Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Less Generalizable Patterns for Better Test-Time Adaptation

Deep neural networks often fail to generalize outside of their training distribution, particularly when only a single data domain is available during training. While test-time adaptation has yielded encouraging results in this setting, we argue that to reach further improvements, these approaches should be combined with training procedure mod-iﬁcations aiming to learn a more diverse set of patterns. Indeed, test-time adaptation methods usually have to rely on a limited representation because of the shortcut learning phenomenon: only a subset of the available predictive patterns is learned with standard training. In this paper, we ﬁrst show that the combined use of existing training-time strategies and test-time batch normalization, a simple adaptation method, does not always improve upon the test-time adaptation alone on the PACS benchmark. Furthermore, experiments on Ofﬁce-Home show that very few training-time methods improve upon standard training, with or without test-time batch normalization. Therefore, we propose a novel approach that mitigates the shortcut learning behavior by having an additional classiﬁcation branch learn less predictive and generalizable patterns. Our experiments show that our method improves upon the state-of-the-art re-sults on both benchmarks and beneﬁts the most to test-time batch normalization.
On the internal stability and asymptotic behavior of 2-D positive systems

Two-dimensional (2-D) positive systems are 2-D state-space models whose variables take only nonnegative values and, hence, are described by a family of nonnegative matrices. The free state evolution of these systems is completely determined by the set of initial conditions and by the pair of nonnegative matrices, (A.B), that represent the shift operators along the coordinate axes. In this paper, internal stability of 2-D positive systems is analyzed and related to the spectral properties of the matrix sum A+B. Also, some aspects of the asymptotic behavior are considered, and conditions guaranteeing that all local states on the same separation set C/sub t/ assume the same direction as t goes to infinity, are provided. Finally, some results on the free evolution of positive systems corresponding to homogeneous distributions of the initial local states around a finite mean value, are presented.
Quantum mechanics of gauge fixing

Abstract In the framework of the canonical Weyl gauge formulation of QED, the quantum mechanics of gauge fixing is discussed. Redundant quantum mechanical variables are eliminated by means of unitary transformations and Gauss′s law. This results in representations of the Weyl-gauge Hamiltonian which contain only unconstrained variables. As a remnant of the original local gauge invariance global residual symmetries may persist. In order to identify these and to handle infrared problems and related "Gribov ambiguities," it is essential to compactify the configuration space. Coulomb, axial, and light-cone representation of QED are derived. The naive light-cone approach is put into perspective. Finally, the Abelian Higgs model is studied; the unitary gauge representation of this model is derived and implications concerning the symmetry of the Higgs phase are discussed.
The dynamic range of THz broadband FET detectors

Field effect transistors are promising detectors of THz radiation. They operate at room temperatures have high responsivity, low noise equivalent power, and fast response time. However, their linearity (dynamic range) and possibility of their application in the domain of high power radiation has not been yet sufficiently studied. We have investigated room temperature field effect transistors, detection at frequencies from 0.3 to 3 THz with power up to 100 kW/cm2. Several types of HEMTs and MOSFETs operating in the broadband non resonant detection regime, have been investigated. To provide a wide range of incident THz radiation intensities we used continuous-wave and pulsed sources: backward oscillators, CO2 pumped methanol laser, free electron laser, NH3, D2O, and CH3F lasers. We find that the photoresponse of HEMTs and MOSFETs is linear in radiation intensity up to a several kW/cm2 and then it saturates. The onset of the saturation depends on the radiation frequency and the transistor type. The observed saturation behavior can not be explained by the existing theoretical model which predict a square root like dependence of the photoresponse. We tentatively attribute the unusual features of the photoresponse saturation observed at high intensities considering high electric field transport phenomena, e.g., electron heating and electron velocity saturation.
A multi-wavelength study of the long-period AM Her system E2003+225 – II. Changes in the accretion geometry

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Self-Modularized Transformer: Learn to Modularize Networks for Systematic Generalization

: Visual Question Answering (VQA) is a task of answering questions about images that fundamentally requires systematic generalization capabilities, i.e., handling novel combinations of known visual attributes (e.g., color and shape) or visual sub-tasks (e.g., FILTER and COUNT ). Recent researches report that Neural Module Networks (NMNs), which compose modules that tackle sub-tasks with a given layout, are a promising approach for the systematic generalization in VQA. However, their performance heavily relies on the human-designed sub-tasks and their layout. Despite being crucial for training, most datasets do not contain these annotations. Self-Modularized Transformer (SMT), a novel Transformer-based NMN that concurrently learns to decompose the question into the sub-tasks and compose modules without such annotations, is proposed to overcome this important limitation of NMNs. SMT outperforms the state-of-the-art NMNs and multi-modal Transform-ers for the systematic generalization to the novel combinations of the sub-tasks in VQA.
Analysis of a Mathematical Model of the Growth of Necrotic Tumors

