Miracle-dz/UniFAR-FTUs huggingface dataset:
An uncontaminated measurement of the escaping Lyman continuum at z ∼ 3

Observations of reionization-era analogues at z ∼ 3 are a powerful tool for constraining reionization. Rest-ultraviolet observations are particularly useful, in which both direct and indirect tracers of ionizing-photon production and escape can be observed. We analyse a sample of 124 z ∼ 3 galaxies from the Keck Lyman Continuum Spectroscopic Survey, with sensitive spectroscopic measurements of the Lyman continuum region. We present a method of removing foreground contamination from our sample using high-resolution, multiband Hubble Space Telescope imaging. We re-measure the global properties of the cleaned sample of 13 individually detected Lyman continuum sources and 107 individually undetected sources, including a sample-averaged absolute escape fraction of fesc, abs = 0.06 ± 0.01 and a sample-averaged ratio of ionizing to non-ionizing ultraviolet flux density of 〈f900/f1500〉out = 0.040 ± 0.006, corrected for attenuation from the intergalactic and circumgalactic media. Based on composite spectra, we also recover a strong positive correlation between 〈f900/f1500〉out and Lyα equivalent width (Wλ(Ly$\rm \alpha$)) and a negative correlation between 〈f900/f1500〉out and UV luminosity. As in previous work, we interpret the relationship between 〈f900/f1500〉out and Wλ(Ly$\rm \alpha$) in terms of the modulation of the escape of ionizing radiation from star-forming galaxies based on the covering fraction of neutral gas. We also use a Wλ(Ly$\rm \alpha$)-weighted 〈f900/f1500〉out to estimate an ionizing emissivity from star-forming galaxies at z ∼ 3 as ϵLyC ≃ 5.5 × 1024 erg s−1 Hz−1 Mpc−3. This estimate, evaluated using the uncontaminated sample of this work, affirms that the contribution of galaxies to the ionizing background at z ∼ 3 is comparable to that of active galactic nuclei.
Review of white box methods for explanations of convolutional neural networks in image classification tasks

Abstract. In recent years, deep learning has become prevalent to solve applications from multiple domains. Convolutional neural networks (CNNs) particularly have demonstrated state-of-the-art performance for the task of image classification. However, the decisions made by these networks are not transparent and cannot be directly interpreted by a human. Several approaches have been proposed to explain the reasoning behind a prediction made by a network. We propose a topology of grouping these methods based on their assumptions and implementations. We focus primarily on white box methods that leverage the information of the internal architecture of a network to explain its decision. Given the task of image classification and a trained CNN, our work aims to provide a comprehensive and detailed overview of a set of methods that can be used to create explanation maps for a particular image, which assign an importance score to each pixel of the image based on its contribution to the decision of the network. We also propose a further classification of the white box methods based on their implementations to enable better comparisons and help researchers find methods best suited for different scenarios.
LT-LM: a novel non-autoregressive language model for single-shot lattice rescoring

Neural network-based language models are commonly used in rescoring approaches to improve the quality of modern automatic speech recognition (ASR) systems. Most of the existing methods are computationally expensive since they use autoregressive language models. We propose a novel rescoring approach, which processes the entire lattice in a single call to the model. The key feature of our rescoring policy is a novel non-autoregressive Lattice Transformer Language Model (LT-LM). This model takes the whole lattice as an input and predicts a new language score for each arc. Additionally, we propose the artificial lattices generation approach to incorporate a large amount of text data in the LT-LM training process. Our single-shot rescoring performs orders of magnitude faster than other rescoring methods in our experiments. It is more than 300 times faster than pruned RNNLM lattice rescoring and N-best rescoring while slightly inferior in terms of WER.
The impact of self-interacting dark matter on the intrinsic alignments of galaxies

The formation and evolution of galaxies is known to be sensitive to tidal processes leading to intrinsic correlations between their shapes and orientations. Such correlations can be measured to high significance today, suggesting that cosmological information can be extracted from them. Among the most pressing questions in particle physics and cosmology is the nature of dark matter. If dark matter is self-interacting, it can leave an imprint on galaxy shapes. In this work, we investigate whether selfinteractions can produce a long-lasting imprint on intrinsic galaxy shape correlations. We investigate this observable at low redshift (z < 0.4) using a state-of-the-art suite of cosmological hydro-dynamical simulations where the dark matter model is varied. We find that dark matter self-interactions induce a mass dependent suppression in the intrinsic alignment signal by up to 50% out to ten’s of mega-parsecs, showing that selfinteractions can impact structure outside the very core of clusters. We find evidence that self-interactions have a scale-dependent impact on the intrinsic alignment signal that is sufficiently different from signatures introduced by differing baryonic physics prescriptions, suggesting that it is detectable with up-coming all-sky surveys.
Normal stability of slow manifolds in nearly periodic Hamiltonian systems

