Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA

Recent works have shown that supervised models often exploit data artifacts to achieve good test scores while their performance severely degrades on samples outside their training distribution. Contrast sets (Gardneret al., 2020) quantify this phenomenon by perturbing test samples in a minimal way such that the output label is modified. While most contrast sets were created manually, requiring intensive annotation effort, we present a novel method which leverages rich semantic input representation to automatically generate contrast sets for the visual question answering task. Our method computes the answer of perturbed questions, thus vastly reducing annotation cost and enabling thorough evaluation of models’ performance on various semantic aspects (e.g., spatial or relational reasoning). We demonstrate the effectiveness of our approach on the GQA dataset and its semantic scene graph image representation. We find that, despite GQA’s compositionality and carefully balanced label distribution, two high-performing models drop 13-17% in accuracy compared to the original test set. Finally, we show that our automatic perturbation can be applied to the training set to mitigate the degradation in performance, opening the door to more robust models.
Visual Place Recognition using LiDAR Intensity Information

Robots and autonomous systems need to know where they are within a map to navigate effectively. Thus, simultaneous localization and mapping or SLAM is a common building block of robot navigation systems. When building a map via a SLAM system, robots need to re-recognize places to find loop closure and reduce the odometry drift. Image-based place recognition received a lot of attention in computer vision, and in this work, we investigate how such approaches can be used for 3D LiDAR data. Recent LiDAR sensors produce high-resolution 3D scans in combination with comparably stable intensity measurements. Through a cylindrical projection, we can turn this information into a 360° panoramic range image. As a result, we can apply techniques from visual place recognition to LiDAR intensity data. The question of how well this approach works in practice has only partially been investigated. This paper provides an analysis of how such visual techniques can be with LiDAR data, and we provide an evaluation on different datasets. Our results suggest that this form of place recognition is possible and an effective means for determining loop closures.
Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots

Multilingual models have demonstrated impressive cross-lingual transfer performance. However, test sets like XNLI are monolingual at the example level. In multilingual communities, it is common for polyglots to code-mix when conversing with each other. Inspired by this phenomenon, we present two strong black-box adversarial attacks (one word-level, one phrase-level) for multilingual models that push their ability to handle code-mixed sentences to the limit. The former uses bilingual dictionaries to propose perturbations and translations of the clean example for sense disambiguation. The latter directly aligns the clean example with its translations before extracting phrases as perturbations. Our phrase-level attack has a success rate of 89.75% against XLM-R-large, bringing its average accuracy of 79.85 down to 8.18 on XNLI. Finally, we propose an efficient adversarial training scheme that trains in the same number of steps as the original model and show that it creates more language-invariant representations, improving clean and robust accuracy in the absence of lexical overlap without degrading performance on the original examples.
Dose-response function approach for detecting spreading processes in temporal network data Exploring social contagion in the Copenhagen Networks Study

Spreading or complex contagion processes on networks are an important mechanistic foundation of tipping dynamics and other nonlinear phenomena in complex social, ecological and technological systems. Increasing amounts of temporal network data are now becoming available to study such spreading processes of behaviours, opinions, ideas, diseases, innovations or technologies and to test hypotheses regarding their speciﬁc properties. To this end, we here present a methodology based on dose-response functions and hypothesis testing using surrogate data sets. We demonstrate this methodology for synthetic temporal network data generated by the adaptive voter model. Furthermore, we apply it to empirical temporal network data from the Copenhagen Networks Study. This data set provides a physically-close-contact network between university students participating in the study over the course of three months. We study the potential spreading dynamics of the health-related behaviour “regularly going to the ﬁtness studio” on this network. Based on a hierarchy of surrogate data models, we ﬁnd that the empirical data neither provide signiﬁcant evidence for an inﬂuence of a dose-response-type network spreading process, nor signiﬁcant evidence for homophily. The empirical dynamics in exercise behaviour are likely better described by individual features such as the disposition towards the behaviour, and the persistence to maintain it, as well as external inﬂuences aﬀecting the whole group, and the non-trivial network structure. The proposed methodology is generic and promising also for applications to other data sets and traits of interest.
Multi-channel Deep Supervision for Crowd Counting

Crowd counting is a task worth exploring in modern society because of its wide applications such as public safety and video monitoring. Many CNN-based approaches have been proposed to improve the accuracy of estimation, but there are some inherent issues affect the performance, such as overfitting and details lost caused by pooling layers. To tackle these problems, in this paper, we propose an effective network called MDSNet, which introduces a novel supervision framework called Multi-channel Deep Supervision (MDS). The MDS conducts channel-wise supervision on the decoder of the estimation model to help generate the density maps. To obtain the accurate supervision information of different channels, the MDSNet employs an auxiliary network called SupervisionNet (SN) to generate abundant supervision maps based on existing groundtruth. Besides the traditional density map supervision, we also use the SN to convert the dot annotations into continuous supervision information and conduct dot supervision in the MDSNet. Extensive experiments on several mainstream benchmarks show that the proposed MDSNet achieves competitive results and the MDS significantly improves the performance without changing the network structure.
What’s in My LiDAR Odometry Toolbox?

