Miracle-dz/UniFAR-FTUs huggingface dataset:
Search for the Majorana Nature of Neutrinos in the Inverted Mass Ordering Region with KamLAND-Zen.

The KamLAND-Zen experiment has provided stringent constraints on the neutrinoless double-beta (0νββ) decay half-life in ^{136}Xe using a xenon-loaded liquid scintillator. We report an improved search using an upgraded detector with almost double the amount of xenon and an ultralow radioactivity container, corresponding to an exposure of 970 kg yr of ^{136}Xe. These new data provide valuable insight into backgrounds, especially from cosmic muon spallation of xenon, and have required the use of novel background rejection techniques. We obtain a lower limit for the 0νββ decay half-life of T_{1/2}^{0ν}>2.3×10^{26} yr at 90% C.L. , corresponding to upper limits on the effective Majorana neutrino mass of 36-156 meV using commonly adopted nuclear matrix element calculations.
IISERB Brains at SemEval-2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English

This paper describes the system architectures and the models submitted by our team “IISERB Brains” to SemEval 2022 Task 6 competition. We contested for all three sub-tasks floated for the English dataset. On the leader-board, we got 19th rank out of 43 teams for sub-task A, 8th rank out of 22 teams for sub-task B, and 13th rank out of 16 teams for sub-task C. Apart from the submitted results and models, we also report the other models and results that we obtained through our experiments after organizers published the gold labels of their evaluation data. All of our code and links to additional resources are present in GitHub for reproducibility.
A combinatorial model for lane merging

A two lane road approaches a stoplight. The left lane merges into the right just past the intersection. Vehicles approach the intersection one at a time, with some drivers always choosing the right lane, while others always choose the shorter lane, giving preference to the right lane to break ties. An arrival sequence of vehicles can be represented as a binary string, where the zeros represent drivers always choosing the right lane, and the ones represent drivers choosing the shorter lane. From each arrival sequence we construct a merging path, which is a lattice path determined by the lane chosen by each car. We give closed formulas for the number of merging paths reaching the point (n,m) with exactly k zeros in the arrival sequence, and the expected length of the right lane for all arrival sequences with exactly k zeros. Proofs involve an adaptation of Andre’s Reflection Principle. Other interesting connections also emerge, including to: Ballot numbers, the expected maximum number of heads or tails appearing in a sequence of n coin flips, the largest domino snake that can be made using pieces up to [n : n], and the longest trail on the complete graph Kn with loops.
A Star-sized Impact-produced Dust Clump in the Terrestrial Zone of the HD 166191 System

We report on five years of 3–5 μm photometry measurements obtained by warm Spitzer to track the dust debris emission in the terrestrial zone of HD 166191 in combination with simultaneous optical data. We show that the debris production in this young (∼10 Myr) system increased significantly in early 2018 and reached a record high level (almost double by mid 2019) by the end of the Spitzer mission (early 2020), suggesting intense collisional activity in its terrestrial zone likely due to either initial assembling of terrestrial planets through giant impacts or dynamical shake-up from unseen planet-mass objects or recent planet migration. This intense activity is further highlighted by detecting a star-size dust clump, passing in front of the star, in the midst of its infrared brightening. We constrain the minimum size and mass of the clump using multiwavelength transit profiles and conclude that the dust clump is most likely created by a large impact involving objects of several hundred kilometers in size with an apparent period of 142 days (i.e., 0.62 au, assuming a circular orbit). The system’s evolutionary state (right after the dispersal of its gas-rich disk) makes it extremely valuable to learn about the process of terrestrial-planet formation and planetary architecture through future observations.
Selective Pseudo-labeling and Class-wise Discriminative Fusion for Sound Event Detection

In recent years, exploring effective sound separation (SSep) techniques to improve overlapping sound event detection (SED) attracts more and more attention. Creating accurate separation signals to avoid the catastrophic error accumulation during SED model training is very important and challenging. In this study, we first propose a novel selective pseudo-labeling approach, termed SPL, to produce high confidence separated target events from blind sound separation outputs. These target events are then used to fine-tune the original SED model that pre-trained on the sound mixtures in a multi-objective learning style. Then, to further leverage the SSep outputs, a class-wise discriminative fusion is proposed to improve the final SED performances, by combining multiple frame-level event predictions of both sound mixtures and their separated signals. All experiments are performed on the public DCASE 2021 Task 4 dataset, and results show that our approaches significantly outperforms the official baseline, the collar-based F 1, PSDS1 and PSDS2 performances are improved from 44.3%, 37.3% and 54.9% to 46.5%, 44.5% and 75.4%, respectively.
BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System

Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition systems' hardware and software are usually closed-source. Its inability to flexible expansion and secondary development is a major obstacle to real-time BCI research. This paper presents the Beijing University of Posts and Telecommunications EEG Acquisition Tool System named BEATS. It implements a comprehensive system from hardware to software, composed of the analog front end, microprocessor, and software platform. BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4 kHz with wireless transmission. Compared to state-of-the-art systems used in many EEG fields, it displays a better sampling rate. Using techniques including direct memory access, first in first out, and timer, the precision and stability of the acquisition are ensured at the microsecond level. An evaluation is conducted during 24 hours of continuous acquisitions. There are no packet losses and the average maximum delay is only 0.07 s/h. Moreover, as an open-source system, BEATS provides detailed design files, and adopts a plug-in structure and easy-to-access materials, which makes it can be quickly reproduced.
Petz-Rényi Relative Entropy of Quantum States Reduces to Rényi Relative Entropy of Classical Probability Measures

Let K be a complex separable Hilbert space of finite or infinite dimensions. We prove that the Petz-Rényi relative entropy of any two quantum states ρ and σ on K, denoted by Dα(ρ||σ) reduces to the Rényi relative entropy (divergence) of two classical probability measures P and Q obtained from ρ and σ. This leads to a number of new results in the infinite dimensions, and new proofs for some known results in the finite dimensional setting. Our method provides a general framework for proving a quantum counterpart of any result about the classical Rényi divergence involving only two probability distributions. Furthermore, we construct an example to show that the information theoretic definition of the von Neumann relative entropy is different from Araki’s definition of relative entropy. This disproves a recent attempt in the literature to prove such a result. All the results proved here are valid in both finite and infinite dimensions and hence can be applied to continuous variable systems as well.
Benchmarking tunnel and encryption methodologies in cloud environments

The recent past has seen the adoption of multi-cloud deployments by enterprises due to availability, features and regulatory requirements. A typical deployment involves parts of an application/workloads running inside a private cloud with the other parts spread across multiple on-prem/public clouds. Typical cluster-to-cluster networking in such deployments involve the establishment of site-to-site encrypted tunnels to connect the workloads. In this report, we benchmark the performance of various tunneling and encryption technologies to provide directions on their use in multi-cloud deployments. Based on the various experiments conducted on three dierent testbeds, we present quantiable data which can be leveraged by operators and cloud providers tasked with design and development decisions of multi-cloud network connectivity and orchestration.
Peeling dynamics of fluid membranes bridged by molecular bonds: moving or breaking

Biological adhesion is a critical mechanical function of complex organisms. At the scale of cell–cell contacts, adhesion is remarkably tunable to enable both cohesion and malleability during development, homeostasis and disease. It is physically supported by transient and laterally mobile molecular bonds embedded in fluid membranes. Thus, unlike specific adhesion at solid–solid or solid–fluid interfaces, peeling at fluid–fluid interfaces can proceed by breaking bonds, by moving bonds or by a combination of both. How the additional degree of freedom provided by bond mobility changes the mechanics of peeling is not understood. To address this, we develop a theoretical model coupling diffusion, reactions and mechanics. Mobility and reaction rates determine distinct peeling regimes. In a diffusion-dominated Stefan-like regime, bond motion establishes self-stabilizing dynamics that increase the effective fracture energy. In a reaction-dominated regime, peeling proceeds by travelling fronts where marginal diffusion and unbinding control peeling speed. In a mixed reaction–diffusion regime, strengthening by bond motion competes with weakening by bond breaking in a force-dependent manner, defining the strength of the adhesion patch. In turn, patch strength depends on molecular properties such as bond stiffness, force sensitivity or crowding. We thus establish the physical rules enabling tunable cohesion in cellular tissues and in engineered biomimetic systems.
Can Authoritative Governments Abuse the Right to Access?

The right to access is a great tool provided by the GDPR to empower data subjects with their data. However, it needs to be implemented properly otherwise it could turn subject access requests against the subjects privacy. Indeed, recent works have shown that it is possible to abuse the right to access using impersonation attacks. We propose to extend those impersonation attacks by considering that the adversary has an access to governmental resources. In this case, the adversary can forge official documents or exploit copy of them. Our attack affects more people than one may expect. To defeat the attacks from this kind of adversary, several solutions are available like multi-factors or proof of aliveness. Our attacks highlight the need for strong procedures to authenticate subject access requests.
FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning

Wi-Fi sensing can classify human activities because each activity causes unique changes to the channel state information (CSI). Existing WiFi sensing suffers from limited scalability as the system needs to be retrained whenever new classes are added, which causes overheads of data collection and retraining. Cross-domain sensing may fail because the mapping between activities and CSI variations is destroyed when a different environment or user (domain) is involved. This paper proposed a few-shot learning-based WiFi sensing system, named FewSense, which can recognise novel classes in unseen domains with only a few samples. Specifically, a feature extractor was pre-trained offline using the source domain data. When the system was applied in the target domain, a few samples were used to fine-tune the feature extractor for domain adaptation. Inference was made by computing the cosine similarity. FewSense can further boost the classification accuracy by collaboratively fusing inference from multiple receivers. We evaluated the performance of FewSense using three public datasets, i.e., SignFi, Widar, and Wiar. The results show that FewSense with five-shot learning recognised novel classes in unseen domains with an accuracy of 93.9%, 96.5%, and 82.7% on the SignFi, Widar, and Wiar datasets, respectively. Our collaborative sensing model improved system performance by an average of 29.2%.
Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation

Existing continual relation learning (CRL) methods rely on plenty of labeled training data for learning a new task, which can be hard to acquire in real scenario as getting large and representative labeled data is often expensive and time-consuming. It is therefore necessary for the model to learn novel relational patterns with very few labeled data while avoiding catastrophic forgetting of previous task knowledge. In this paper, we formulate this challenging yet practical problem as continual few-shot relation learning (CFRL). Based on the finding that learning for new emerging few-shot tasks often results in feature distributions that are incompatible with previous tasks’ learned distributions, we propose a novel method based on embedding space regularization and data augmentation. Our method generalizes to new few-shot tasks and avoids catastrophic forgetting of previous tasks by enforcing extra constraints on the relational embeddings and by adding extra relevant data in a self-supervised manner. With extensive experiments we demonstrate that our method can significantly outperform previous state-of-the-art methods in CFRL task settings.
Collision-Aware Fast Simulation for Soft Robots by Optimization-Based Geometric Computing

Soft robots can safely interact with environments because of their mechanical compliance. Self-collision is also employed in the modern design of soft robots to enhance their performance during different tasks. However, developing an efficient and reliable simulator that can handle the collision response well, is still a challenging task in the research of soft robotics. This paper presents a collision-aware simulator based on geometric optimization, in which we develop a highly efficient and realistic collision checking / response model incorporating a hyperelastic material property. Both actuated deformation and collision response for soft robots are formulated as geometry-based objectives. The collision-free body of a soft robot can be obtained by minimizing the geometry-based objective function. Unlike the FEA-based physical simulation, the proposed pipeline performs a much lower computational cost. Moreover, adaptive remeshing is applied to achieve the improvement of the convergence when dealing with soft robots that have large volume variations. Experimental tests are conducted on different soft robots to verify the performance of our approach.
Sharper Bounds for Proximal Gradient Algorithms with Errors

We analyse the convergence of the proximal gradient algorithm for convex composite problems in the presence of gradient and proximal computational inaccuracies. We derive new tighter deterministic and probabilistic bounds that we use to verify a simulated (MPC) and a synthetic (LASSO) optimization problems solved on a reduced-precision machine in combination with an inaccurate proximal operator. We also show how the probabilistic bounds are more robust for algorithm verification and more accurate for application performance guarantees. Under some statistical assumptions, we also prove that some cumulative error terms follow a martingale property. And conforming to observations, e.g., in \cite{schmidt2011convergence}, we also show how the acceleration of the algorithm amplifies the gradient and proximal computational errors.
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation

3D Multi-Object Tracking (MOT) is an important part of the unmanned vehicle perception module. Most methods optimize object detection and data association independently. These methods make the network structure complicated and limit the improvement of MOT accuracy. we proposed a 3D MOT framework based on simultaneous optimization of object detection and scene flow estimation. In the framework, a detection-guidance scene flow module is proposed to relieve the problem of incorrect inter-frame assocation. For more accurate scene flow label especially in the case of motion with rotation, a box-transformation-based scene flow ground truth calculation method is proposed. Experimental results on the KITTI MOT dataset show competitive results over the state-of-the-arts and the robustness under extreme motion with rotation.
All-purpose, all-sky photometric redshifts for the Legacy Imaging Surveys Data Release 8

In this paper we present photometric redshift (photo-z) estimates for the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, currently the most sensitive optical survey covering the majority of the extra-galactic sky. Our photo-z methodology is based on a machine-learning approach, using sparse Gaussian processes augmented with Gaussian mixture models (GMMs) that allow regions of parameter space to be identified and trained separately in a purely data-driven way. The same GMMs are also used to calculate cost-sensitive learning weights that mitigate biases in the spectroscopic training sample. By design, this approach aims to produce reliable and unbiased predictions for all parts of the parameter space present in wide area surveys. Compared to previous literature estimates using the same underlying photometry, our photo-zs are significantly less biased and more accurate at z > 1, with negligible loss in precision or reliability for resolved galaxies at z < 1. Our photo-z estimates offer accurate predictions for rare high-value populations within the parent sample, including optically selected quasars at the highest redshifts (z > 6), as well as X-ray or radio continuum selected populations across a broad range of flux (densities) and redshift. Deriving photo-z estimates for the full Legacy Imaging Surveys Data Release 8, the catalogues provided in this work offer photo-z estimates predicted to be high quality for & 9 × 108 galaxies over ∼ 19 400 deg2 and spanning 0 < z . 7, offering one of the most extensive samples of redshift estimates ever produced.
Compositional Mapping of Europa Using MCMC Modeling of Near-IR VLT/SPHERE and Galileo/NIMS Observations