M. Kruskal showed that each nearly-periodic dynamical system admits a formal $U(1)$ symmetry, generated by the so-called roto-rate. We prove that such systems also admit nearly-invariant manifolds of each order, near which rapid oscillations are suppressed. We study the nonlinear normal stability of these slow manifolds for nearly-periodic Hamiltonian systems on barely symplectic manifolds -- manifolds equipped with closed, non-degenerate $2$-forms that may be degenerate to leading order. In particular, we establish a sufficient condition for long-term normal stability based on second derivatives of the well-known adiabatic invariant. We use these results to investigate the problem of embedding guiding center dynamics of a magnetized charged particle as a slow manifold in a nearly-periodic system. We prove that one previous embedding, and two new embeddings enjoy long-term normal stability, and thereby strengthen the theoretical justification for these models.
Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?

With the consolidation of deep learning in drug discovery, several novel algorithms for learning molecular representations have been proposed. Despite the interest of the community in developing new methods for learning molecular embeddings and their theoretical benefits, comparing molecular embeddings with each other and with traditional representations is not straightforward, which in turn hinders the process of choosing a suitable representation for Quantitative Structure-Activity Relationship (QSAR) modeling. A reason behind this issue is the difficulty of conducting a fair and thorough comparison of the different existing embedding approaches, which requires numerous experiments on various datasets and training scenarios. To close this gap, we reviewed the literature on methods for molecular embeddings and reproduced three unsupervised and two supervised molecular embedding techniques recently proposed in the literature. We compared these five methods concerning their performance in QSAR scenarios using different classification and regression datasets. We also compared these representations to traditional molecular representations, namely molecular descriptors and fingerprints. As opposed to the expected outcome, our experimental setup consisting of over $25 000$ trained models and statistical tests revealed that the predictive performance using molecular embeddings did not significantly surpass that of traditional representations. Although supervised embeddings yielded competitive results compared with those using traditional molecular representations, unsupervised embeddings tended to perform worse than traditional representations. Our results highlight the need for conducting a careful comparison and analysis of the different embedding techniques prior to using them in drug design tasks and motivate a discussion about the potential of molecular embeddings in computer-aided drug design.
Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion

How to leverage dynamic contextual information in end-to-end speech recognition has remained an active research area. Previous solutions to this problem were either designed for specialized use cases that did not generalize well to open-domain scenarios, did not scale to large biasing lists, or underperformed on rare long-tail words. We address these limitations by proposing a novel solution that combines shallow fusion, trie-based deep biasing, and neural network language model contextualization. These techniques result in significant 19.5% relative Word Error Rate improvement over existing contextual biasing approaches and 5.4%-9.3% improvement compared to a strong hybrid baseline on both open-domain and constrained contextualization tasks, where the targets consist of mostly rare long-tail words. Our final system remains lightweight and modular, allowing for quick modification without model re-training.
A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE)

Context. The evolution of galaxies depends on their interaction with the surrounding environment. Ultra-diffuse galaxies (UDGs) have been found in large numbers in clusters. We detected a few star-forming blobs in the VESTIGE survey, located at ∼5 kpc from a UDG, namely NGVS 3543, in association with an HI gas cloud AGC 226178, suggesting a recent interaction between this low-surface-brightness system and the surrounding cluster environment. Aims. We use a complete set of multi-frequency data including deep optical, UV, and narrow-band Hα imaging and HI data to understand the formation process that gave birth to this peculiar system. Methods. For this purpose, we measured (i) the multi-wavelength radial surface brightness profiles of NGVS 3543 and compared them to the predictions of spectro-photometric models of galaxy evolution in rich clusters; and (ii) the aperture photometry of the blue regions in the vicinity of NGVS 3543 in order to determine their age and stellar mass. Results. Comparisons of the observations with evolutionary models indicate that NGVS 3543 has undergone a ram-pressure stripping that peaked ∼100 Myr ago, transforming a blue gas-rich UDG into a red gas-poor UDG. Star formation has taken place in the ram pressure stripped gas, the mass of which is ∼108 M⊙, forming star complexes with a typical age of ∼20 Myr and a stellar mass of ∼104 M⊙. Conclusions. These results suggest that we are observing for the first time the ongoing transformation of a gas-rich UDG into a red and quiescent UDG under the effect of a ram-pressure stripping event. The same process could explain the lack of star-forming UDGs in rich environments observed in several nearby clusters.
Hey Robot, Which Way Are You Going? Nonverbal Motion Legibility Cues for Human-Robot Spatial Interaction