Abstract In this paper we study a model of necrotic tumor growth. The tumor comprises necrotic cells which occupy a radially symmetric core and life proliferating cells which occupy a radially symmetric shell adjacent to the core. The proliferating cells receive nutrients through diffusion from the outer boundary as well as by means of blood flow through a network of capillary vessels. The mathematical model describes the evolution of the nutrient concentration σ between the boundary of the necrotic core r = ρ( t ) and the outer boundary of the tumor r = R ( t ); within the core itself the concentration is a constant σ = σ nec , a level under which life cells cannot be sustained. Both of the surfaces r = ρ( t ) and r = R ( t ) are free boundaries, which are unknown in advance. Under some assumptions on the parameters, we prove that (i) there exists a stationary solution with radii r = ρ s , r = R s ; (ii) for any initial data near the stationary solution, the time dependent model has a unique solution σ( r , t ) with free boundaries r = ρ( t ), r = R ( t ); and (iii) ρ( t ) → ρ s and R ( t ) → R s as t → ∞.
Development, Implementation and Validation of a Stochastic Prediction Model of UICC Stages for Missing Values in Large Data Sets in a Hospital Cancer Registry

: Cancer is still a fatal disease in many cases, despite intensive research into prevention, treatment and follow-up. In this context, an important parameter is the stage of the cancer. The TNM/UICC classification is an important method to describe a cancer. It dates back to the surgeon Pierre Denoix and is an important prognostic factor for patient survival. Unfortunately, despite its importance, the TNM/UICC classification is often poorly documented in cancer registries. The aim of this work is to investigate the possibility of predicting UICC stages using statistical learning methods based on cancer registry data. Data from the Cancer Registry Clinic Arlesheim (CRCA) were used for this analysis. It contains a total of 5,305 records of which 1,539 cases were eligible for data analysis. For prediction classification and regression trees, random forests, gradient tree boosting and logistic regression are used as statistical methods for the problem at hand. As performance measures Mean misclassification error (mmce), area under the receiver operating curve (AUC) and Cohen’s kappa are applied. Misclassification rates were in the range of 28.0% to 30.4%. AUCs ranged between 0.73 and 0.80 and Cohen kappa showed values between 0.39 and 0.44 which only show a moderate predictive performance. However, with only 1,539 records, the data set considered here was significantly lower than those of larger cancer registries, so that the results found here should be interpreted with caution.
STABILITY ANALYSIS OF WHEAT THROUGH GENOTYPE BY ENVIRONMENT INTERACTION IN THREE REGIONS OF KHYBER PAKHTUNKHWA, PAKISTAN

Genotype-by-environment (GEI) interaction provides the basic information that helps breeders to select stable and superior genotypes for targeted environments. Climate change also alters the recommendations of the genotype for a specific condition. The field study carried out during the cropping seasons of 2015–2016 determined the performance of 16 wheat genotypes (13 lines and three cultivars) at three different locations: The University of Agriculture, Peshawar (UAP), Nuclear Institute of Food and Agriculture, Peshawar (NIFA), and Cereal Crops Research Institute (CCRI), Pirsabak-Nowshera, Khyber Pakhtunkhwa, Pakistan. Data on yield contributing parameters (days to heading, spike length, spikelets spike-1, grain weight spike-1, 1000-grain weight, and grain yield) were collected and analysed using analysis of variance (ANOVA) and stability analysis. Mean square values revealed highly significant results for all the traits under consideration for genotypes (G) and locations (L). Likewise, interactions (GEI) were highly significant for all the traits except grain weight spike-1. The performance of genotypes under different locations indicated no similarity. However, the overall performance of genotypes displayed better under environmental conditions of the Cereal Crops Research Institute (CCRI- Location No: 03). According to the Eberhart and Russell approach, the genotypes K-87, PR-113, Pak-13, PR-112, NR-449, and KT-338 were confirmed stable under tested locations for yield contributing traits as their regression coefficient (bi) value exhibited close to unity. Correlations of grain yield with yield contributing traits considered were significant and positive except for days to heading.
Direct numerical simulations of sedimenting spherical particles at non-zero Reynolds number

