Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement of the Branching Fraction of the Singly Cabibbo-Suppressed Decay $\Lambda_{c}^{+}\to\Lambda K^{+}$

We report a branching fraction measurement of the singly Cabibbo-suppressed decay $\Lambda_{c}^{+}\to\Lambda K^{+}$ using a data sample collected with the BESIII detector at the BEPCII storage ring. The data span center-of-mass energies from 4.599 to 4.950 GeV and correspond to an integrated luminosity of 6.44 fb$^{-1}$. The branching fraction of $\Lambda_{c}^{+}\to\Lambda K^{+}$ relative to that of the Cabibbo-favored decay $\Lambda_{c}^{+}\to\Lambda \pi^{+}$ is measured to be $\mathcal{R}=\frac{\mathcal{B}(\Lambda_{c}^{+}\to\Lambda K^{+})}{\mathcal{B}(\Lambda_{c}^{+}\to\Lambda \pi^{+})}=(4.78\pm0.34\pm0.20)\%$. Combining with the world-average value of $\mathcal{B}(\Lambda_{c}^{+}\to\Lambda \pi^{+})$, we obtain $\mathcal{B}(\Lambda_c^+\to\Lambda K^+)=(6.21\pm0.44\pm0.26\pm0.34)\times 10^{-4}$. Here the first uncertainties are statistical, the second systematic, and the third comes from the uncertainty of the $\Lambda_{c}^{+}\to\Lambda \pi^{+}$ branching fraction. This result, which is more precise than previous measurements, does not agree with theoretical predictions, and suggests that non-factorizable contributions have been under-estimated in current models.
Thirty-Two Years of IEEE VIS: Authors, Fields of Study and Citations

The IEEE VIS Conference (VIS) recently rebranded itself as a unified conference and officially positioned itself within the discipline of Data Science. Driven by this movement, we investigated (1) who contributed to VIS, and (2) where VIS stands in the scientific world. We examined the authors and fields of study of 3,240 VIS publications in the past 32 years based on data collected from OpenAlex and IEEE Xplore, among other sources. We also examined the citation flows from referenced papers (i.e., those referenced in VIS) to VIS, and from VIS to citing papers (i.e., those citing VIS). We found that VIS has been becoming increasingly popular and collaborative. The number of publications, of unique authors, and of participating countries have been steadily growing. Both cross-country collaborations, and collaborations between educational and non-educational affiliations, namely “cross-type collaborations”, are increasing. The dominance of the US is decreasing, and authors from China are now an important part of VIS. In terms of author affiliation types, VIS is increasingly dominated by authors from universities. We found that the topics, inspirations, and influences of VIS research is limited such that (1) VIS, and their referenced and citing papers largely fall into the Computer Science domain, and (2) citations flow mostly between the same set of subfields within Computer Science. Our citation analyses showed that award-winning VIS papers had higher citations. Interactive visualizations, replication data, source code and supplementary material are available at https://32vis.hongtaoh. com and https://osf.io/zkvjm.
Left seminear-rings, groups semidirect products and left cancellative left semi-braces

We study some relations between left cancellative left semi-braces and other existing algebraic structures. In particular, we show that every left semi-brace arises from a left seminear-ring, extending the correspondence given by Rump between skew left braces and left near-rings in \cite{rump2019set}. Moreover, we show a correspondence between certain groups semidirect products and left cancellative left semi-braces satisfying an additional hypothesis on the set of idempotents. As an application, we classify left cancellative left semi-braces of size $pq$ and $2p^2$ such that the set of idempotents $E$ is a Sylow subgroup of the multiplicative group. Finally, we study various type of nilpotency, recently introduced in \cite{catino2022nilpotency}, of these left semi-braces.
Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model

In order to steer e-commerce users towards making a purchase, marketers rely upon predictions of when users exit without purchasing. Previously, such predictions were based upon hidden Markov models (HMMs) due to their ability of modeling latent shopping phases with diﬀerent user intents. In this work, we develop a duration-dependent hidden Markov model. In contrast to traditional HMMs, it explicitly models the duration of latent states and thereby allows states to become “sticky”. The proposed model is superior to prior HMMs in detecting user exits: out of 100 user exits without purchase, it correctly identiﬁes an additional 18. This helps marketers in better managing the online behavior of e-commerce customers. The reason for the superior performance of our model is the duration dependence, which allows our model to recover latent states that are characterized by a distorted sense of time. We ﬁnally provide a theoretical explanation for this, which builds upon the concept of “ﬂow”.
A review on longitudinal data analysis with random forest

Abstract In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often promising alternatives to standard statistical methods, especially in the context of high-dimensional data. In this paper, we review extensions of the standard RF method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate response longitudinal data and further categorize the repeated measurements according to whether the time effect is relevant. Even though most extensions are proposed for low-dimensional data, some can be applied to high-dimensional data. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions.
First come first served: the impact of file position on code review