With the democratization of 3D LiDAR sensors, precise LiDAR odometries and SLAM are in high demand. New methods regularly appear, proposing solutions ranging from small variations in classical algorithms to radically new paradigms based on deep learning. Yet it is often difficult to compare these methods, notably due to the few datasets on which the methods can be evaluated and compared. Furthermore, their weaknesses are rarely examined, often letting the user discover the hard way whether a method would be appropriate for a use case.In this paper, we review and organize the main 3D LiDAR odometries into distinct categories. We implemented several approaches (geometric based, deep learning based, and hybrid methods) to conduct an in-depth analysis of their strengths and weaknesses on multiple datasets, guiding the reader through the different LiDAR odometries available. Implementation of the methods has been made publicly available at: https://github.com/Kitware/pyLiDAR-SLAM.
Measuring Stellar Masses of Emission-line Galaxies at 1.2 < z < 1.9

The accurate measurement of stellar masses over a wide range of galaxy properties is essential for better constraining models of galaxy evolution. Emission-line galaxies (ELGs) tend to have better redshift estimates than continuum-selected objects, and have been shown to span a large range of physical properties, including stellar mass. Using data from the 3D-HST Treasury program, in this work, we construct a carefully vetted sample of 4350 ELGs at redshifts of 1.16 < z < 1.90. We combine the 3D-HST emission-line fluxes with far-UV through near-IR photometry, and use the MCSED spectral energy distribution fitting code to constrain the galaxies’ physical parameters, such as their star-formation rate and stellar masses. Our sample is consistent with the z ∼ 2 mass–metallicity relation. More importantly, we show that there is a simple, but tight correlation between stellar mass and absolute magnitude in a near-IR filter, which should prove particularly useful in terms of the rapid calculation of accurate stellar masses for millions of galaxies in upcoming missions such as Euclid, and the Nancy Grace Roman Space Telescope.
Nonlocal Ernst Equations

We show that the Ernst equations for stationary axially symmetric Einstein-Maxwell and Einstein N-abelian Yang-Mills field equations have local and nonlocal reductions. Among these reduced equations the nonlocal Ernst equations are new. We show that a class of these field equations admit reflection symmetry without requiring the asymptotical flatness.
X-ray emitting structures in the Vela SNR: ejecta anisotropies and progenitor stellar wind residuals

Context. The Vela supernova remnant (SNR) shows several ejecta fragments (or shrapnel) protruding beyond the forward shock, which are most likely relics of anisotropies that developed during the supernova (SN) explosion. Recent studies have revealed high Si abundance in two shrapnel (shrapnel A and G), located in opposite directions with respect to the SNR center. This suggests the possible existence of a Si-rich jet-counterjet structure, similar to that observed in the SNR Cassiopea A. Aims. We analyzed an XMM-Newton observation of a bright clump, behind shrapnel G, which lies along the direction connecting shrapnel A and G. The aim is to study the physical and chemical properties of this clump to ascertain whether it is part of this putative jet-like structure. Methods. We produced background-corrected and adaptively-smoothed count-rate images and median photon energy maps, and performed a spatially resolved spectral analysis. Results. We identified two structures with different physical properties. The first one is remarkably elongated along the direction connecting shrapnel A and G. Its X-ray spectrum is much softer than that of the other two shrapnel, to the point of hindering the determination of the Si abundance; however, its physical and chemical properties are consistent with those of shrapnel A and shrapnel G. The second structure, running along the southeast-northwest direction, has a higher temperature and appears similar to a thin filament. By analyzing the ROSAT data, we have found that this filament is part of a very large and coherent structure that we identified in the western rim of the shell. Conclusions. We obtained a thorough description of the collimated, jet-like tail of shrapnel G in Vela SNR. In addition we discovered a coherent and very extended feature roughly perpendicular to the jet-like structure that we interpret as a signature of an earlier interaction of the remnant with the stellar wind of its progenitor star. The peculiar Ne/O ratio we found in the wind residual may be suggestive of a Wolf-Rayet progenitor for Vela SNR, though further analysis is required to address this point.
Direct Observation of Terahertz Frequency Comb Generation in Difference-Frequency Quantum Cascade Lasers

