Miracle-dz/UniFAR-FTUs huggingface dataset:
A comprehensive guide to the physics and usage of PYTHIA 8.3

This manual describes the Pythia event generator, the most recent version of an evolving physics tool used to answer fundamental questions in particle physics. The program is most often used to generate high-energy-physics collision “events”, i.e. sets of particles produced in association with the collision of two incoming high-energy particles, but has several uses beyond that. The guiding philosophy is to produce and re-produce properties of experimentally obtained collisions as accurately as possible. The program includes a wide ranges of reactions within and beyond the Standard Model, and extending to heavy ion physics. Emphasis is put on phenomena where strong interactions play a major role. The manual contains both pedagogical and practical components. All included physics models are described in enough detail to allow the user to obtain a cursory overview of used assumptions and approximations, enabling an informed evaluation of the program output. A number of the most central algorithms are described in enough detail that the main results of the program can be reproduced independently, allowing further development of existing models or the addition of new ones. Finally, a chapter dedicated fully to the user is included towards the end, providing pedagogical examples of standard use cases, and a detailed description of a number of external interfaces. The program code, the online manual, and the latest version of this print manual can be found on the Pythia web page: https://www.pythia. org/.
Wave function of the universe in the presence of trans-Planckian censorship

The wave function for a closed de Sitter universe has been computed, demanding consistency with the recently proposed Trans-Planckian Censorship Conjecture (TCC). We extend the Einstein-Hilbert action to contain a complex-valued term which provides an exponentially decaying weight for the geometries violating TCC in the Lorentzian path integral sum while working in the minisuperspace approach to quantum cosmology. This postulated modification suppresses the probability of evolution of the universe into configurations that violate TCC. We show that due to the presence of this suppression factor, the Hubble rate of the universe at the end of the inflation gets subdued and assumes a value less than what is expected classically. Moreover, the consequences of this quantum gravity motivated correction in the primordial power spectrum are discussed as well.
A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-tuning Results for Child Speech Synthesis.

Speech synthesis has come a long way as current text-to-speech (TTS) models can now generate natural human-sounding speech. However, most of the TTS research focuses on using adult speech data and there has been very limited work done on child speech synthesis. This study developed and validated a training pipeline for fine-tuning state-of-the-art (SOTA) neural TTS models using child speech datasets. This approach adopts a multi-speaker TTS retuning workflow to provide a transfer-learning pipeline. A publicly available child speech dataset was cleaned to provide a smaller subset of approximately 19 hours, which formed the basis of our fine-tuning experiments. Both subjective and objective evaluations were performed using a pretrained MOSNet for objective evaluation and a novel subjective framework for mean opinion score (MOS) evaluations. Subjective evaluations achieved the MOS of 3.95 for speech intelligibility, 3.89 for voice naturalness, and 3.96 for voice consistency. Objective evaluation using a pretrained MOSNet showed a strong correlation between real and synthetic child voices. Speaker similarity was also verified by calculating the cosine similarity between the embeddings of utterances. An automatic speech recognition (ASR) model is also used to provide a word error rate (WER) comparison between the real and synthetic child voices. The final trained TTS model was able to synthesize child-like speech from reference audio samples as short as 5 seconds.
An Adaptive Optics Census of Companions to Northern Stars Within 25 pc with Robo-AO

In order to assess the multiplicity statistics of stars across spectral types and populations in a volume-limited sample, we censused nearby stars for companions with Robo-AO. We report on observations of 1157 stars of all spectral types within 25 pc with decl. >−13° searching for tight companions. We detected 154 companion candidates with separations ranging from ∼0. ″15 to 4. ″0 and magnitude differences up to Δm i′≤7 using the robotic adaptive optics instrument Robo-AO. We confirmed physical association from Gaia EDR3 astrometry for 53 of the companion candidates, 99 remain to be confirmed, and two were ruled out as background objects. We complemented the high-resolution imaging companion search with a search for comoving objects with separations out to 10,000 au in Gaia EDR3, which resulted in an additional 147 companions registered. Of the 301 total companions reported in this study, 49 of them are new discoveries. Out of the 191 stars with significant acceleration measurements in the Hipparcos–Gaia catalog of accelerations, we detect companions around 115 of them, with the significance of the acceleration increasing as the companion separation decreases. From this survey, we report the following multiplicity fractions (compared to literature values): 40.9% ± 3.0% (44%) for FGK stars and 28.2% ± 2.3% (27%) for M stars, as well as higher-order fractions of 5.5% ± 1.1% (11%) and 3.9% ± 0.9% (5%) for FGK stars and M-type stars, respectively.
Clustering units in neural networks: upstream vs downstream information

