Miracle-dz/UniFAR-FTUs huggingface dataset:
C-12/C-13 isotope ratio across the Galaxy from observations of C-13/O-18 in molecular clouds

The carbon isotopic ratio across the Galaxy was studied by observing nine interstellar clouds in the doubly rare isotope of carbon monoxide, C-13/O-18, and in the rare isotopic species C-12/O-18. The goal in this study was to improve the data from optically thin carbon monoxide emissions. A systematic gradient is found in the C-12/C-13 isotopic ratio across the Galaxy, ranging from about 30 in the inner part at 5 kpc to about 70 at 12 kpc, with a Galactic center value of 24. Near the solar radius the average ratio is 57, but it is somewhat larger, about 67, in Orion. Carbon monoxide isotopic ratios are smaller than those of formaldehyde, but they have roughly the same slope across the Galaxy. The result of 57 for the local CO ratio lies between the values from various CH(+) absorption measurements. 48 refs.
Witten-Reshetikhin-Turaev invariants and indefinite false theta functions for plumbing indefinite H-graphs

Gukov--Pei--Putrov--Vafa conjectured the existence of $ q $-series whose radial limits are Witten--Reshetikhin--Turaev invariants and called them homological blocks. For weakly negative definite plumbed 3-manifolds, Gukov--Pei--Putrov--Vafa and Gukov-Manolescu constructed homological blocks. In this paper, we construct indefinite false theta functions which are candidates of homological blocks for some plumbed $ 3 $-manifolds which are not weakly negative definite. Moreover we prove that, for the Poincar\'{e} homology sphere, our indefinite false theta function coincides with the original homological block.
On the Blind Spots of Model-Based Evaluation Metrics for Text Generation

In this work, we explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics: stress tests with synthetic data. Basically, we design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. We examine a range of recently proposed evaluation metrics based on pretrained language models, for the tasks of open-ended generation, translation, and summarization. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics. For example, we find that BERTScore is confused by truncation errors in summarization, and MAUVE (built on top of GPT-2) is insensitive to errors at the beginning or middle of generations. Further, we investigate the reasons behind these blind spots and suggest practical workarounds for a more reliable evaluation of text generation. We have released our code and data at https://github.com/cloudygoose/blindspot_nlg.
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation

Recently, extensive studies on photonic reinforcement learning to accelerate the process of calculation by exploiting the physical nature of light have been conducted. Previous studies utilized quantum interference of photons to achieve collective decision-making without choice conflicts when solving the competitive multi-armed bandit problem, a fundamental example of reinforcement learning. However, the bandit problem deals with a static environment where the agent's action does not influence the reward probabilities. This study aims to extend the conventional approach to a more general multi-agent reinforcement learning targeting the grid world problem. Unlike the conventional approach, the proposed scheme deals with a dynamic environment where the reward changes because of agents' actions. A successful photonic reinforcement learning scheme requires both a photonic system that contributes to the quality of learning and a suitable algorithm. This study proposes a novel learning algorithm, discontinuous bandit Q-learning, in view of a potential photonic implementation. Here, state-action pairs in the environment are regarded as slot machines in the context of the bandit problem and an updated amount of Q-value is regarded as the reward of the bandit problem. We perform numerical simulations to validate the effectiveness of the bandit algorithm. In addition, we propose a multi-agent architecture in which agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state-action pair selections among agents. We demonstrate that multi-agent reinforcement learning can be accelerated owing to conflict avoidance among multiple agents.
Entanglement phase diagrams from partial transpose moments

We present experimentally and numerically accessible quantities that can be used to differentiate among various families of random entangled states. To this end, we analyze the entanglement properties of bipartite reduced states of a tripartite pure state. We introduce a ratio of simple polynomials of low-order moments of the partially transposed reduced density matrix and show that this ratio takes well-defined values in the thermodynamic limit for various families of entangled states. This allows to sharply distinguish entanglement phases, in a way that can be understood from a quantum information perspective based on the spectrum of the partially transposed density matrix. We analyze in particular the entanglement phase diagram of Haar random states, states resulting form the evolution of chaotic Hamiltonians, stabilizer states, which are outputs of Clifford circuits, Matrix Product States, and fermionic Gaussian states. We show that for Haar random states the resulting phase diagram resembles the one obtained via the negativity and that for all the cases mentioned above a very distinctive behaviour is observed. Our results can be used to experimentally test necessary conditions for different types of mixed-state randomness, in quantum states formed in quantum computers and programmable quantum simulators.
Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

Background Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. Objective We aimed to address these 3 challenges to inform future work in stratified analyses. Methods Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. Results We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression. Conclusions This work contributes to our understanding of how these mobile health–derived features are associated with depression symptom severity to inform future work in stratified analyses.
PHANGS–JWST First Results: A Global and Moderately Resolved View of Mid-infrared and CO Line Emission from Galaxies at the Start of the JWST Era

