Miracle-dz/UniFAR-FTUs huggingface dataset:
A Detailed Study of the Interstellar Protons toward the TeV γ-Ray SNR RX J0852.0–4622 (G266.2–1.2, Vela Jr.): The Third Case of the γ-Ray and ISM Spatial Correspondence

We present a new analysis of the interstellar protons toward the TeV γ-ray SNR RX J0852.0−4622 (G266.2−1.2, Vela Jr.). We used the NANTEN2 12CO(J = 1–0) and Australia Telescope Compact Array and Parkes H i data sets in order to derive the molecular and atomic gas associated with the TeV γ-ray shell of the SNR. We find that atomic gas over a velocity range from VLSR = −4 to 50 km s−1 or 60 km s−1 is associated with the entire SNR, while molecular gas is associated with a limited portion of the SNR. The large velocity dispersion of the H i is ascribed to the expanding motion of a few H i shells overlapping toward the SNR but is not due to the Galactic rotation. The total masses of the associated H i and molecular gases are estimated to be M⊙ and ∼103 M⊙, respectively. A comparison with the High Energy Stereoscopic System TeV γ-rays indicates that the interstellar protons have an average density around 100 cm−3 and shows a good spatial correspondence with the TeV γ-rays. The total cosmic-ray proton energy is estimated to be ∼1048 erg for the hadronic γ-ray production, which may still be an underestimate by a factor of a few due to a small filling factor of the SNR volume by the interstellar protons. This result presents a third case, after RX J1713.7−3946 and HESS J1731−347, of the good spatial correspondence between the TeV γ-rays and the interstellar protons, lending further support for a hadronic component in the γ-rays from young TeV γ-ray SNRs.
Quantum capacity of a deformed bosonic dephasing channel

In this paper, using the notion of nonlinear coherent states, we define a deformed bosonic dephasing channel modelling the impact of a Kerr medium on a quantum state, as it occurs, for instance, in quantum communication based on optical fibers. We show that, in certain regimes, the Kerr nonlinearity is able to compensate the dephasing. In addition, our studies reveal that the quantum capacity of the deformed bosonic dephasing channel can be greater than that of the undeformed, standard bosonic dephasing channel for certain nonlinearity parameters.
Array Configuration-Agnostic Personalized Speech Enhancement using Long-Short-Term Spatial Coherence

Personalized speech enhancement (PSE) has been a field of active research for suppression of speech-like interferers, such as competing speakers or television (TV) dialogue. Compared with single-channel approaches, multichannel PSE systems can be more effective in adverse acoustic conditions by leveraging the spatial information in microphone signals. However, the implementation of multichannel PSEs to accommodate a wide range of array topology in household applications can be challenging. To develop an array configuration-agnostic PSE system, we define a spatial feature termed the long-short-term spatial coherence (LSTSC) with a dynamic forgetting factor as the input feature to a convolutional recurrent network to monitor the spatial activity of the target speaker. As another refinement, an equivalent rectangular bandwidth-scaled LSTSC feature can be used to reduce the computational cost. Experiments were conducted to compare the proposed PSE systems, including the complete and the simplified versions with four baselines using unseen room responses and array configurations (geometry and channel count) in the presence of TV noise and competing speakers. The results demonstrated that the proposed multichannel PSE network trained with the LSTSC feature with a dynamic forgetting factor achieves superior enhancement performance without precise knowledge of the array configurations and room responses.
Annotation of Soft Onsets in String Ensemble Recordings