It has been hypothesized that some form of"modular"structure in artificial neural networks should be useful for learning, compositionality, and generalization. However, defining and quantifying modularity remains an open problem. We cast the problem of detecting functional modules into the problem of detecting clusters of similar-functioning units. This begs the question of what makes two units functionally similar. For this, we consider two broad families of methods: those that define similarity based on how units respond to structured variations in inputs ("upstream"), and those based on how variations in hidden unit activations affect outputs ("downstream"). We conduct an empirical study quantifying modularity of hidden layer representations of simple feedforward, fully connected networks, across a range of hyperparameters. For each model, we quantify pairwise associations between hidden units in each layer using a variety of both upstream and downstream measures, then cluster them by maximizing their"modularity score"using established tools from network science. We find two surprising results: first, dropout dramatically increased modularity, while other forms of weight regularization had more modest effects. Second, although we observe that there is usually good agreement about clusters within both upstream methods and downstream methods, there is little agreement about the cluster assignments across these two families of methods. This has important implications for representation-learning, as it suggests that finding modular representations that reflect structure in inputs (e.g. disentanglement) may be a distinct goal from learning modular representations that reflect structure in outputs (e.g. compositionality).
On Neural Network Equivalence Checking using SMT Solvers

Two pretrained neural networks are deemed equivalent if they yield similar outputs for the same inputs. Equivalence checking of neural networks is of great importance, due to its utility in replacing learning-enabled components with equivalent ones, when there is need to fulfill additional requirements or to address security threats, as is the case for example when using knowledge distillation, adversarial training etc. SMT solvers can potentially provide solutions to the problem of neural network equivalence checking that will be sound and complete, but as it is expected any such solution is associated with significant limitations with respect to the size of neural networks to be checked. This work presents a first SMT-based encoding of the equivalence checking problem, explores its utility and limitations and proposes avenues for future research and improvements towards more scalable and practically applicable solutions. We present experimental results that shed light to the aforementioned issues, for diverse types of neural network models (classifiers and regression networks) and equivalence criteria, towards a general and application-independent equivalence checking approach.
Irreducible modules of toroidal Lie algebras arising from $\phi_\epsilon$-coordinated modules of vertex algebras

In this paper, for every ǫ ∈ Z, we introduce an extension of the 2-toroidal Lie algebra by certain derivations. Based on the φǫ-coordinated modules theory for vertex algebras, we give an explicit realization of a class of irreducible highest weight modules for this extended toroidal Lie algebra. When ǫ = 1, this affords a realization of certain irreducible modules for the toroidal extended affine Lie algebras first constructed by Billig.
Unconventional Pressure-Driven Metamagnetic Transitions in Topological van der Waals Magnets.

Activating metamagnetic transitions between ordered states in van der Waals magnets and devices bring great opportunities in spintronics. We show that external pressure, which enhances the interlayer hopping without introducing chemical disorders, triggers multiple metamagnetic transitions upon cooling in the topological van der Waals magnets Mn(Bi1-xSbx)4Te7, where the antiferromagnetic interlayer superexchange coupling competes with the ferromagnetic interlayer coupling mediated by the antisite Mn spins. The temperature-pressure phase diagrams reveal that while the ordering temperature from the paramagnetic to ordered states is almost pressure-independent, the metamagnetic transitions show nontrivial pressure and temperature dependence, even re-entrance. For these highly anisotropic magnets, we attribute the former to the ordering temperature being only weakly dependent on the intralayer parameters and the latter to the parametrically different pressure and temperature dependence of the two interlayer couplings. Our independent probing of these disparate magnetic interactions paves an avenue for efficient magnetic manipulations in van der Waals magnets.
Spin Precession in the Gravity Wave Analogue Black Hole Spacetime

It was predicted that the spin precession frequency of a stationary gyroscope shows various anomalies in the strong gravity regime if its orbit shrinks, and eventually, its precession frequency becomes arbitrarily high very close to the horizon of a rotating black hole. Considering the gravity waves of a flowing fluid with a vortex in a shallow basin, which acts as a rotating analogue black hole, one can observe the predicted strong gravity effect on the spin precession in the laboratory. Attaching a thread with the buoyant particles and anchoring it to the bottom of the fluid container with a short-length miniature chain, one can construct a simple local test gyroscope to measure the spin precession frequency in the vicinity of the gravity wave analogue black hole. The thread acts as the axis of the gyroscope. By regulating the orbital frequency of the test gyroscope, one can also measure the strong gravity Lense–Thirring effect and geodetic/de-Sitter effect with this experimental set-up as the special cases. For example, to measure the Lense–Thirring effect, the length of the miniature chain can be set to zero, so that the gyroscope becomes static. One can also measure the geodetic precession with this system by orbiting the test gyroscope in the so-called Keplerian frequency around the non-rotating analogue black hole that can be constructed by making the rotation of the fluid/vortex negligible compared to its radial velocity.
Four-hundred Very Metal-poor Stars Studied with LAMOST and Subaru. I. Survey Design, Follow-up Program, and Binary Frequency