The most popular code review tools (e.g., Gerrit and GitHub) present the files to review sorted in alphabetical order. Could this choice or, more generally, the relative position in which a file is presented bias the outcome of code reviews? We investigate this hypothesis by triangulating complementary evidence in a two-step study. First, we observe developers’ code review activity. We analyze the review comments pertaining to 219,476 Pull Requests (PRs) from 138 popular Java projects on GitHub. We found files shown earlier in a PR to receive more comments than files shown later, also when controlling for possible confounding factors: e.g., the presence of discussion threads or the lines added in a file. Second, we measure the impact of file position on defect finding in code review. Recruit- ing 106 participants, we conduct an online controlled experiment in which we measure participants’ performance in detecting two unrelated defects seeded into two different files. Participants are assigned to one of two treatments in which the position of the defective files is switched. For one type of defect, participants are not affected by its file’s position; for the other, they have 64% lower odds to identify it when its file is last as opposed to first. Overall, our findings provide evidence that the relative position in which files are presented has an impact on code reviews’ outcome; we discuss these results and implications for tool design and code review.
Few-to-many particle crossover of pair excitations in a superfluid

Motivated by recent advances in the creation of few-body atomic Fermi gases with attractive interactions, we study theoretically the few-to-many particle crossover of pair excitations, which for large particle numbers evolve into modes describing amplitude fluctuations of the superfluid order parameter (the"Higgs"modes). Our analysis is based on a Richardson-type pairing model that captures interactions between time-reversed fermion pairs. The model is integrable, which allows a systematic quantitative study of the few-to-many particle crossover with only minor numerical effort. We first establish a parity effect in the ground state energy, which is quantified by a so-called Matveev-Larkin parameter that generalizes the pairing gap to mesoscopic ensembles and which behaves quantitatively differently in a few-body and a many-body regime. The crossover point for this quantity is widely tunable as a function of interaction strength. We then compute the excitation spectrum and demonstrate that the pair excitation energy shows a minimum that deepens with increasing particle number and shifts to smaller interaction strengths, consistent with the finite-size precursor of a quantum phase transition to a superfluid state. We extract a critical finite-size scaling exponent that characterizes the decrease of the gap with increasing particle number.
Variability Signatures of a Burst Process in Flaring Gamma-Ray Blazars

Blazars exhibit stochastic flux variability across the electromagnetic spectrum, often exhibiting heavy-tailed flux distributions, commonly modeled as lognormal. However, Tavecchio et al. and Adams et al. found that the high-energy gamma-ray flux distributions of several of the brightest flaring Fermi-LAT flat-spectrum radio quasars (FSRQs) are well modeled by an even heavier-tailed distribution, which we show is the inverse gamma distribution. We propose an autoregressive inverse gamma variability model in which an inverse gamma flux distribution arises as a consequence of a shot-noise process. In this model, discrete bursts are individually unresolved and averaged over within time bins, as in the analysis of Fermi-LAT data. Stochastic variability on timescales longer than the time-bin duration is modeled using first-order autoregressive structure. The flux distribution becomes approximately lognormal in the limiting case of many weak bursts. The fractional variability is predicted to decrease as the time-bin duration increases. Using simulated light curves, we show that the proposed model is consistent with the typical gamma-ray variability properties of FSRQs and BL Lac objects. The model parameters can be physically interpreted as the average burst rate, the burst fluence, and the timescale of long-term stochastic fluctuations.
Synchronized Firing Induced by Correlated Bidirectional Couplings in a Neural Network Model for Spontaneous Activity

In the absence of sensory stimuli, continuous neuronal firings are observed in cortical networks. Such self-sustained ongoing activity is referred to as “spontaneous activity,” the dynamics of which is characterized by (1) low firing frequency, (2) irregularity, and (3) asynchronous firings among neurons. Despite numerous theoretical attempts, the mechanism that underlies the spontaneous firing activity has remained unclear. Recently, Teramae et al. proposed a neuronal network model with excitatory postsynaptic potentials (EPSPs) obeying a lognormal distribution, as observed in physiological experiments. The model successfully reproduced the key features of the spontaneous activity. Their model, however, focused mainly on the lognormal distribution of the network connectivity, where the correlation of EPSPs observed between bidirectionally coupled neurons was disregarded. The present paper introduces the correlated EPSPs to the lognormal network model and shows that a physiologically plausible level of such correlation causes (i) synchronous firings among neurons, (ii) extremely high firing frequencies observed in a group of neurons, and (iii) intermittent switching between asynchronous and synchronous firing states.
High-gain observer for the nitrification process including sensor dynamics