Onset detection is the process of identifying the start points of musical note events within an audio recording. While the detection of percussive onsets is often considered a solved problem, soft onsets-as found in string instrument recordings-still pose a significant challenge for state-of-the-art algorithms. The problem is further exacerbated by a paucity of data containing expert annotations and research related to best practices for curating soft onset annotations for string instruments. To this end, we investigate inter-annotator agreement between 24 participants, extend an algorithm for determining the most consistent annotator, and compare the performance of human annotators and state-of-the-art onset detection algorithms. Experimental results reveal a positive trend between musical experience and both inter-annotator agreement and performance in comparison with automated systems. Additionally, onsets produced by changes in fingering as well as those from the cello were found to be particularly challenging for both human annotators and automatic approaches. To promote research in best practices for annotation of soft onsets, we have made all experimental data associated with this study publicly available. In addition, we publish the ARME Virtuoso Strings dataset, consisting of over 144 recordings of professional performances of an excerpt from Haydn's string quartet Op. 74 No. 1 Finale, each with corresponding individual instrumental onset annotations.
Missing odd-order Shapiro steps do not uniquely indicate fractional Josephson effect

Topological superconductivity is expected to spur Majorana zero modes—exotic states that are also considered a quantum technology asset. Fractional Josephson eﬀect is their manifestation in electronic transport measurements, often under microwave irradiation. A fraction of induced resonances, known as Shapiro steps, should vanish, in a pattern that signiﬁes the presence of Majorana modes. Here we report patterns of Shapiro steps expected in topological Josephson junctions, such as the missing ﬁrst Shapiro step, or several missing odd-order steps. But our junctions, which are InAs quantum wells with Al contacts, are studied near zero magnetic ﬁeld, meaning that they are not in the topological regime. We also observe other patterns such as missing even steps and several missing steps in a row, not relevant to topological superconductivity. Potentially responsible for our observations is rounding of not fully developed steps superimposed on non-monotonic resistance versus voltage curves, but several origins may be at play. Our results demonstrate that any single pattern, even striking, cannot uniquely identify topological superconductivity, and a multifactor approach is necessary to unambiguously establish this important phenomenon.
Who Reviews The Reviewers? A Multi-Level Jury Problem

We consider the problem of determining a binary ground truth using advice from a group of independent reviewers (experts) who express their guess about a ground truth correctly with some independent probability (competence). In this setting, when all reviewers are competent (competence greater than one-half), the Condorcet Jury Theorem tells us that adding more reviewers increases the overall accuracy, and if all competences are known, then there exists an optimal weighting of the reviewers. However, in practical settings, reviewers may be noisy or incompetent, i.e., competence below half, and the number of experts may be small, so the asymptotic Condorcet Jury Theorem is not practically relevant. In such cases we explore appointing one or more chairs (judges) who determine the weight of each reviewer for aggregation, creating multiple levels. However, these chairs may be unable to correctly identify the competence of the reviewers they oversee, and therefore unable to compute the optimal weighting. We give conditions when a set of chairs is able to weight the reviewers optimally, and depending on the competence distribution of the agents, give results about when it is better to have more chairs or more reviewers. Through numerical simulations we show that in some cases it is better to have more chairs, but in many cases it is better to have more reviewers.
Microscopic dynamics at the Running of the Bulls (San Fermin Festival) in the context of the Social Force Model

This research explores the dynamics of the emergency evacuation during the “Running of the Bulls” festival (Spain, 2013). As people run to escape from danger, many pedestrians stumble and fall down, while others will try to pass over them. We carefully examined three speciﬁc recordings of the running, that show this kind of behavior. We developed a microscopic model mimicking the stumbling mechanism in the context of the Social Force Model (SFM). In our model, “moving” individuals can suddenly switch to a “fallen” state when they are in the vicinity of a fallen individual. We arrived to the conclusion that the presence of a fallen pedestrian increases dramatically the falling probability of the pedestrians nearby. Also, the product between the local density gradient and the velocity of each pedestrian appears as a relevant indicator for an imminent fall. We call this the pedestrian “falling susceptibility ( f s )”.
Vector-Valued Least-Squares Regression under Output Regularity Assumptions