We explore the relationship between mid-infrared (mid-IR) and CO rotational line emission from massive star-forming galaxies, which is one of the tightest scalings in the local universe. We assemble a large set of unresolved and moderately (∼1 kpc) spatially resolved measurements of CO (1–0) and CO (2–1) intensity, I CO, and mid-IR intensity, I MIR, at 8, 12, 22, and 24 μm. The I CO versus I MIR relationship is reasonably described by a power law with slopes 0.7–1.2 and normalization I CO ∼ 1 K km s−1 at I MIR ∼ 1 MJy sr−1. Both the slopes and intercepts vary systematically with choice of line and band. The comparison between the relations measured for CO (1–0) and CO (2–1) allow us to infer that R21∝IMIR0.2 , in good agreement with other work. The 8 μm and 12 μm bands, with strong polycyclic aromatic hydrocarbon (PAH) features, show steeper CO versus mid-IR slopes than the 22 and 24 μm, consistent with PAH emission arising not just from CO-bright gas but also from atomic or CO-dark gas. The CO-to-mid-IR ratio correlates with global galaxy stellar mass (M ⋆) and anticorrelates with star formation rate/M ⋆. At ∼1 kpc resolution, the first four PHANGS–JWST targets show CO-to-mid-IR relationships that are quantitatively similar to our larger literature sample, including showing the steep CO-to-mid-IR slopes for the JWST PAH-tracing bands, although we caution that these initial data have a small sample size and span a limited range of intensities.
Plug & play directed evolution of proteins with gradient-based discrete MCMC

A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast Markov chain Monte Carlo sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650 M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.
Improved performance of 1.3μm multilayer InAs quantum-dot lasers using a high-growth-temperature GaAs spacer layer

The use of a high-growth-temperature GaAs spacer layer is demonstrated to significantly improve the performance of 1. 3μm multilayer self-assembled InAs∕InGaAs dot-in-a-well lasers. The high-growth-temperature spacer layer inhibits threading dislocation formation, resulting in enhanced electrical and optical characteristics. Incorporation of these spacer layers allows the fabrication of multilayer quantum-dot devices emitting above 1. 3μm, with extremely low room-temperature threshold current densities and with operation up to 105°C.
AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection

Supervised machine learning approaches often rely on a "ground truth" label. However, obtaining one label through majority voting ignores the important subjectivity information in tasks such hate speech detection. Existing neural network models principally regard labels as categorical variables, while ignoring the semantic information in diverse label texts. In this paper, we propose AnnoBERT, a first-of-its-kind architecture integrating annotator characteristics and label text with a transformer-based model to detect hate speech, with unique representations based on each annotator's characteristics via Collaborative Topic Regression (CTR) and integrate label text to enrich textual representations. During training, the model associates annotators with their label choices given a piece of text; during evaluation, when label information is not available, the model predicts the aggregated label given by the participating annotators by utilising the learnt association. The proposed approach displayed an advantage in detecting hate speech, especially in the minority class and edge cases with annotator disagreement. Improvement in the overall performance is the largest when the dataset is more label-imbalanced, suggesting its practical value in identifying real-world hate speech, as the volume of hate speech in-the-wild is extremely small on social media, when compared with normal (non-hate) speech. Through ablation studies, we show the relative contributions of annotator embeddings and label text to the model performance, and tested a range of alternative annotator embeddings and label text combinations.
Recognition and reconstruction of cell differentiation patterns with deep learning

Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with spatial summary statistics and deep learning algorithms to recognize and reconstruct cell fate patterns in organoids of mouse embryonic stem cells. Application of Moran’s index and pair correlation functions for in vitro and synthetic data from the model showed local clustering and radial segregation. To assess the patterns as a whole, a graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70% accuracy of cell fate imputation based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms.
High Impedance Fault Detection Through Quasi-Static State Estimation: A Parameter Error Modeling Approach

This paper presents a model for detecting high-impedance faults (HIFs) using parameter error modeling and a two-step per-phase weighted least squares state estimation (SE) process. The proposed scheme leverages the use of phasor measurement units and synthetic measurements to identify per-phase power flow and injection measurements which indicate a parameter error through $\chi^{2}$ Hypothesis Testing applied to the composed measurement error (CME). Although current and voltage waveforms are commonly analyzed for high-impedance fault detection, wide-area power flow and injection measurements, which are already inherent to the SE process, also show promise for real-world high-impedance fault detection applications. The error distributions after detection share the measurement function error spread observed in proven parameter error diagnostics and can be applied to HIF identification. Further, this error spread related to the HIF will be clearly discerned from measurement error. Case studies are performed on the 33-Bus Distribution System in Simulink.
On the boundary conformal field theory approach to symmetry-resolved entanglement

We study the symmetry resolution of the entanglement entropy of an interval in two-dimensional conformal field theories (CFTs), by relating the bipartition to the geometry of an annulus with conformal boundary conditions. In the presence of extended symmetries such as Kac-Moody type current algebrae, symmetry resolution is possible only if the boundary conditions on the annulus preserve part of the symmetry group, i.e. if the factorization map associated with the spatial bipartition is compatible with the symmetry in question. The partition function of the boundary CFT (BCFT) is then decomposed in terms of the characters of the irreducible representations of the symmetry group preserved by the boundary conditions. We demonstrate that this decomposition already provides the symmetry resolution of the entanglement spectrum of the corresponding bipartition. Considering the various terms of the partition function associated with the same representation, or charge sector, the symmetry-resolved Rényi entropies can be derived to all orders in the UV cutoff expansion without the need to compute the charged moments. We apply this idea to the theory of a free massless boson with U(1)U(1), \mathbb{R}ℝ and \mathbb{Z}_2ℤ2 symmetry.
Discrete fragmentation equations with time-dependent coefficients

