Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of AGN-driven winds on physical and observable galaxy sizes

Without AGN feedback, simulated massive, star-forming galaxies become too compact relative to observed galaxies at z ≲ 2. In this paper, we perform high-resolution re-simulations of a massive ($M_{\star }\sim 10^{11}\, \rm {{\rm M}_{\odot }}$) galaxy at z ∼ 2.3, drawn from the Feedback in Realistic Environments (FIRE) project. In the simulation without AGN feedback, the galaxy experiences a rapid starburst and shrinking of its half-mass radius. We experiment with driving mechanical AGN winds, using a state-of-the-art hyper-Lagrangian refinement technique to increase particle resolution. These winds reduce the gas surface density in the inner regions of the galaxy, suppressing the compact starburst and maintaining an approximately constant half-mass radius. Using radiative transfer, we study the impact of AGN feedback on the magnitude and extent of the multi-wavelength continuum emission. When AGN winds are included, the suppression of the compact, dusty starburst results in lowered flux at FIR wavelengths (due to decreased star formation) but increased flux at optical-to-near-IR wavelengths (due to decreased dust attenuation, in spite of the lowered star formation rate), relative to the case without AGN winds. The FIR half-light radius decreases from $\sim 1\, \rm {kpc}$ to $\sim 0.1\, \rm {kpc}$ in $\lesssim 40\, \rm {Myr}$ when AGN winds are not included, but increases to $\sim 2\, \rm {kpc}$ when they are. Interestingly, the half-light radius at optical-NIR wavelengths remains approximately constant over $35\, \rm {Myr}$, for simulations with and without AGN winds. In the case without winds, this occurs despite the rapid compaction, and is due to heavy dust obscuration in the inner regions of the galaxy. This work highlights the importance of forward-modelling when comparing simulated and observed galaxy populations.
Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective

Endeavors have been recently made to leverage the vision transformer (ViT) for the challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross-attention in ViT for direct domain alignment. However, as the performance of cross-attention highly relies on the quality of pseudo labels for targeted samples, it becomes less effective when the domain gap becomes large. We solve this problem from a game theory's perspective with the proposed model dubbed as PMTrans, which bridges source and target domains with an intermediate domain. Specifically, we propose a novel ViT-based module called PatchMix that effectively builds up the intermediate domain, i.e., probability distribution, by learning to sample patches from both domains based on the game-theoretical models. This way, it learns to mix the patches from the source and target domains to maximize the cross entropy (CE), while exploiting two semi-supervised mixup losses in the feature and label spaces to minimize it. As such, we interpret the process of UDA as a min-max CE game with three players, including the feature extractor, classifier, and PatchMix, to find the Nash Equilibria. Moreover, we leverage attention maps from ViT to re-weight the label of each patch by its importance, making it possible to obtain more domain-discriminative feature representations. We conduct extensive experiments on four benchmark datasets, and the results show that PMTrans significantly surpasses the ViT-based and CNN-based SoTA methods by +3.6% on Office-Home, +1.4% on Office-31, and +17.7% on DomainNet, respectively. https://vlis2022.github. io/cvpr23/PMTrans
Semantic Image Attack for Visual Model Diagnosis

In practice, metric analysis on a specific train and test dataset does not guarantee reliable or fair ML models. This is partially due to the fact that obtaining a balanced, diverse, and perfectly labeled dataset is typically expensive, time-consuming, and error-prone. Rather than relying on a carefully designed test set to assess ML models' failures, fairness, or robustness, this paper proposes Semantic Image Attack (SIA), a method based on the adversarial attack that provides semantic adversarial images to allow model diagnosis, interpretability, and robustness. Traditional adversarial training is a popular methodology for robustifying ML models against attacks. However, existing adversarial methods do not combine the two aspects that enable the interpretation and analysis of the model's flaws: semantic traceability and perceptual quality. SIA combines the two features via iterative gradient ascent on a predefined semantic attribute space and the image space. We illustrate the validity of our approach in three scenarios for keypoint detection and classification. (1) Model diagnosis: SIA generates a histogram of attributes that highlights the semantic vulnerability of the ML model (i.e., attributes that make the model fail). (2) Stronger attacks: SIA generates adversarial examples with visually interpretable attributes that lead to higher attack success rates than baseline methods. The adversarial training on SIA improves the transferable robustness across different gradient-based attacks. (3) Robustness to imbalanced datasets: we use SIA to augment the underrepresented classes, which outperforms strong augmentation and re-balancing baselines.
RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion

This work presents a novel RGB-D-inertial dynamic SLAM method that can enable accurate localisation when the majority of the camera view is occluded by multiple dynamic objects over a long period of time. Most dynamic SLAM approaches either remove dynamic objects as outliers when they account for a minor proportion of the visual input, or detect dynamic objects using semantic segmentation before camera tracking. Therefore, dynamic objects that cause large occlusions are difficult to detect without prior information. The remaining visual information from the static background is also not enough to support localisation when large occlusion lasts for a long period. To overcome these problems, our framework presents a robust visual-inertial bundle adjustment that simultaneously tracks camera, estimates cluster-wise dense segmentation of dynamic objects and maintains a static sparse map by combining dense and sparse features. The experiment results demonstrate that our method achieves promising localisation and object segmentation performance compared to other state-of-the-art methods in the scenario of long-term large occlusion.
Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control

Algorithm aversion occurs when humans are reluctant to use algorithms despite their superior performance. Studies show that giving users outcome control by providing agency over how models’ predictions are incorporated into decision-making mitigates algorithm aversion. We study whether algorithm aversion is mitigated by process control, wherein users can decide what input factors and algorithms to use in model training. We conduct a replication study of outcome control, and test novel process control study conditions on Amazon Mechanical Turk (MTurk) and Prolific. Our results partly confirm prior findings on the mitigating effects of outcome control, while also forefronting reproducibility challenges. We find that process control in the form of choosing the training algorithm mitigates algorithm aversion, but changing inputs does not. Furthermore, giving users both outcome and process control does not reduce algorithm aversion more than outcome or process control alone. This study contributes to design considerations around mitigating algorithm aversion.
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition

In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal Transformers (SPARTAN) approach to Group Activity Recognition (GAR) using unlabeled video data. Given a video, we create local and global Spatio-temporal views with varying spatial patch sizes and frame rates. The proposed self-supervised objective aims to match the features of these contrasting views representing the same video to be consistent with the variations in spatiotemporal domains. To the best of our knowledge, the proposed mechanism is one of the first works to alleviate the weakly supervised setting of GAR using the encoders in video transformers. Furthermore, using the advantage of transformer models, our proposed approach supports long-term relationship modeling along spatio-temporal dimensions. The proposed SPARTAN approach performs well on two group activity recognition benchmarks, including NBA and Volleyball datasets, by surpassing the state-of-the-art results by a significant margin in terms of MCA and MPCA metrics1.
Mock data study for next-generation ground-based detectors: The performance loss of matched filtering due to correlated confusion noise

The next-generation (3G/XG) ground-based gravitational-wave (GW) detectors such as Einstein Telescope (ET) and Cosmic Explorer (CE) will begin observing in the next decade. Due to the extremely high sensitivity of these detectors, the majority of stellar-mass compact-binary mergers in the entire Universe will be observed. It is also expected that 3G detectors will have significant sensitivity down to 2-7 Hz; the observed duration of binary neutron star signals could increase to several hours or days. The abundance and duration of signals will cause them to overlap in time, which may form a confusion noise that could affect the detection of individual GW sources when using naive matched filtering; matched filtering is only optimal for stationary Gaussian noise. We create mock data for CE and ET using the latest population models informed by the GWTC-3 catalog and investigate the performance loss of matched filtering due to overlapping signals. We find the performance loss mainly comes from a deviation in the noise's measured amplitude spectral density. The redshift reach of CE (ET) can be reduced by 15%-38% (8%-21%) depending on the merger rate estimate. The direct contribution of confusion noise to the total signal-to-noise ratio (SNR) is generally negligible compared to the contribution from instrumental noise. We also find that correlated confusion noise has a negligible effect on the quadrature summation rule of network SNR for ET, but might reduce the network SNR of high detector-frame mass signals for detector networks including CE if no mitigation is applied. For ET, the null stream can mitigate the astrophysical foreground. For CE, we demonstrate that a computationally efficient, straightforward single-detector signal subtraction method suppresses the total noise to almost the instrument noise level; this will allow for near-optimal searches.
Computational model advance deep brain stimulation for Parkinson's disease