We propose and analyse a reduced-rank method for solving least-squares regression problems with infinite dimensional output. We derive learning bounds for our method, and study under which setting statistical performance is improved in comparison to full-rank method. Our analysis extends the interest of reduced-rank regression beyond the standard low-rank setting to more general output regularity assumptions. We illustrate our theoretical insights on synthetic least-squares problems. Then, we propose a surrogate structured prediction method derived from this reduced-rank method. We assess its benefits on three different problems: image reconstruction, multi-label classification, and metabolite identification.
TransCC: Transformer-based Multiple Illuminant Color Constancy Using Multitask Learning

Multi-illuminant color constancy is a challenging problem with only a few existing methods. For example, one prior work used a small set of predeﬁned white balance settings and spatially blended among them, limiting the solu-tion to predeﬁned illuminations. Another method proposed a generative adversarial network and an angular loss, yet the performance is suboptimal due to the lack of regularization for multi-illumination colors. This paper introduces a transformer-based multi-task learning method to estimate single and multiple light colors from a single input image. To help our deep learning model have better cues of the light colors, achromatic-pixel detection, and edge detection are used as auxiliary tasks in our multi-task learning setting. By exploiting extracted content features from the input image as tokens, illuminant color correlations be-tween pixels are learned by leveraging contextual information in our transformer. Our transformer approach is further assisted via a contrastive loss deﬁned between the input, output, and ground truth. We demonstrate that our proposed model achieves 40.7% improvement compared to a state-of-the-art multi-illuminant color constancy method on a multi-illuminant dataset (LSMI). Moreover, our model maintains a robust performance on the single illuminant dataset (NUS-8) and provides 22.3% improvement on the state-of-the-art single color constancy method.
Off-Shell Strings I: S-matrix and Action

We explain why Tseytlin's off-shell formulation of string theory is well-defined. Although quantizing strings on an off-shell background requires an arbitrary choice of Weyl frame, this choice is not physically significant since it can be absorbed into a field redefinition of the target space fields. The off-shell formalism is particularly subtle at tree-level, due to the treatment of the noncompact conformal Killing group SL(2,$\mathbb{C}$) of the sphere. We prove that Tseytlin's sphere prescriptions recover the standard tree-level Lorentzian S-matrix, and show how to extract the stringy $i\varepsilon$ prescription from the UV cutoff on the worldsheet. We also demonstrate that the correct tree-level equations of motion are obtained to all orders in perturbation theory in $g_s$ and $\alpha^{\prime}$, and illuminate the close connection between the string action and the c-theorem.
Causal Bandits: Online Decision-Making in Endogenous Settings

The deployment of Multi-Armed Bandits (MAB) has become commonplace in many economic applications. However, regret guarantees for even state-of-the-art linear bandit algorithms (such as Optimism in the Face of Uncertainty Linear bandit (OFUL)) make strong exogeneity assumptions w.r. t. arm covariates. This assumption is very often violated in many economic contexts and using such algorithms can lead to sub-optimal decisions. Further, in social science analysis, it is also important to understand the asymptotic distribution of estimated parameters. To this end, in this paper, we consider the problem of online learning in linear stochastic contextual bandit problems with endogenous covariates. We propose an algorithm we term $\epsilon$-BanditIV, that uses instrumental variables to correct for this bias, and prove an $\tilde{\mathcal{O}}(k\sqrt{T})$ upper bound for the expected regret of the algorithm. Further, we demonstrate the asymptotic consistency and normality of the $\epsilon$-BanditIV estimator. We carry out extensive Monte Carlo simulations to demonstrate the performance of our algorithms compared to other methods. We show that $\epsilon$-BanditIV significantly outperforms other existing methods in endogenous settings. Finally, we use data from real-time bidding (RTB) system to demonstrate how $\epsilon$-BanditIV can be used to estimate the causal impact of advertising in such settings and compare its performance with other existing methods.
Searching for Carriers of the Diffuse Interstellar Bands Across Disciplines, using Natural Language Processing