The chemical abundances of very metal-poor stars provide important constraints on the nucleosynthesis of the first generation of stars and early chemical evolution of the Galaxy. We have obtained high-resolution spectra with the Subaru Telescope for candidates of very metal-poor stars selected with a large survey of Galactic stars carried out with LAMOST. In this series of papers, we report on the elemental abundances of about 400 very metal-poor stars and discuss the kinematics of the sample obtained by combining the radial velocities measured in this study and recent astrometry obtained with Gaia. This paper provides an overview of our survey and follow-up program, and reports radial velocities for the whole sample. We identify seven double-lined spectroscopic binaries from our high-resolution spectra, for which radial velocities of the components are reported. We discuss the frequency of such relatively short-period binaries at very low metallicity.
Delicatessen: M-Estimation in Python

M-estimation is a general statistical approach that simpliﬁes and uniﬁes estimation in a variety of settings. Here, we introduce delicatessen , a Python library that automates the tedious calculations of M-estimation. To highlight the utility of delicatessen for data analyses in life science research, we provide several illustrations: linear regression robust to outliers, estimation of a dose-response curve, and standardization of results.
Explainability in reinforcement learning: perspective and position

Artificial intelligence (AI) has been embedded into many aspects of people's daily lives and it has become normal for people to have AI make decisions for them. Reinforcement learning (RL) models increase the space of solvable problems with respect to other machine learning paradigms. Some of the most interesting applications are in situations with non-differentiable expected reward function, operating in unknown or underdefined environment, as well as for algorithmic discovery that surpasses performance of any teacher, whereby agent learns from experimental experience through simple feedback. The range of applications and their social impact is vast, just to name a few: genomics, game-playing (chess, Go, etc.), general optimization, financial investment, governmental policies, self-driving cars, recommendation systems, etc. It is therefore essential to improve the trust and transparency of RL-based systems through explanations. Most articles dealing with explainability in artificial intelligence provide methods that concern supervised learning and there are very few articles dealing with this in the area of RL. The reasons for this are the credit assignment problem, delayed rewards, and the inability to assume that data is independently and identically distributed (i.i.d.). This position paper attempts to give a systematic overview of existing methods in the explainable RL area and propose a novel unified taxonomy, building and expanding on the existing ones. The position section describes pragmatic aspects of how explainability can be observed. The gap between the parties receiving and generating the explanation is especially emphasized. To reduce the gap and achieve honesty and truthfulness of explanations, we set up three pillars: proactivity, risk attitudes, and epistemological constraints. To this end, we illustrate our proposal on simple variants of the shortest path problem.
Topological Iwasawa invariants and arithmetic statistics

Given a prime number $p$, we study topological analogues of Iwasawa invariants associated to $\mathbb{Z}_p$-covers of the $3$-sphere that are branched along a link. We prove explicit criteria to detect these Iwasawa invariants, and apply them to the study of links consisting of $2$ component knots. Fixing the prime $p$, we prove statistical results for the average behaviour of $p$-primary Iwasawa invariants for $2$-bridge links that are in Schubert normal form. Our main result, which is entirely unconditional, shows that the density of $2$-bridge links for which the $\mu$-invariant vanishes, and the $\lambda$-invariant is equal to $1$, is $(1-\frac{1}{p})$. We also conjecture that the density of $2$-bridge links for which the $\mu$-invariant vanishes is $1$, and this is significantly backed by computational evidence. Our results are proven in a topological setting, yet have arithmetic significance, as we set out new directions in arithmetic statistics and arithmetic topology.
A Method for Estimating Individual Socioeconomic Status of Twitter Users