We present maps of surface composition of Europa’s anti-Jovian hemisphere acquired using high spatial resolution IFU multispectral data from the SPHERE instrument on the Very Large Telescope (0.95–1.65 μm) and the NIMS instrument on the Galileo orbiter (0.7–5.2 μm). Spectral modeling was performed using a Markov Chain Monte Carlo method to estimate endmember abundances and to quantify their associated uncertainties. Modeling results support the leading–trailing hemisphere difference in hydrated sulfuric acid abundances caused by exogenic plasma bombardment. Water-ice grains are found to be in the 100 μm–1 mm range, with larger grains present on the trailing hemisphere, consistent with radiation-driven sputtering destroying smaller grains. Modeling best estimates suggest a mixture of sulfate and chlorinated salts, although uncertainties derived from the MCMC modeling suggest that it is difficult to confidently detect individual salt abundances with low spectral resolution spectra from SPHERE and NIMS. The high spatial resolution offered by SPHERE allows the small-scale spatial distribution (<150 km) of potential species to be mapped, including ground-based detection of lineae and impact features. This could be used in combination with other higher spectral resolution observations to confirm the presence of these species.
Scaling K2. V. Statistical Validation of 60 New Exoplanets From K2 Campaigns 2–18

The NASA K2 mission, salvaged from the hardware failures of the Kepler telescope, has continued Kepler’s planet-hunting success. It has revealed nearly 500 transiting planets around the ecliptic plane, many of which are the subject of further study, and over 1000 additional candidates. Here we present the results of an ongoing project to follow-up and statistically validate new K2 planets, in particular to identify promising new targets for further characterization. By analyzing the reconnaissance spectra, high-resolution imaging, centroid variations, and statistical likelihood of the signals of 91 candidates, we validate 60 new planets in 46 systems. These include a number of planets amenable to transmission spectroscopy (K2-384 f, K2-387 b, K2-390 b, K2-403 b, and K2-398 c), emission spectroscopy (K2-371 b, K2-370 b, and K2-399 b), and both (K2-405 b and K2-406 b); several systems with planets in or close to mean motion resonances (K2-381 and K2-398) including a compact, TRAPPIST-1-like system of five small planets orbiting a mid-M dwarf (K2-384); an ultra-short-period sub-Saturn in the hot Saturn desert (K2-399 b); and a super-Earth orbiting a moderately bright (V = 11.93), metal-poor ([Fe/H] = −0.579 ± 0.080) host star (K2-408 b). In total we validate planets around four F stars, 26 G stars, 13 K stars, and three M dwarfs. In addition, we provide a list of 37 vetted planet candidates that should be prioritized for future follow-up observation in order to be confirmed or validated.
Instantaneous support propagation for $\Lambda$-Fleming-Viot processes

For a probability-measure-valued neutral Fleming-Viot process Zt with Lévy mutation and resampling mechanism associated to a general Λ-coalescent with multiple collisions, we prove the instantaneous propagation of supports. That is, at any fixed time t > 0, with probability one the closed support S(Zt) of the Fleming-Viot process satisfies S(ν ∗ Zt) ⊆ S(Zt), where ν is the Lévy measure of the mutation process. To show this result, we apply Donnelly-Kurtz’s lookdown particle representation for Fleming-Viot processes.
Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, such as when pedestrians suddenly start or stop walking. We suggest that predicting these highly non-linear transitions should form a core component to improve the robustness of motion prediction algorithms. In this paper, we introduce the new task of pedestrian stop and go forecasting. Considering the lack of suitable existing datasets for it, we release TRANS, a benchmark for explicitly studying the stop and go behaviors of pedestrians in urban traffic. We build it from several existing datasets annotated with pedestrians' walking motions, in order to have various scenarios and behaviors. We also propose a novel hybrid model that leverages pedestrian-specific and scene features from several modalities, both video sequences and high-level attributes, and gradually fuses them to integrate multiple levels of context. We evaluate our model and several baselines on TRANS, and set a new benchmark for the community to work on pedestrian stop and go forecasting.