We performed direct numerical simulations, using a smoothed profile method to investigate the inertial effects on the static and dynamic properties of a sedimenting suspension over a wide range of volume fractions from 0.01 to 0.4. We found that at Reynolds number Re ≤ 0.5, static and dynamic properties show the typical non-Brownian, Stokes regime characteristics, due to insignificant inertial effects. The microstructure analysis at the high Re revealed that at Re = 1 inertial forces have significant effects and these create a deficiency of particles around a given particle, which is more pronounced in the direction of gravity than in the perpendicular direction. This deficiency decreased the velocity fluctuations and particle diffusion in the vertical direction, whereas both of these properties remain unchanged in the perpendicular direction. Moreover, at Re = 10, strong inertial forces generated a significant deficit of particles in both directions, which decreased velocity fluctuations and particle diffusion in both directions. We also observed that the range of volume fraction affected by inertial forces is increased with the increase of Re. At high volume fraction ϕ ≳ 0.15, intrinsic many-body interactions dominate the phenomena and govern the transport properties thereafter.
Effect of Interaction Strength on Unsteadiness in Shock-Wave-Induced Separations

The effect of the interaction strength on the unsteady behavior of a planar shock wave impinging on a low Reynolds turbulent boundary layer is investigated. This is achieved by means of a variation in incident shock angle under otherwise constant flow conditions. In addition, the effect of an order-of-magnitude variation in the Reynolds number is considered. This has been done for equivalent interaction strength, based on a similar probability of occurrence of instantaneous flow separations. The measurement technique employed is two-component planar particle image velocimetry. Common mechanisms for the large-scale reflected-shock unsteadiness are deduced by means of conditional statistics based on the separation bubble height. The results indicate that both upstream and downstream mechanisms are at work, the dominant mechanism depending on the interaction strength. No significant dependence on the Reynolds number was observed for interactions with a similar probability of instantaneous flow separations.
Electromagnetic wave technique to determine radiation torque on micromachines driven by light

A method to determine radiation torque on a dielectric microrotor driven by a focused laser beam, while considering the wave nature of the incident light, is introduced. The method involves numerically determining the electric and magnetic field quantities of the scattered light on the surface of the rotor using the finite difference time domain method. Maxwell’s stress tensor is then employed to calculate the force over the rotor surface, enabling the determination of the rotor torque about the axis of rotation. The method is demonstrated using a simple rotor shape, with results that are in good qualitative agreement with observations reported in literature.
Shape Morphing as a Minimal Path in the Graph of Cubified Shapes

: The systematic study of morphings for non parametric shapes suffers from ambiguities in defining good general morphings, such as the trade-off between plausibility and smoothness, above all under large topology changes. In the recent years, only neural networks have offered a generic solution, using their latent space as a shape prior. But these models are optimized for single shape reconstruction, giving little control on the generated morphings. In this paper, we show how qualitatively similar results can be achieved when replacing neural networks with a set of carefully crafted components: a style-content separation method via the fitting of a Deformable Voxel Grid, a similarity metric adapted to the extracted content, and a formulation of morphings as minimal paths in a graph. While forgoing the automatic learning of a generative model, we still achieve similar morphing capabilities. We performed various evaluations, quantitative analysis on the robustness of our proposed method and on the quality of the results, and demonstrate the usefulness of each component. Finally, we provide guidance on how manual intervention can improve quality. This is indeed possible since, unlike neural networks, each component in our method is interpretable.
Two‐dimensional studies of streamers in gases

We present the results of two‐dimensional computer simulations of streamer initiation and propagation in atmospheric pressure N2. The simulation algorithm makes use of flux‐corrected transport techniques and was used as a tool to study the solutions of the transport equations under conditions suitable for streamers, for which realistic analytic solutions are not known. We present and discuss conclusions about streamer transport based on the results of these studies. Finally, we present a novel method of checking on the numerical accuracy with which the algorithm solves the transport equations.
On the Analysis of a Cloud Seeding Dataset over Tasmania

An analysis of cloud seeding activity for the period 1960‐2005 over a hydroelectric catchment (target) area located in central Tasmania, Australia, is presented. The analysis is performed using a double ratio on monthly area-averaged rainfall for the months of May‐October. Results indicate that increases in monthly precipitation are observed within the target area relative to nearby controls during periods of cloud seeding activity. Ten independent tests were performed and all double ratios found are above unity with values that range from 5% to 14%. Nine out of 10 confidence intervals are entirely above unity and overlap in the range of 6%‐11%. Nine tests obtain levels of significance . 0.05 level. If the Bonferroni adjustment is made to account for multiple comparisons, six tests are found to be significant at the adjusted alpha level. Furtherfield measurements of the cloud microphysics over this region are needed to provide a physical basis for these statistical results.
Predicting Hospital Length of Stay of Patients Leaving the Emergency Department