Deep brain stimulation(DBS)has become an effective intervention for advanced Parkinson's disease, but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia(BG), the abnormal pathological changes of the BG in Parkinson's disease, and how computational models can help understand and advance DBS. We also describe two types of models:mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic principle underlying DBS, while clinical predictive models focus more on patients' outcomes, helping to adapt treatment plans for each patient and advance novel electrode designs. Finally, we provide insights and an outlook on future technologies.
GP-Net: Flexible Viewpoint Grasp Proposal

We present the Grasp Proposal Network (GP-net), a Convolutional Neural Network model which can generate 6-DoF grasps from flexible viewpoints, e.g. as experienced by mobile manipulators. To train GP-net, we synthetically generate a dataset containing depth-images and ground-truth grasp information. In real-world experiments, we use the EGAD evaluation benchmark to evaluate GP-net against two commonly used algorithms, the Volumetric Grasping Network (VGN) and the Grasp Pose Detection package (GPD), on a PAL TIAGo mobile manipulator. In contrast to the state-of-the-art methods in robotic grasping, GP-net can be used for grasping objects from flexible, unknown viewpoints without the need to define the workspace and achieves a grasp success of 54.4% compared to 51.6% for VGN and 44.2% for GPD. We provide a ROS package along with our code and pre-trained models at https://aucoroboticsmu.github. io/GP-net/.
Decentralization illusion in Decentralized Finance: Evidence from tokenized voting in MakerDAO polls

Decentralized Autonomous Organization (DAO) is very popular in Decentralized Finance (DeFi) applications as it provides a decentralized governance solution through blockchain. We analyze the governance characteristics in the Maker protocol, its stablecoin DAI and governance token Maker (MKR). To achieve that, we establish several measurements of centralized governance. Our empirical analysis investigates the effect of centralized governance over a series of factors related to MKR and DAI, such as financial, transaction, network and twitter sentiment indicators. Our results show that governance centralization influences both the Maker protocol, and the distribution of voting power matters. The main implication of this study is that centralized governance in MakerDAO very much exists, while DeFi investors face a trade-off between decentralization and performance of a DeFi protocol. This further contributes to the contemporary debate on whether DeFi can be truly decentralized. centralized governance in MakerDAO very much exists, while DeFi investors face a trade-off between efficiency and decentralization. This further contributes to the contemporary debate on whether DeFi can be truly decentralized.
Observer design for particle size distribution in emulsion polymerization

Monitoring of the particle size distribution (PSD) in emulsion polymerization is a very important task to improve the product quality. However, on-line sensors used to measure PSD, such as capillary hydrodynamic fractionation, usually require taking samples and the analysis time is still too long for control. This work proposes the combination of mathematical models with existing on-line sensors to obtain a continuous estimation of the PSD by constructing nonlinear estimators. Simulations of the observers show that they are able to detect particle nucleation and to give an estimation of the whole PSD. © 2005 American Institute of Chemical Engineers AIChE J, 2005
Ultralow resistance in situ Ohmic contacts to InGaAs/InP

We report a sharp reduction in the resistivity of Ohmic contacts using in situ deposition of molybdenum (Mo) contacts onto n-type In0.53Ga0.47As grown on InP. The contacts were formed by evaporating Mo onto the wafer using an electron beam evaporator connected to a molecular beam epitaxy chamber under ultrahigh vacuum. Transmission line measurements showed specific contact resistivities of 0.5±0.3 Ω μm2 (2.90 Ω μm), 0.9±0.4 Ω μm2 (4.3 Ω μm), and 1.3±0.4 Ω μm2 (4.7 Ω μm) for Mo on abrupt InAs/InGaAs heterojunctions, graded InAs/InGaAs, and InGaAs films, respectively. These low resistances meet the requirements for terahertz transistors.
Unavoidable systems of functions