We examine an inﬁnite, linear system of ordinary diﬀerential equations that models the evolution of fragmenting clusters, where each cluster is assumed to be composed of identical units. In contrast to previous investigations into such discrete-size fragmentation models, we allow the fragmentation coeﬃcients to vary with time. By formulating the initial-value problem for the system as a non-autonomous abstract Cauchy problem, posed in an appropriately weighted ℓ 1 space, and then applying results from the theory of evolution families, we prove the existence and uniqueness of physically relevant, classical solutions for suitably constrained coef-ﬁcients.
Improving estimation efficiency for two-phase, outcome-dependent sampling studies

Two-phase outcome dependent sampling (ODS) is widely used in many fields, especially when certain covariates are expensive and/or difficult to measure. For two-phase ODS, the conditional maximum likelihood (CML) method is very attractive because it can handle zero Phase 2 selection probabilities and avoids modeling the covariate distribution. However, most existing CML-based methods use only the Phase 2 sample and thus may be less efficient than other methods. We propose a general empirical likelihood method that uses CML augmented with additional information in the whole Phase 1 sample to improve estimation efficiency. The proposed method maintains the ability to handle zero selection probabilities and avoids modeling the covariate distribution, but can lead to substantial efficiency gains over CML in the inexpensive covariates, or in the influential covariate when a surrogate is available, because of an effective use of the Phase 1 data. Simulations and a real data illustration using NHANES data are presented.
Index-Based Concatenated Codes for the Multi-Draw DNA Storage Channel

We consider error-correcting coding for DNA-based storage. We model the DNA storage channel as a multi-draw IDS channel where the input data is chunked into M short DNA strands, which are copied a random number of times, and the channel outputs a random selection of N noisy DNA strands. The retrieved DNA strands are prone to insertion, deletion, and substitution (IDS) errors. We propose an index-based concatenated coding scheme consisting of the concatenation of an outer code, an index code, and an inner synchronization code, where the latter two tackle IDS errors. We further propose a mismatched joint index-synchronization code maximum a posteriori probability decoder with optional clustering to infer symbolwise a posteriori probabilities for the outer decoder. We compute achievable information rates for the outer code and present Monte-Carlo simulations for information-outage probabilities and frame error rates on synthetic and experimental data, respectively.
Transverse Doppler effect and parameter estimation of LISA three-body systems

Some binary black hole systems potentially observable in LISA could be in orbit around a supermassive black hole (SMBH). The imprint of relativistic three-body effects on the waveform of the binary can be used to estimate all the parameters of the triple system, in particular the mass of the SMBH. We determine the phase shift in the waveform due to the Doppler effect of the SMBH up to second order in velocity, which breaks a well-known exact degeneracy of the lowest-order Doppler effect between the mass of the SMBH and its inclination. We perform several parameter estimations for LISA signals including this additional dephasing in the wave, showing that one can determine accurately all parameters of the three-body system. Our results indicate that one can measure the mass of a $10^8\,$M$_{\odot}$ SMBH with an accuracy better than $\sim 30\%$ (resp. $\sim 15\%$) by monitoring the waveform of a binary system whose period around the SMBH is less 100 yr (resp. 20 yr).
Main Sequence Star Super-flare Frequency based on Entire Kepler Data

We wrote and used an automated flare detection Python script to search for super-flares on main sequence stars of types A, F, G, K and M in Kepler’s long-cadence data from Q0 to Q17. We studied the statistical properties of the occurrence rate of super-flares. For the G-type data set, we compared our results with the previous results of Okamoto et al. by splitting the data set into four rotational bands. We found similar power-law indices for the flare frequency distribution. Hence, we show that inclusion of a high-pass filter, sample biases, gyrochronology and completeness of flare detection is of no significance, as our results are similar to those of Okamoto et al. We estimated that a super-flare on G-type dwarfs with energy of 1035 erg occurs on a star once every 4360 yr. We found 4637 super-flares on 1896 G-type dwarfs. Moreover, we identified 321, 1125, 4538 and 5445 super-flares on 136, 522, 770 and 312 dwarfs of types A, F, K and M, respectively. We ascertained that the occurrence rate (dN/dE) of super-flares versus flare energy, E, shows a power-law distribution with dN/dE∝E−α , where α ≃ 2.0 to 2.1 for the spectral types from F-type to M-type stars. In contrast, the obtained α ≃ 1.3 for A-type stars suggests that the flare conditions differ from those of the other spectral-type stars. We note an increase in flare incidence rate in F-type to M-type stars and a decrease in A-type to F-type stars.