Terahertz quantum cascade laser sources based on intra-cavity difference frequency generation from mid-IR devices are an important asset for applications in rotational molecular spectroscopy and sensing, being the only electrically pumped device able to operate in the 0.6–6 THz range without the need of bulky and expensive liquid helium cooling. Here we present comb operation obtained by intra-cavity mixing of a distributed feedback laser at λ = 6.5 μm and a Fabry–Pérot device at around λ = 6.9 μm. The resulting ultra-broadband THz emission extends from 1.8 to 3.3 THz, with a total output power of 8 μW at 78 K. The THz emission has been characterized by multi-heterodyne detection with a primary frequency standard referenced THz comb, obtained by optical rectification of near infrared pulses. The down-converted beatnotes, simultaneously acquired, confirm an equally spaced THz emission down to 1 MHz accuracy. In the future, this setup can be used for Fourier transform based evaluation of the phase relation among the emitted THz modes, paving the way to room-temperature, compact, and field-deployable metrological grade THz frequency combs.
In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning

In this study, we report the successful execution of in-air knotting of rope using a dual-arm two-finger robot based on deep learning. Owing to its flexibility, the state of the rope was in constant flux during the operation of the robot. This required the robot control system to dynamically correspond to the state of the object at all times. However, a manual description of appropriate robot motions corresponding to all object states is difficult to be prepared in advance. To resolve this issue, we constructed a model that instructed the robot to perform bowknots and overhand knots based on two deep neural networks trained using the data gathered from its sensorimotor, including visual and proximity sensors. The resultant model was verified to be capable of predicting the appropriate robot motions based on the sensory information available online. In addition, we designed certain task motions based on the Ian knot method using the dual-arm two-fingers robot. The designed knotting motions do not require a dedicated workbench or robot hand, thereby enhancing the versatility of the proposed method. Finally, experiments were performed to estimate the knotting performance of the real robot while executing overhand knots and bowknots on rope and its success rate. The experimental results established the effectiveness and high performance of the proposed method.
Investigating X-Ray Sources during the Epoch of Reionization with the 21 cm Signal

Heating of neutral gas by energetic sources is crucial for the prediction of the 21 cm signal during the epoch of reionization. To investigate differences induced on the statistics of the 21 cm signal by various source types, we use five radiative transfer simulations that have the same stellar UV emission model and varying combinations of more energetic sources, such as X-ray binaries (XRBs), accreting nuclear black holes (BHs), and hot interstellar medium emission (ISM). We find that the efficient heating from the ISM increases the average global 21 cm signal while reducing its fluctuations and thus power spectrum. A clear impact is also observed in the bispectrum in terms of scale and timing of the transition between a positive and a negative value. The impact of XRBs is similar to that of the ISM, although it is delayed in time and reduced in intensity because of the less efficient heating. Due to the paucity of nuclear BHs, the behavior of the 21 cm statistics in their presence is very similar to that of a case when only stars are considered, with the exception of the latest stages of reionization, when the effect of BHs is clearly visible. We find that differences between the source scenarios investigated here are larger than the instrumental noise of SKA1-low at z ≳ 7–8, suggesting that in the future it might be possible to constrain the spectral energy distribution of the sources contributing to the reionization process.
A unified description of gravity- and kinematics-induced segregation forces in dense granular flows

Abstract Particle segregation is common in natural and industrial processes involving flowing granular materials. Complex, and seemingly contradictory, segregation phenomena have been observed for different boundary conditions and forcing. Using discrete element method simulations, we show that segregation of a single particle intruder can be described in a unified manner across different flow configurations. A scaling relation for the net segregation force is obtained by measuring forces on an intruder particle in controlled-velocity flows where gravity and flow kinematics are varied independently. The scaling law consists of two additive terms: a buoyancy-like gravity-induced pressure gradient term and a shear rate gradient term, both of which depend on the particle size ratio. The shear rate gradient term reflects a kinematics-driven mechanism whereby larger (smaller) intruders are pushed toward higher (lower) shear rate regions. The scaling is validated, without refitting, in wall-driven flows, inclined wall-driven flows, vertical silo flows, and free-surface flows down inclines. Comparing the segregation force with the intruder weight results in predictions of the segregation direction that match experimental and computational results for various flow configurations.
Gauss sums and the maximum cliquesin generalized Paley graphs of square order

Let GP (q, d) be the d-Paley graph defined on the finite field Fq . It is notoriously difficult to improve the trivial upper bound √ q on the clique number of GP (q, d). In this paper, we investigate the connection between Gauss sums over a finite field and the maximum cliques of their corresponding generalized Paley graphs. We show that the trivial upper bound on the clique number of GP (q, d) is tight if and only if d | (√q + 1), which strengthens the previous related results by Broere-Döman-Ridley and Schneider-Silva. We also obtain a new simple proof of Stickelberger’s theorem on evaluating semi-primitive Gauss sums.
Developing a Maximum-Entropy Restricted Boltzmann Machine with a Quantum Thermodynamics Formalism