: In this paper, we aim to predict the patient’s length of stay (LOS) after they are dismissed from the emergency department and transferred to the next hospital unit. An accurate prediction has positive effects for patients, doctors and hospital administrators. We extract a dataset of 181,797 patients from the United States and perform a set of feature engineering steps. For the prediction we use a CatBoost regression architecture with a specifically implemented loss function. The results are compared with baseline models and results from related work on other use cases. With an average absolute error of 2.36 days in the newly defined use case of post ED LOS prediction, we outperform baseline models achieve comparable results to use cases from intensive care unit LOS prediction. The approach can be used as a new baseline for further improvements of the prediction.
When Simple Statistical Algorithms Outperform Deep Learning: A Case of Keystroke Dynamics

: Keystroke dynamics has gained relevance over the years for its potential in solving practical problems like online fraud and account takeovers. Statistical algorithms such as distance measures have long been a common choice for keystroke authentication due to their simplicity and ease of implementation. However, deep learning has recently started to gain popularity due to their ability to achieve better performance. When should statistical algorithms be preferred over deep learning and vice-versa? To answer this question, we set up experiments to evaluate two state-of-the-art statistical algorithms: Scaled Manhattan and the Instance-based Tail Area Density (ITAD) metric, with a state-of-the-art deep learning model called TypeNet, on three datasets (one small and two large). Our results show that on the small dataset, statistical algorithms significantly outperform the deep learning approach (Equal Error Rate (EER) of 4.3% for Scaled Manhattan / 1.3% for ITAD versus 19.18% for TypeNet). However, on the two large datasets, the deep learning approach performs better (22.9% & 28.07% for Scaled Manhattan / 12.25% & 20.74% for ITAD versus 0.93% & 6.77% for TypeNet).
Understanding hunter support for early successional habitat management

ABSTRACT Early successional habitat (ESH), which is important to many game species, is declining throughout the Northeast United States. In New York State, management agencies are increasing efforts to manage for ESH, but such efforts require support from key stakeholders. We sought to understand the factors influencing such support among hunting license holders, and a subsample specializing in woodcock hunting, who use Wildlife Management Areas. Using structural equation modeling, we found significant direct and indirect relationships between type of hunter, hunting avidity, place attachment, and support for ESHM actions. Our findings suggest that to build support for habitat management, communications should focus on the benefits to the place, and emphasize management actions for wildlife species hunters are pursuing. Increased habitat from this type of management should lead to larger populations of game species, which could increase hunter satisfaction and perhaps increase the number of hunters interested in hunting for these species.
Pyramid Swin Transformer: Different-Size Windows Swin Transformer for Image Classification and Object Detection

: We present the Pyramid Swin Transformer for object detection and image classification, by taking advantage of more shift window operations, smaller and more different size windows. We also add a Feature Pyramid Network for object detection, which produces excellent results. This architecture is implemented in four stages, containing different size window layers. We test our architecture on ImageNet classification and COCO detection. Pyramid Swin Transformer achieves 85.4% accuracy on ImageNet classification and 54.3 box AP on COCO.
Application of Accelerometer Data to Mars Odyssey Aerobraking and Atmospheric Modeling

Aerobraking was an enabling technology for the Mars Odyssey mission, even though it involved risk due primarily to the variability of the Mars upper atmosphere. To reduce the risk, numerous analyses, based on various data types, were performed during operations. The use of one such data type, measurements from spacecrafts accelerometers, for determining atmospheric density during Odyssey aerobraking operations is reported. Accelerometer data were analyzed in near real time to provide estimates of density at periapsis, maximum density, density scale height, latitudinal gradient, longitudinal wave variations, and location of the polar vortex. Summaries of the aerobraking phase of the mission, the accelerometer data analysis methods and operational procedures, applications to determining thermospheric properties, and several remaining issues on interpretation of the data are discussed. Although acceleration was measured along three orthogonal axes, only data from the component along the axis nominally into the flow were used during operations. For a 1-s count time, the rms noise level, derived from the acceleration, varied from 0.07 to 0.5 mm/s 2 , permitting density recovery to between 0.15 and 1.1 kg/km 3 , or about 2% of the mean density at periapsis during aerobraking. Preflight estimates of natural variability based on Mars Global Surveyor accelerometer measurements proved reliable in the midlatitudes but overestimated the variability inside the polar vortex.
The Implied Equity Premium

I propose and test a simple model of the equity premium implied by the prices of options on the stock market. The model assumes that markets for the stock index and its options are frictionless and complete to extract as much information as possible from their prices. Its forecasts of the equity premium are more accurate than those in prior work, especially when arbitrage costs are low. It offers new economic insights into why the premium varies, including why it increased for many years after the 2008 crisis. The model also provides a unified explanation of risk premiums for variance and higher-order moments of market returns. Comments welcome. I thank Jack McCoy and Matt Massicotte for outstanding research assistance. I thank Kent Daniel, Lars Lochstoer, Ian Martin, and Tano Santos for feedback. First draft: January 2023. All errors are my own. Author email: paul.tetlock@columbia.edu.