Mobile robots have recently been deployed in public spaces such as shopping malls, airports, and urban sidewalks. Most of these robots are designed with human-aware motion planning capabilities but are not designed to communicate with pedestrians. Pedestrians that encounter these robots without prior understanding of the robots’ behaviour can experience discomfort, confusion, and delayed social acceptance. In this work we designed and evaluated nonverbal robot motion legibility cues, which communicate a mobile robot's motion intention to pedestrians. We compared a motion legibility cue using Projected Arrows to one using Flashing Lights. We designed the cues to communicate path information, goal information, or both, and explored different Robot Movement Scenarios. We conducted an online user study with 229 participants using videos of the motion legibility cues. Our results show that the absence of cues was not socially acceptable, and that Projected Arrows were the more socially acceptable cue in most experimental conditions. We conclude that the presence and choice of motion legibility cues can positively influence robots’ acceptance and successful deployment in public spaces.
Neurons learn slower than they think

Recent studies revealed complex convergence dynamics in gradient-based methods, which has been little understood so far. Changing the step size to balance between high convergence rate and small generalization error may not be sufficient: maximizing the test accuracy usually requires a larger learning rate than minimizing the training loss. To explore the dynamic bounds of convergence rate, this study introduces \textit{differential capability} into an optimization process, which measures whether the test accuracy increases as fast as a model approaches the decision boundary in a classification problem. The convergence analysis showed that: 1) a higher convergence rate leads to slower capability growth; 2) a lower convergence rate results in faster capability growth and decay; 3) regulating a convergence rate in either direction reduces differential capability.
Noncollinear ferrielectricity and morphotropic phase boundary in monolayer GeS

Two-dimensional polarity is intriguing but remains in the early stage. Here a structural evolution diagram is established for GeS monolayer, which leads a noncollinear ferrielectric $\delta$-phase energetically as stable as the ferroelectric $\alpha$-phase. Its ferrielectricity is induced by the phonon frustration, i.e., the competition between ferroelectric and antiferroelectric modes, providing more routes to tune its polarity. Besides its prominent properties like large band gap, large polarization, and high Curie temperature, more interestingly, the morphotropic phase boundary between $\alpha$- and $\delta$-phases is highly possible, which is crucial to obtain giant piezoelectricity for lead-free applications.
Transport in two dimensional electronic micro-emulsions

In two dimensional electron systems with Coulomb or dipolar interactions, a direct transition, whether first or second order, from a liquid to a crystalline state is forbidden. As a result, between these phases there must be other (microemulsion) phases which can be viewed as a meso-scale mixture of the liquid and crystalline phases. We investigate the transport properties of these new electronic phases and present arguments that they are responsible for the various transport anomalies that have been seen in experiments on the strongly correlated 2DEG in high mobility semiconductor devices with low electron densities.
Self Calibration of Scalar and Vector Sensor Arrays

In this work, we consider the problem of joint calibration and direction-of-arrival (DOA) estimation using sensor arrays. This joint estimation problem is referred to as self calibration. Unlike many previous iterative approaches, we propose geometry independent convex optimization algorithms for jointly estimating the sensor gain and phase errors as well as the source DOAs. We derive these algorithms based on both the conventional element-space data model and the covariance data model. We focus on sparse and regular arrays formed using scalar sensors as well as vector sensors. The developed algorithms are obtained by transforming the underlying bilinear calibration model into a linear model, and subsequently by using standard convex relaxation techniques to estimate the unknown parameters. Prior to the algorithm discussion, we also derive identifiability conditions for the existence of a unique solution to the self calibration problem. To demonstrate the effectiveness of the developed techniques, numerical experiments and comparisons to the state-of-the-art methods are provided. Finally, the results from an experiment that was performed in an anechoic chamber using an acoustic vector sensor array are presented to demonstrate the usefulness of the proposed self calibration techniques.
An orthogonal equivalence theorem for third order tensors

In 2011, Kilmer and Martin proposed tensor singular value decomposition (T-SVD) for third order tensors. Since then, T-SVD has applications in low rank tensor approximation, tensor recovery, multi-view clustering, multi-view feature extraction, tensor sketching, etc. By going through the Discrete Fourier Transform (DFT), matrix SVD and inverse DFT, a third order tensor is mapped to an f-diagonal third order tensor. We call this a Kilmer-Martin mapping. We show that the Kilmer-Martin mapping of a third order tensor is invariant if that third order tensor is taking T-product with some orthogonal tensors. We define singular values and T-rank of that third order tensor based upon its Kilmer-Martin mapping. Thus, tensor tubal rank, T-rank, singular values and T-singular values of a third order tensor are invariant when it is taking T-product with some orthogonal tensors. Some properties of singular values, T-rank and best T-rank one approximation are discussed.
Activating hidden teleportation power: Theory and experiment