Suppose that D is a plane domain and that f(z), g(z) are meromorphic in D and f(z) ╪ g(z) for all z in D. Then following Rubel and Yang [11], we say that f(z) avoids g(z) in D. A system of functions g1(z), …, gn(z) is said to be unavoidable if, whenever f is meromorphic in D, at least one of the equations f(z) = gv(z) has a root in D. Rubel and Yang [11] proved that if D is the open plane, then any two functions form an avoidable system, but three distinct polynomials a1, a2, a3 such that a1−a2 and a2−a3 are not both constant form an unavoidable system.
Horizontal energy propagation in a barotropic atmosphere with meridional and zonal structure

Abstract To investigate how the propagation of energy away from steady sources may be influenced by the horizontal structure of the time-averaged flow in the troposphere, solutions to a barotropic model are displayed and interpreted. The model is steady and linearized about a basic state which varies in latitude and longitude. Emphasis is placed on cases where the longitudinal variations are gradual. Many of the results can be analyzed by locally applying tools developed for zonally symmetric basic states, e.g., ray tracing and refractive indices. Simple background flows are constructed which have longitudinally confined critical lines, reflective surfaces, wave guides, and regions conducive to propagation. An example is given in which longitudinal derivatives of the basic state are significant. The theory of ray tracing in a two-dimensional wind must be employed as an interpretive tool in this case. It is concluded that zonal variations tend to enhance poleward (equatorward) propagation in large-scale tr.
Across‐frequency timing in speech perception: Effective processing of contrasting temporal speech patterns requires proper synchronous alignment

Although narrow‐band temporal speech patterns produce virtually no intelligibility when presented individually, a contrasting pair of such patterns can produce appreciable intelligibility [E. W. Healy, Dissertation, University of Wisconsin–Milwaukee (1998)]. In the current study, the role of across‐frequency timing in speech perception was investigated using these contrasting temporal speech patterns which allow the strict isolation of across‐frequency integration. Across‐band timing was investigated by varying the synchrony of paired temporal patterns. Individual temporal patterns were created by amplitude modulating tones at 750 and 3000 Hz to follow the temporal fluctuations of corresponding 1/3‐octave sentences. The pair of individually unintelligible narrow‐band temporal patterns were mixed and presented to listeners either in temporal synchrony, or with offsets of 12.5, 25, 50, or 100 ms. High intelligibility scores in the aligned condition were reduced when as little as 12.5 ms of misalignment was .
Nonlinear dynamics in directly modulated multiple-quantum-well laser diodes

A theoretical and experimental analysis of the nonlinear dynamics of Fabry-Perot (FP) and distributed feedback (DFB) multiple-quantum-well (MQW) laser diodes is presented. The analysis is performed under single-tone and two-tone direct modulation. In the FP laser, we observe period doubling and in the DFB laser both period doubling and period tripling are identified. Period doubling is found over a wide range of modulation frequencies in both lasers. The reason for this wide modulation frequency range is attributed to the large relaxation frequencies found in MQW laser diodes. The spontaneous emission factor is measured for both FP and DFB lasers. The dependencies of period doubling on output power and RF input power level are also analyzed. The nonlinear dynamics of the laser are found to be enhanced when modulated under two-tone modulation. Numerical simulations carried out show good agreement with the measured results.
Charged particle periodicity in the Saturnian magnetosphere