Fertilization is commonly used to increase harvests. The lack of knowledge of soil properties and the excessive use of fertilizers can result in overfertilization. Current sensor technology is able to measure the concentrations of some of the involved substances only at selected locations and depths. Point measurements of adjacent sensors in coarse sensor networks can be used to infer upon the state of nitrate concentrations in the sensor surroundings. For this purpose, a high gain observer is proposed. Models of the nitrification process as well as the measurement dynamics for the observer design are derived and discretized on a grid to obtain a system of ordinary differential equations. It is shown that the nonlinearities of the model can be bounded and how the observer gain can be computed via linear matrix inequalities. Furthermore, a model reduction is proposed, which allows the consideration of more grid points. A simulation study demonstrates the proposed approach.
SelfCoLearn: Self-Supervised Collaborative Learning for Accelerating Dynamic MR Imaging

Lately, deep learning technology has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampled reference data for training, the current approaches may have limited abilities in recovering fine details or structures. To address this challenge, this paper proposes a self-supervised collaborative learning framework (SelfCoLearn) for accurate dynamic MR image reconstruction from undersampled k-space data directly. The proposed SelfCoLearn is equipped with three important components, namely, dual-network collaborative learning, reunderampling data augmentation and a special-designed co-training loss. The framework is flexible and can be integrated into various model-based iterative un-rolled networks. The proposed method has been evaluated on an in vivo dataset and was compared to four state-of-the-art methods. The results show that the proposed method possesses strong capabilities in capturing essential and inherent representations for direct reconstructions from the undersampled k-space data and thus enables high-quality and fast dynamic MR imaging.
Extensions of derivations and symmetric operators

Given a densely deﬁned skew-symmetric operators A 0 on a real or complex Hilbert space V , we parametrize all m -dissi-pative extensions in terms of contractions Φ : H - → H + , where H and H + are Hilbert spaces associated with a boundary quadruple . Such an extension generates a unitary C 0 -group if and only if Φ is a unitary operator. As corollary we obtain the parametrization of all selfadjoint extensions of a symmetric operator by unitary operators from H - to H + . Our results extend the theory of boundary triples initiated by von Neumann and developed by V. I. and M. L. Gorbachuk, J. Behrndt and M. Langer, S. A. Wegner and many others, in the sense that a boundary quadruple always exists (even if the defect indices are diﬀerent in the symmetric case).
Supplemental Materials: Inpainting at Modern Camera Resolution by Guided PatchMatch with Auto-Curation

Because of our research topic of inpainting at modern camera resolutions, we have prepared a 2 gigabyte supplemental material with an included HTML viewer that shows results of all methods at full resolution, and have uploaded that in an anonymized way to cloud file hosting. We checked with a program committee member and were told we can include a link as long as we are very confident we have anonymized it, which we are. The link is https://drive.google. com/ file/d/1Lmar1byASRReJ0SimBfWAE2Dt_-gMhor . To prove that the supplemental is not changed since the time of submission, the MD5 sum of the 2 gigabyte supplemental material is 59eb1591b1601f491bd6962e43e81672 .
Regular multiplier Hopf algebroids II. Integration on and duality of algebraic quantum groupoids

A fundamental feature of quantum groups is that many come in pairs of mutually dual objects, like finite-dimensional Hopf algebras and their duals, or quantisations of function algebras and of universal enveloping algebras of Poisson-Lie groups. The same phenomenon was studied for quantum groupoids in various settings. In the purely algebraic setup, the construction of a dual object was given by Schauenburg and by Kadison and Szlach\'anyi, but required the quantum groupoid to be finite with respect to the base. A sophisticated duality for measured quantum groupoids was developed by Enock, Lesieur and Vallin in the setting of von Neumann algebras. We propose a purely algebraic duality theory without any finiteness assumptions, generalising Van Daele's duality theory of multiplier Hopf algebras and borrowing ideas from the theory of measured quantum groupoids. Our approach is based on the multiplier Hopf algebroids recently introduced by Van Daele and the author, and on a new approach to integration on algebraic quantum groupoids. The main concept are left and right integrals on regular multiplier Hopf algebroids that are adapted to quasi-invariant weights on the basis. Given such integrals, we show that they are unique up to rescaling, admit modular automorphisms, and that left and right ones are related by modular elements. Then, we construct, without any finiteness or Frobenius assumption, a dual multiplier Hopf algebroid with integrals and prove biduality.
MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis

Autonomous bin picking poses significant challenges to vision-driven robotic systems given the complexity of the problem, ranging from various sensor modalities, to highly entangled object layouts, to diverse item properties and gripper types. Existing methods often address the problem from one perspective. Diverse items and complex bin scenes require diverse picking strategies together with advanced reasoning. As such, to build robust and effective machine-learning algorithms for solving this complex task requires significant amounts of comprehensive and high quality data. Collecting such data in real world would be too expensive and time prohibitive and therefore intractable from a scalability perspective. To tackle this big, diverse data problem, we take inspiration from the recent rise in the concept of metaverses, and introduce MetaGraspNet, a large-scale photo-realistic bin picking dataset constructed via physics-based metaverse synthesis. The pro-posed dataset contains 217k RGBD images across 82 different article types, with full annotations for object detection, amodal perception, keypoint detection, manipulation order and ambidextrous grasp labels for a parallel-jaw and vacuum gripper. We also provide a real dataset consisting of over 2.3k fully annotated high-quality RGBD images, divided into 5 levels of difficulties and an unseen object set to evaluate different object and layout properties. Finally, we conduct extensive experiments showing that our proposed vacuum seal model and synthetic dataset achieves state-of-the-art performance and generalizes to real world use-cases.
Estimating the Local Air Pollution Impacts of Maritime Traffic: A Principled Approach for Observational Data

We propose a new approach to estimate the causal effects of maritime traffic when natural or policy experiments are not available. We apply this method to the case of Marseille, a large Mediterranean port city, where air pollution emitted by cruise vessels is a growing concern. Using a recent matching algorithm designed for time series data, we create hypothetical randomized experiments to estimate the change in local air pollution caused by a short-term increase in cruise traffic. We then rely on randomization inference to compute nonparametric 95% uncertainty intervals. We find that cruise vessels’ arrivals have large impacts on city-level hourly concentrations of nitrogen dioxide, particulate matter, and sulfur dioxide. At the daily level, road traffic seems however to have a much larger impact than cruise traffic. Our procedure also helps assess in a transparent manner the identification challenges specific to this type of high-frequency time series data. Website: https://lzabrocki.github. io/cruise_air_pollution/ Replication Data: https://osf.io/v8aps/ †We thank the Marseille Port officials for sharing their port call data, Méteo-France for providing us their weather data, AtmoSud for openly sharing their air pollution data on their website, and Milena Suarez-Castillo for providing road traffic data from the DIR Méditerranée. We are grateful to Tirthankar Dasgupta for his guidance on the computation of Fisherian intervals and to Stéphane Shao for developing the matching algorithm. We thank Michela Baccini, Sylvain Chabé-Ferret, Olivier Chanel, Augustin Colette, Clément de Chaisemartin, Tatyana Deryugina, Francois Libois, Mirabelle Muûls, Hélène Ollivier, Thomas Piketty, Laure de Preux, Quentin Lippmann, Philippe Quirion, Adam Rosenberg, Thiago Scarelli, Katheline Schubert, Georgia Thebault, Ulrich Wagner, as well as participants to the PSE Applied Economics, PSIPSE and REM seminars and conference participants from EAERE and FAERE for their feedback. Léo Zabrocki and Marion Leroutier acknowledge the support of the EUR grant ANR-17-EURE-0001. 1RFF-CMCC European Institute on Economics and the Environment (EIEE), Milan, Italy. Email: leo. zabrocki@gmail.com. Corresponding author. 2Misum, Stockholm School of Economics, Stockholm, Sweden. Email: marion.leroutier@hhs.se 3Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA. Email: ma.bind@mail. harvard.edu ar X iv :2 10 5. 03 99 6v 2 [ st at . A P] 5 A ug 2 02 2
Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval

In this paper, we propose a new dense retrieval model which learns diverse document representations with deep query interactions. Our model encodes each document with a set of generated pseudo-queries to get query-informed, multi-view document representations. It not only enjoys high inference efficiency like the vanilla dual-encoder models, but also enables deep query-document interactions in document encoding and provides multi-faceted representations to better match different queries. Experiments on several benchmarks demonstrate the effectiveness of the proposed method, out-performing strong dual encoder baselines.The code is available at \url{https://github.com/jordane95/dual-cross-encoder
Probabilistic Amplitude Shaping and Nonlinearity Tolerance: Analysis and Sequence Selection Method

Probabilistic amplitude shaping (PAS) is a practical means to achieve a shaping gain in optical fiber communication. However, PAS and shaping in general also affect the signal-dependent generation of nonlinear interference. This provides an opportunity for nonlinearity mitigation through PAS, which is also referred to as a nonlinear shaping gain. In this paper, we introduce a linear lowpass filter model that relates transmitted symbol-energy sequences and nonlinear distortion experienced in an optical fiber channel. Based on this model, we conduct a nonlinearity analysis of PAS with respect to shaping blocklength and mapping strategy. Our model explains results and relationships found in literature and can be used as a design tool for PAS with improved nonlinearity tolerance. We use the model to introduce a new metric for PAS with sequence selection. We perform simulations of selection-based PAS with various amplitude shapers and mapping strategies to demonstrate the effectiveness of the new metric in different optical fiber system scenarios.