The rise of social media has opened countless opportunities to explore social science questions with new data and methods. However, research on socioeconomic inequality remains constrained by limited individual-level socioeconomic status (SES) measures in digital trace data. Following Bourdieu, we argue that the commercial and entertainment accounts Twitter users follow reflect their economic and cultural capital. Adapting a political science method for inferring political ideology, we use correspondence analysis to estimate the SES of 3,482,652 Twitter users who follow the accounts of 339 brands in the United States. We validate our estimates with data from the Facebook Marketing application programming interface, self-reported job titles on users’ Twitter profiles, and a small survey sample. The results show reasonable correlations with the standard proxies for SES, alongside much weaker or nonsignificant correlations with other demographic variables. The proposed method opens new opportunities for innovative social research on inequality on Twitter and similar online platforms.
A Survey on Techniques for Identifying and Resolving Representation Bias in Data

The grand goal of data-driven decision-making is to help humans make decisions, not only easily and at scale but also wisely, accurately, and just. However, data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often miss representing minorities. Representation Bias in data can happen due to various reasons ranging from historical discrimination to selection and sampling biases in the data acquisition and preparation methods. One cannot expect AI-based societal solutions to have equitable outcomes without addressing the representation bias. This paper surveys the existing literature on representation bias in the data. It presents a taxonomy to categorize the studied techniques based on multiple design dimensions and provide a side-by-side comparison of their properties. There is still a long way to fully address representation bias issues in data. The authors hope that this survey motivates researchers to approach these challenges in the future by observing existing work within their respective domains.
Estimation of speaker age and height from speech signal using bi-encoder transformer mixture model

The estimation of speaker characteristics such as age and height is a challenging task, having numerous applications in voice forensic analysis. In this work, we propose a bi-encoder transformer mixture model for speaker age and height estimation. Considering the wide differences in male and female voice characteristics such as differences in formant and fundamental frequencies, we propose the use of two separate transformer encoders for the extraction of specific voice features in the male and female gender, using wav2vec 2.0 as a common-level feature extractor. This architecture reduces the interference effects during backpropagation and improves the generalizability of the model. We perform our experiments on the TIMIT dataset and significantly outperform the current state-of-the-art results on age estimation. Specifically, we achieve root mean squared error (RMSE) of 5.54 years and 6.49 years for male and female age estimation, respectively. Further experiment to evaluate the relative importance of different phonetic types for our task demonstrate that vowel sounds are the most distinguishing for age estimation.
Robust Classification Using Contractive Hamiltonian Neural ODEs

Deep neural networks can be fragile and sensitive to small input perturbations that might cause a significant change in the output. In this letter, we employ contraction theory to improve the robustness of neural ODEs (NODEs). A dynamical system is contractive if all solutions with different initial conditions converge to each other exponentially fast. As a consequence, perturbations in initial conditions become less and less relevant over time. Since in NODEs the input data corresponds to the initial condition of dynamical systems, we show contractivity can mitigate the effect of input perturbations. More precisely, inspired by NODEs with Hamiltonian dynamics, we propose a class of contractive Hamiltonian NODEs (CH-NODEs). By properly tuning a scalar parameter, CH-NODEs ensure contractivity by design and can be trained using standard backpropagation. Moreover, CH-NODEs enjoy built-in guarantees of non-exploding gradients, which ensure a well-posed training process. Finally, we demonstrate the robustness of CH-NODEs on the MNIST image classification problem with noisy test data.
A Splicing Approach to Best Subset of Groups Selection

Best subset of groups selection (BSGS) is the process of selecting a small part of nonoverlapping groups to achieve the best interpretability on the response variable. It has attracted increasing attention and has far-reaching applications in practice. However, due to the computational intractability of BSGS in high-dimensional settings, developing efficient algorithms for solving BSGS remains a research hotspot. In this paper, we propose a group-splicing algorithm that iteratively detects the relevant groups and excludes the irrelevant ones. Moreover, coupled with a novel group information criterion, we develop an adaptive algorithm to determine the optimal model size. Under certain conditions, it is certifiable that our algorithm can identify the optimal subset of groups in polynomial time with high probability. Finally, we demonstrate the efficiency and accuracy of our methods by comparing them with several state-of-the-art algorithms on both synthetic and real-world data sets. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms-Discrete. Funding: This work was supported by National Natural Science Foundation of China [Grants 72171216, 71921001, and 71991474], the Key Research and Development Program of Guangdong [Grant 2019B020228001], the Science and Technology Program of Guangzhou, China [Grant 202002030129], The Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China [Grant 22XNH161], and the Outstanding Graduate Student Innovation and Development Program of Sun Yat-Sen University [Grant 19lgyjs64]. Supplemental Material: The online appendix and video are available at https://doi.org/10.1287/ijoc.2022. 1241 .