We propose a Restricted Boltzmann Machine (RBM) neural network using a quantum thermodynamics formalism and the maximization of entropy as the cost function for the optimization problem. We verify the possibility of using an entropy maximization approach for modeling physical systems by applying it to the Inverse Ising Problem, which describes a system of interacting spins. The temperature-dependent behaviors of physical quantities, such as magnetization and Gibbs energy, were calculated from the Ising Hamiltonian whose coefficients were obtained through entropy maximization and were found to be in good agreement to literature results. These results suggest that RBM neural networks using the principle of maximum entropy can be applied to modeling physical systems that can be described by discrete states, including fundamental quantum physics such as topological systems, and biological systems such as the correlated spiking of neurons.
The Double-Faced Electrostatic Behavior of PNIPAm Microgels

PNIPAm microgels synthesized via free radical polymerization (FRP) are often considered as neutral colloids in aqueous media, although it is well known, since the pioneering works of Pelton and coworkers, that the vanishing electrophoretic mobility characterizing swollen microgels largely increases above the lower critical solution temperature (LCST) of PNIPAm, at which microgels partially collapse. The presence of an electric charge has been attributed to the ionic initiators that are employed when FRP is performed in water and that stay anchored to microgel particles. Combining dynamic light scattering (DLS), electrophoresis, transmission electron microscopy (TEM) and atomic force microscopy (AFM) experiments, we show that collapsed ionic PNIPAm microgels undergo large mobility reversal and reentrant condensation when they are co-suspended with oppositely charged polyelectrolytes (PE) or nanoparticles (NP), while their stability remains unaffected by PE or NP addition at lower temperatures, where microgels are swollen and their charge density is low. Our results highlight a somehow double-faced electrostatic behavior of PNIPAm microgels due to their tunable charge density: they behave as quasi-neutral colloids at temperature below LCST, while they strongly interact with oppositely charged species when they are in their collapsed state. The very similar phenomenology encountered when microgels are surrounded by polylysine chains and silica nanoparticles points to the general character of this twofold behavior of PNIPAm-based colloids in water.
Artificial food sweetener aspartame induces stress response in model organism Schizosaccharomyces pombe

Aspartame (APM) is a non-nutritive artificial sweetener that has been widely used in many products since 1981. Molecular studies have found that it alters the expression of tumour suppressor genes and oncogenes, forms DNA-DNA and DNA-protein crosslinks, and sister chromatid exchanges. While these results confirm that aspartame is a carcinogenic substance, other studies have failed to detect any negative effect. The present work was aimed to reveal the molecular mechanisms of APM’s effects in the simpler model organism, Schizosaccharomyces pombe, which has cellular processes similar to those of mammals. The human HP1 (heterochromatin protein 1) family ortholog swi6 was selected for the evaluation because swi6 expression is downregulated in cancer cells. Swi6 is a telomere, centromere, and mating-type locus binding protein which regulates the structure of heterochromatin. To verify whether the carcinogenic effects of APM are linked with Swi6, S. pombe parental and swi6Δ strains were analysed through a number of tests, including cell viability, intracellular oxidation, glucose consumption, nucleus DAPI (4',6-diamidino-2-phenylindole) staining, and quantitative real time polymerase chain reaction (qRT-PCR) methods. Based on the results, the S. pombe parental strain adapts to APM effects by activating the stress response pathway, while swi6Δ did not show a meaningful response. Thus, it is proposed that there is a relationship between APM and Swi6, and that APM may be carrying out its effects through Swi6. Nevertheless, it is understood that aspartame is not an effective carcinogenic agent since its effects are weak when compared with the swi6Δ phenotype.
QUANTIZATION OF CHEMICAL REACTION: THE DYNAMIC CORRELATION DIAGRAM METHOD FREE FROM NONCROSSING RULE

Both Fukui's frontier orbital (FO) theory and Woodward–Hoffmann's orbital symmetry conservation (W–H) theory are based on the molecular orbital theory. However, there are some obvious inconsistencies between the two theories in explaining the electron movement. The process of chemical reactions has been explained by the potential surface analysis based on the time-independent Schrodinger equation. However, this approach is not always appropriate for describing unsteady states, because the variable of reaction coordinate should be time t by its very nature. When considering the time-scale of chemical reactions for molecules, there is inherent uncertainty in the energy levels for the midway state of chemical reactions owing to the Heisenberg uncertainty principle. The states which can be accurately described by quantum mechanics exist discontinuously in chemical reactions. Such quantization of chemical reactions solves all noncrossing problems. We also show that such an essential fact leads to new concepts and theories in chemical reactions such as stable molecule, elementary reaction and minimum deformation of orbital phases. Finally, taking the regularity in organic reactions as an example, we demonstrate that FO theory and W–H theory can be unified consistently by the universally applicable "dynamic correlation diagram method. "