The explosion of scientific publications overloads researchers with information. This is even more dramatic for interdisciplinary studies, where several fields need to be explored. A tool to help researchers overcome this is Natural Language Processing (NLP): a machine-learning (ML) technique that allows scientists to automatically synthesize information from many articles. As a practical example, we have used NLP to conduct an interdisciplinary search for compounds that could be carriers for Diffuse Interstellar Bands (DIBs), a long-standing open question in astrophysics. We have trained a NLP model on a corpus of 1.5 million cross-domain articles in open access, and fine-tuned this model with a corpus of astrophysical publications about DIBs. Our analysis points us toward several molecules, studied primarily in biology, having transitions at the wavelengths of several DIBs and composed of abundant interstellar atoms. Several of these molecules contain chromophores, small molecular groups responsible for the molecule's colour, could be promising candidate carriers. Identifying viable carriers demonstrates the value of using NLP to tackle open scientific questions, in an interdisciplinary manner.
A Creative Industry Image Generation Dataset Based on Captions

Most image generation methods are difficult to precisely control the properties of the generated images, such as structure, scale, shape, etc., which limits its large-scale application in creative industries such as conceptual design and graphic design, and so on. Using the prompt and the sketch is a practical solution for controllability. Existing datasets lack either prompt or sketch and are not designed for the creative industry. Here is the main contribution of our work. a) This is the first dataset that covers the 4 most important areas of creative industry domains and is labeled with prompt and sketch. b) We provide multiple reference images in the test set and fine-grained scores for each reference which are useful for measurement. c) We apply two state-of-the-art models to our dataset and then find some shortcomings, such as the prompt is more highly valued than the sketch.
Egocentric Hand-object Interaction Detection

In this paper, we propose a method to jointly determine the status of hand-object interaction. This is crucial for egocentric human activity understanding and interaction. From a computer vision perspective, we believe that determining whether a hand is interacting with an object depends on whether there is an interactive hand pose and whether the hand is touching the object. Thus, we extract the hand pose, hand-object masks to jointly determine the interaction status. In order to solve the problem of hand pose estimation due to in-hand object occlusion, we use a multi-cam system to capture hand pose data from multiple perspectives. We evaluate and compare our method with the most recent work from Shan et al. [1] on selected images from EPIC-KITCHENS [2] dataset and achieve 89% accuracy on HOI (hand-object interaction) detection which is comparative to Shan’s (92%). However, for real-time performance, our method can run over 30 FPS which is much more efficient than Shan’s ($1\sim 2$ FPS). A demo can be found from https://www.youtube. com/watch? v=XVj3zBuynmQ.
Squeeze flow of micro-droplets: convolutional neural network with trainable and tunable refinement

We propose a platform based on neural networks to solve the image-to-image translation problem in the context of squeeze flow of micro-droplets. In the first part of this paper, we present the governing partial differential equations to lay out the underlying physics of the problem. We also discuss our developed Python package, sqflow, which can potentially serve as free, flexible, and scalable standardized benchmarks in the fields of machine learning and computer vision. In the second part of this paper, we introduce a residual convolutional neural network to solve the corresponding inverse problem: to translate a high-resolution (HR) imprint image with a specific liquid film thickness to a low-resolution (LR) droplet pattern image capable of producing the given imprint image for an appropriate spread time of droplets. We propose a neural network architecture that learns to systematically tune the refinement level of its residual convolutional blocks by using the function approximators that are trained to map a given input parameter (film thickness) to an appropriate refinement level indicator. We use multiple stacks of convolutional layers the output of which is translated according to the refinement level indicators provided by the directly-connected function approximators. Together with a non-linear activation function, such a translation mechanism enables the HR imprint image to be refined sequentially in multiple steps until the target LR droplet pattern image is revealed. The proposed platform can be potentially applied to data compression and data encryption. The developed package and datasets are publicly available on GitHub at https://github.com/sqflow/sqflow.
kogito: A Commonsense Knowledge Inference Toolkit