Ideal quantum teleportation transfers an unknown quantum state intact from one party Alice to the other Bob via the use of a maximally entangled state and the communication of classical information. If Alice and Bob do not share entanglement, the maximal average fidelity between the state to be teleported and the state received, according to a classical measure-and-prepare scheme, is upper bounded by a function $f_{\mathrm{c}}$ that is inversely proportional to the Hilbert space dimension. In fact, even if they share entanglement, the so-called teleportation fidelity may still be less than the classical threshold $f_{\mathrm{c}}$. For two-qubit entangled states, conditioned on a successful local filtering, the teleportation fidelity can always be activated, i.e., boosted beyond $f_{\mathrm{c}}$. Here, for all dimensions larger than two, we show that the teleportation power hidden in a subset of entangled two-qudit Werner states can also be activated. In addition, we show that an entire family of two-qudit rank-deficient states violates the reduction criterion of separability, and thus their teleportation power is either above the classical threshold or can be activated. Using hybrid entanglement prepared in photon pairs, we also provide the first proof-of-principle experimental demonstration of the activation of teleportation power hidden in this latter family of qubit states. The connection between the possibility of activating hidden teleportation power with the closely-related problem of entanglement distillation is discussed.
Development of Polymeric Membrane Sensor for the Determination of Histamine: Experimental and Theoretical Study

Abstract Some original histamine membrane sensors were prepared by incorporating thiopyrilium (TP) derivative as an excellent charged carrier. Among these sensors, TP2 shows the best response to histamine in a wide concentration range from 2.5×10−6 to 1.0×10−1 mol L−1 with a slope of 55.6±0.7 mV decade−1 and with a detection limit of 1.0 µmol L−1 (∼0.1 ppm). The interference between histamine and several amino acids and common inorganic anions was negligible as shown by the potentiometric selectivity coefficient data. The prepared electrode was used for the determination of histamine in a human serum synthetic sample. Theoretical calculations were carried out to find the best interactions between histamine and different ligands.
The impact of uninformative parafoveal masks on L1 and late L2 speakers

Much reading research has found that informative parafoveal masks lead to a reading benefit for native speakers (see 1). However, little reading research has tested the impact of uninformative parafoveal masks during reading. Additionally, parafoveal processing research is primarily restricted to native speakers. In the current study we manipulated the type of uninformative preview using a gaze contingent boundary paradigm with a group of L1 English speakers and a group of late L2 English speakers (L1 German). We were interested in how different types of uninformative masks impact on parafoveal processing, whether L1 and L2 speakers are similarly impacted, and whether they are sensitive to parafoveally viewed language-specific sub-lexical orthographic information. We manipulated six types of uninformative masks to test these objectives: an Identical, English pseudo-word, German pseudo-word, illegal string of letters, series of X’s, and a blank mask. We found that X masks affect reading the most with slight graded differences across the other masks, L1 and L2 speakers are impacted similarly, and neither group is sensitive to sub-lexical orthographic information. Overall these data show that not all previews are equal, and research should be aware of the way uninformative masks affect reading behavior. Additionally, we hope that future research starts to approach models of eye-movement behavior during reading from not only a monolingual but also from a multilingual perspective.
Concise and Efficient Synthesis of Highly Potent and Selective Dipeptidyl Peptidase II Inhibitors

Abstract Highly potent and selective DPP II inhibitors N′‐(4‐Chlorobenzyl)‐N′‐methyl‐4‐oxo‐4‐(1‐piperidinyl)‐1,3‐(S)‐butane‐diamine dihydrochloride 1 and N′‐(4‐chlorobenzyl)‐4‐oxo‐4‐(1‐piperidinyl)‐1,3‐(S)‐butanediamine dihydrochloride 2 have been efficiently synthesized starting from L‐glutamine. A short and high yielding route with simple isolation techniques has been disclosed.
Complete mitochondrial genome of the copepod Sinergasilus undulates (Copepoda: Poecilostomatoida)

Abstract The total mitochondrial genome size of Sinergasilus undulatus is 14,239 bp in length, including 13 protein-coding genes (PCGs), two rRNA genes, 22 transfer RNA genes, and a non-coding control region (D-loop). The overall nucleotide composition of the mitochondrial DNA of S. undulatus is 34.9% A, 35.5% T, 15.7% C, 13.9% G, and 70.4% AT, respectively. Phylogenetic analysis suggests that the genus Sinergasilus is monophyletic, and S. undulatus is closely related to S. polycolpus. The complete mitochondrial genome of S. undulatus would be useful for species identification, epidemiology, and phylogenetics among Copepods.