The Low Energy Charged Particle (LECP) experiments on the Voyager 1 and 2 spacecraft performed measurements of electrons (∼22 keV to ∼20 MeV) and ions (∼28 keV to ∼150 MeV) during the Saturn encounters in 1980 and 1981. Count rate ratios of two of the low energy electron (22 to 35 keV and 183 to 500 keV) and ion (43 to 80 keV and 137 to 215 keV) channels exhibit an approximate 10 hour periodicity in the outer Saturnian magnetosphere beyond the orbit of Titan. Electron ratios vary from ∼50 to ∼300; ion ratios vary from ∼3 to ∼20. Similar but less pronounced periodicities are observed for higher and lower energy electron and ion spectral indices. Three complete cycles were observed during the Voyager 2 outbound portion of the encounter from which were determined an electron ratio period of 10h21m±48m and an ion ratio period of 9h49m±59m. Using Saturn Kilometric Radiation (SKR) and Saturn Electrostatic Discharge (SED) periods, extrapolation backward from Voyager 2 to Voyager 1 suggests that the periodicities are Saturnian rather than Jovian in nature, and that they persist in phase for time intervals at least as long as 287 days. Ratio minima, or spectral hardenings, occur in the same hemisphere as do auroral brightenings, SKR activity, and spoke enhancement. We interpret the observations as prima facie evidence of an asymmetry in the Saturnian magnetic field and the root cause of the observed SKR periodicity.
Capacitively induced high mobility two-dimensional electron gas in undoped Si∕Si1−xGex heterostructures with atomic-layer-deposited dielectric

The authors demonstrate that a high mobility two-dimensional electron gas can be capacitively induced in an undoped Si∕Si1−xGex heterostructure using atomic-layer-deposited Al2O3 as the dielectric. The density is tuned up to 4.2×1011∕cm2, limited by the gate leakage current. The mobility increases with the density rapidly and reaches 5.5×104cm2∕Vs at the highest density. The observation of well developed quantum Hall states and two-dimensional metal-insulator transition shows that the devices are suitable for two-dimensional electron physics studies.
The strong nonlinear interaction of Tollmien–Schlichting waves and Taylor–Görtler vortices in curved channel flow

Viscous fluid flows with curved streamlines can support both centrifugal and viscous travelling wave instabilities. Here the interaction of these instabilities in the context of the fully developed flow in a curved channel is discussed. The viscous (Tollmien–Schlichting) instability is described asymptotically at high Reynolds numbers and it is found that it can induce a Taylor–Görtler flow even at extremely small amplitudes. In this interaction, the Tollmien–Schlichting wave can drive a vortex state with wavelength either comparable with the channel width or the wavelength of lower-branch viscous modes. The nonlinear equations which describe these interactions are solved for nonlinear equilibrium states.
MAGNETOHYDRODYNAMIC POISEUILLE-COUETTE FLOW AND HEAT TRANSFER IN AN INCLINED CHANNEL

An analysis of the Poiseuille-Couette flow of two immiscible fluids between inclined parallel plates is investigated. One of the fluids is assumed to be electrically conducting while the other fluid and channel walls are assumed to be electrically insulating. The viscous and Ohmic dissipation terms are taken into account in the energy equation. The coupled nonlinear equations are solved both analytically valid for small values of the product of Prandtl number and Eckert number (= e) and numerically valid for all e. Solutions for large e reveal a marked change on the flow and rate of heat transfer. The effects of various parameters such as Hartmann number, Grashof number, angle of inclination, ratios of viscosities, widths and thermal conductivities are presented and discussed in detail.
Solar Flare Activity and Variability of Electric Current Helicity

Recent study using Huairou vector magnetograph data shows that during flare activity, the current helicity exhibits rapid and substantial variations and, in some cases, a recovering phase. We considered various representative the magnetic configurations. First, interacting twisted magnetic flux tubes are analyzed separately for positive, negative and mixed-sign helicity regions. The results show that the helicity during flares decreases in positive-sign, and increases in negative-sign regions. Then, flaring arcade also shows that the magnitude of the helicity decreases after flares. We also investigated current sheets formed by sheared magnetic field and showed that the current helicity (with either positive and negative signs) vanishes after reconnection. The emergence of twisted flux tubes which can trigger flares may be another source of flare-associated variability of current helicity. We demonstrate the relevance of current helicity to the description of flare activity by comparing its variation with that of shear angle in the active region AR 6891.