipole-forbidden but quadrupole-allowed 1s exciton state.
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment

We consider the estimation of average and counterfactual treatment effects, under two settings: back-door adjustment and front-door adjustment. The goal in both cases is to recover the treatment effect without having an access to a hidden confounder. This objective is attained by first estimating the conditional mean of the desired outcome variable given relevant covariates (the"first stage"regression), and then taking the (conditional) expectation of this function as a"second stage"procedure. We propose to compute these conditional expectations directly using a regression function to the learned input features of the first stage, thus avoiding the need for sampling or density estimation. All functions and features (and in particular, the output features in the second stage) are neural networks learned adaptively from data, with the sole requirement that the final layer of the first stage should be linear. The proposed method is shown to converge to the true causal parameter, and outperforms the recent state-of-the-art methods on challenging causal benchmarks, including settings involving high-dimensional image data.
Tutorial Note on Merging Matrix Elements with Parton Showers

In this short note, I introduce to essential conceptual features and main building blocks of matrix element merging algorithms, operating on fixed order calculations both at leading order and next-to-leading order. The intention is purely pedagogical, i.e. to familiarize the reader with the essential basic concepts in a concise way, thus serving as an introduction to beginners and other interested readers. Unitarization is discussed briefly. The tutorial is highly biased towards transverse momentum ordered parton showers, and in particular towards merging schemes as they are implemented in the PYTHIA 8 general purpose Monte Carlo generator.
That’s the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data

Pretraining multimodal models on Electronic Health Records (EHRs) provides a means of learning representations that can transfer to downstream tasks with minimal supervision. Recent multimodal models induce soft local alignments between image regions and sentences. This is of particular interest in the medical domain, where alignments might highlight regions in an image relevant to specific phenomena described in free-text. While past work has suggested that attention “heatmaps” can be interpreted in this manner, there has been little evaluation of such alignments. We compare alignments from a state-of-the-art multimodal (image and text) model for EHR with human annotations that link image regions to sentences. Our main finding is that the text has an often weak or unintuitive influence on attention; alignments do not consistently reflect basic anatomical information. Moreover, synthetic modifications — such as substituting “left” for “right” — do not substantially influence highlights. Simple techniques such as allowing the model to opt out of attending to the image and few-shot finetuning show promise in terms of their ability to improve alignments with very little or no supervision. We make our code and checkpoints open-source.
Tone prediction and orthographic conversion for Basaa

In this paper, we present a seq2seq approach for transliterating missionary Basaa orthographies into the official orthography. Our model uses pre-trained Basaa missionary and official orthography corpora using BERT. Since Basaa is a low-resource language, we have decided to use the mT5 model for our project. Before training our model, we pre-processed our corpora by eliminating one-to-one correspondences between spellings and unifying characters variably containing either one to two characters into single-character form. Our best mT5 model achieved a CER equal to 12.6747 and a WER equal to 40.1012.
Dynamical effects from anomalies: Modified electrodynamics in Weyl semimetals

We discuss the modified quantum electrodynamics from a time-reversal-breaking Weyl semimetal coupled with a $U(1)$ gauge (electromagnetic) field. A key role is played by the soft dispersion of the photons in a particular direction, say $\hat{z}$, due to the Hall conductivity of the Weyl semimetal. Due to the soft photon, the fermion velocity in $\hat{z}$ is logarithmically reduced under renormalization group flow, together with the fine structure constant. Meanwhile, fermions acquire a finite lifetime from spontaneous emission of the soft photon, namely the Cherenkov radiation. At low energy $E$, the inverse of the fermion lifetime scales as $\tau^{-1}\sim E/{\rm PolyLog}(E)$. Therefore, even though fermion quasiparticles are eventually well-defined at very low energy, over a wide intermediate energy window the Weyl semimetal behaves like a marginal Fermi liquid. Phenomenologically, our results are more relevant for emergent Weyl semimetals, where the fermions and photons all emerge from strongly correlated lattice systems. Possible experimental implications are discussed.
A Bayesian Optimization Framework for Finding Local Optima in Expensive Multi-Modal Functions

Bayesian optimization (BO) is a popular global optimization scheme for sample-efficient optimization in domains with expensive function evaluations. The existing BO techniques are capable of finding a single global optimum solution. However, finding a set of global and local optimum solutions is crucial in a wide range of real-world problems, as implementing some of the optimal solutions might not be feasible due to various practical restrictions (e.g., resource limitation, physical constraints, etc.). In such domains, if multiple solutions are known, the implementation can be quickly switched to another solution, and the best possible system performance can still be obtained. This paper develops a multimodal BO framework to effectively find a set of local/global solutions for expensive-to-evaluate multimodal objective functions. We consider the standard BO setting with Gaussian process regression representing the objective function. We analytically derive the joint distribution of the objective function and its first-order derivatives. This joint distribution is used in the body of the BO acquisition functions to search for local optima during the optimization process. We introduce variants of the well-known BO acquisition functions to the multimodal setting and demonstrate the performance of the proposed framework in locating a set of local optimum solutions using multiple optimization problems.
Walk a Mile in Their Shoes: a New Fairness Criterion for Machine Learning

The old empathetic adage, ``Walk a mile in their shoes,'' asks that one imagine the difficulties others may face. This suggests a new ML counterfactual fairness criterion, based on a \textit{group} level: How would members of a nonprotected group fare if their group were subject to conditions in some protected group? Instead of asking what sentence would a particular Caucasian convict receive if he were Black, take that notion to entire groups; e.g. how would the average sentence for all White convicts change if they were Black, but with their same White characteristics, e.g. same number of prior convictions? We frame the problem and study it empirically, for different datasets. Our approach also is a solution to the problem of covariate correlation with sensitive attributes.
Back to the features: assessing the discriminating power of future CMB missions on inflationary models

Future Cosmic Microwave Background (CMB) experiments will deliver extremely accurate measurements of the E-modes pattern of the CMB polarization field. Given the sharpness of the E-modes transfer functions, such surveys make for a powerful detector of high-frequency signals from primordial features that may be lurking in current data sets. With a handful of toy models that increase the fit to the latest Planck data, but are of marginal statistical significance, we use a state-of-the-art forecast pipeline to illustrate the promising prospects to test primordial features in the next decade. Not only will future experiments allow us to detect such features in data, but they will also be able to discriminate between models and narrow down the physical mechanism originating them with high statistical significance. On the other hand, if the anomalies in the currently measured CMB spectra are just statistical fluctuations, all the current feature best fit candidates will be ruled out. Either way, our results show that primordial features are a clear target of forthcoming CMB surveys beyond the detection of tensor modes.
SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller

In this paper, we propose a new task of sub-event generation for an unseen process to evaluate the understanding of the coherence of sub-event actions and objects. To solve the problem, we design SubeventWriter, a sub-event sequence generation framework with a coherence controller. Given an unseen process, the framework can iteratively construct the sub-event sequence by generating one sub-event at each iteration. We also design a very effective coherence controller to decode more coherent sub-events. As our extensive experiments and analysis indicate, SubeventWriter can generate more reliable and meaningful sub-event sequences for unseen processes.