In this paper, we present kogito, an open-source tool for generating commonsense inferences about situations described in text. kogito provides an intuitive and extensible interface to interact with natural language generation models that can be used for hypothesizing commonsense knowledge inference from a textual input. In particular, kogito offers several features for targeted, multi-granularity knowledge generation. These include a standardized API for training and evaluating knowledge models, and generating and filtering inferences from them. We also include helper functions for converting natural language texts into a format ingestible by knowledge models — intermediate pipeline stages such as knowledge head extraction from text, heuristic and model-based knowledge head-relation matching, and an ability to define and use custom knowledge relations. We make the code for kogito available at https://github.com/epfl-nlp/kogito along with thorough documentation at https://kogito.readthedocs. io.
Ranging-Based Localizability Optimization for Mobile Robotic Networks

In robotic networks relying on noisy range measurements between agents for cooperative localization, the achievable positioning accuracy strongly depends on the network geometry. This motivates the problem of planning robot trajectories in such multirobot systems in a way that maintains high localization accuracy. We present potential-based planning methods, where localizability potentials are introduced to characterize the quality of the network geometry for cooperative position estimation. These potentials are based on Cramér Rao lower bounds (CRLB) and provide a theoretical lower bound on the error covariance achievable by any unbiased position estimator. In the process, we establish connections between CRLBs and the theory of graph rigidity, which has been previously used to plan the motion of robotic networks. We develop decentralized deployment algorithms appropriate for large networks, and we use equality-constrained CRLBs to extend the concept of localizability to scenarios where additional information about the relative positions of the ranging sensors is known. We illustrate the resulting robot deployment methodology through simulated examples and an experiment.
Investigating the multifrequency pulse profiles of PSRs B0329+54 and B1642-03 in an inverse Compton scattering model

Emission geometries, including emission region heights, beam shapes and radius-to-frequency mapping, are important clues for pulsar radiation models. Multiband radio observations reveal this valuable information. In this paper, we study two bright pulsars, PSRs B0329+54 and B1642-03, and observe them at high frequencies: 2.5, 5 and 8 GHz. The newly acquired data, together with historical archives, provide an atlas of multifrequency profiles spanning from 100 MHz to 10 GHz. We study the frequency evolution of pulse profiles as well as radiation regions using these data. First, we fit the pulse profiles with Gaussian functions to determine the phase of each component, and then we calculate the radiation altitudes of different emission components and radiation regions. We find that the inverse Compton scattering (ICS) model can reproduce the radiation geometry of these two pulsars. However, for PSR B0329+54, the radiation can be generated in either an annular gap (AG) or a core gap (CG), while the radiation of PSR B1642-03 can only be generated in a CG. This difference is caused by the inclination angle and the impact angle of these two pulsars. The relationship between the beaming angle (the angle between the radiation direction and the magnetic axis) and the radiation altitude versus frequency is also presented by modelling the beam-frequency evolution in the ICS model. The multiband pulse profiles of these two pulsars can be described well by the ICS model combined with the CG and AG.
Gravitational energy in the framework of embedding and splitting theories

We study various definitions of the gravitational field energy based on the usage of isometric embeddings in the Regge–Teitelboim approach. For the embedding theory, we consider the coordinate translations on the surface as well as the coordinate translations in the flat bulk. In the latter case, the independent definition of gravitational energy–momentum tensor appears as a Noether current corresponding to global inner symmetry. In the field-theoretic form of this approach (splitting theory), we consider Noether procedure and the alternative method of energy–momentum tensor defining by varying the action of the theory with respect to flat bulk metric. As a result, we obtain energy definition in field-theoretic form of embedding theory which, among the other features, gives a nontrivial result for the solutions of embedding theory which are also solutions of Einstein equations. The question of energy localization is also discussed.