Miracle-dz/UniFAR-FTUs huggingface dataset:
Target Domain Data induces Negative Transfer in Mixed Domain Training with Disjoint Classes

In practical scenarios, it is often the case that the available training data within the target domain only exist for a limited number of classes, with the remaining classes only available within surrogate domains. We show that including the target domain in training when there exist disjoint classes between the target and surrogate domains creates significant negative transfer, and causes performance to significantly decrease compared to training without the target domain at all. We hypothesize that this negative transfer is due to an intermediate shortcut that only occurs when multiple source domains are present, and provide experimental evidence that this may be the case. We show that this phenomena occurs on over 25 distinct domain shifts, both synthetic and real, and in many cases deteriorates the performance to well worse than random, even when using state-of-the-art domain adaptation methods.
Language Variety Identification with True Labels

Language identification is an important first step in many IR and NLP applications. Most publicly available language identification datasets, however, are compiled under the assumption that the gold label of each instance is determined by where texts are retrieved from. Research has shown that this is a problematic assumption, particularly in the case of very similar languages (e.g., Croatian and Serbian) and national language varieties (e.g., Brazilian and European Portuguese), where texts may contain no distinctive marker of the particular language or variety. To overcome this important limitation, this paper presents DSL True Labels (DSL-TL), the first human-annotated multilingual dataset for language variety identification. DSL-TL contains a total of 12,900 instances in Portuguese, split between European Portuguese and Brazilian Portuguese; Spanish, split between Argentine Spanish and Castilian Spanish; and English, split between American English and British English. We trained multiple models to discriminate between these language varieties, and we present the results in detail. The data and models presented in this paper provide a reliable benchmark toward the development of robust and fairer language variety identification systems. We make DSL-TL freely available to the research community.
Development of special corrective processing of HIRDLS data and early validation

The High Resolution Dynamics Limb Sounder (HIRDLS) instrument was launched on NASA's Aura spacecraft on 15 July 2004. When activation was completed 25 days later, it was discovered that the measured radiances were very different from those that were expected. After a long series of analyses and diagnostic tests, the cause was confirmed to be a blockage that covers much of the front aperture, preventing even one completely clear view of the atmosphere. In this paper the steps required to correct the radiances for the effects of the blockage are noted. These are calibrating the radiances, removing the effects of the blockage oscillating, and the radiance coming from the blockage, correcting for the effects of the partial aperture, and filtering the noise. The paper describes the algorithms needed, and presents the results of their application. The success of the procedures will be demonstrated by the quality of the resulting radiances and retrieved profiles of temperature and trace species. The difficulties that have been eliminated, and that still remain are noted, along with plans for further improvement. Finally, the scientific implications are briefly discussed.
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Biomedical data is inherently multimodal, comprising physical measurements and natural language narratives. A generalist biomedical AI model needs to simultaneously process different modalities of data, including text and images. Therefore, training an effective generalist biomedical model requires high-quality multimodal data, such as parallel image-text pairs. Here, we present PMC-15M, a novel dataset that is two orders of magnitude larger than existing biomedical multimodal datasets such as MIMIC-CXR, and spans a diverse range of biomedical image types. PMC-15M contains 15 million biomedical image-text pairs collected from 4.4 million scientific articles. Based on PMC-15M, we have pretrained BiomedCLIP, a multimodal foundation model, with domain-specific adaptations tailored to biomedical vision-language processing. We conducted extensive experiments and ablation studies on standard biomedical imaging tasks from retrieval to classification to visual question-answering (VQA). BiomedCLIP achieved new state-of-the-art results in a wide range of standard datasets, substantially outperforming prior approaches. Intriguingly, by large-scale pretraining on diverse biomedical image types, BiomedCLIP even outperforms state-of-the-art radiology-specific models such as BioViL in radiology-specific tasks such as RSNA pneumonia detection. In summary, BiomedCLIP is a fully open-access foundation model that achieves state-of-the-art performance on various biomedical tasks, paving the way for transformative multimodal biomedical discovery and applications. We release our models at https://aka.ms/biomedclip to facilitate future research in multimodal biomedical AI.
Response of boron carbide subjected to large strains, high strain rates, and high pressures

This article presents an analysis of the response of boron carbide (B4C) to severe loading conditions that produce large strains, high strain rates, and high pressures. Experimental data from the literature are used to determine and/or estimate constants for the JH-2 constitutive model for brittle materials. Because B4C is a very strong material, it is not always possible to determine the constants explicitly. Instead they must sometimes be inferred from the limited experimental data that are available. The process of determining constants provides insight into the constitutive behavior for some loading conditions, but it also raises questions regarding the response under other loading conditions. Several Lagrangian finite element and Eulerian finite difference computations are provided to illustrate responses for a variety of impact and penetration problems.
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning

Unsupervised meta-learning aims to learn generalizable knowledge across a distribution of tasks constructed from unlabeled data. Here, the main challenge is how to construct diverse tasks for meta-learning without label information; recent works have proposed to create, e.g., pseudo-labeling via pretrained representations or creating synthetic samples via generative models. However, such a task construction strategy is fundamentally limited due to heavy reliance on the immutable pseudo-labels during meta-learning and the quality of the representations or the generated samples. To overcome the limitations, we propose a simple yet effective unsupervised meta-learning framework, coined Pseudo-supervised Contrast (PsCo), for few-shot classification. We are inspired by the recent self-supervised learning literature; PsCo utilizes a momentum network and a queue of previous batches to improve pseudo-labeling and construct diverse tasks in a progressive manner. Our extensive experiments demonstrate that PsCo outperforms existing unsupervised meta-learning methods under various in-domain and cross-domain few-shot classification benchmarks. We also validate that PsCo is easily scalable to a large-scale benchmark, while recent prior-art meta-schemes are not.
Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression

Abdominal aortic aneurysms (AAAs) are progressive dilatations of the abdominal aorta that, if left untreated, can rupture with lethal consequences. Imaging-based patient monitoring is required to select patients eligible for surgical repair. In this work, we present a model based on implicit neural representations (INRs) to model AAA progression. We represent the AAA wall over time as the zero-level set of a signed distance function (SDF), estimated by a multilayer perception that operates on space and time. We optimize this INR using automatically extracted segmentation masks in longitudinal CT data. This network is conditioned on spatiotemporal coordinates and represents the AAA surface at any desired resolution at any moment in time. Using regularization on spatial and temporal gradients of the SDF, we ensure proper interpolation of the AAA shape. We demonstrate the network's ability to produce AAA interpolations with average surface distances ranging between 0.72 and 2.52 mm from images acquired at highly irregular intervals. The results indicate that our model can accurately interpolate AAA shapes over time, with potential clinical value for a more personalised assessment of AAA progression.
Construction of real algebraic functions with prescribed preimages

We present real algebraic functions with prescribed preimages. Smooth closed manifolds are, according to Nash's celebrating theory, regarded as the zero sets of some real polynomials and smooth. Moreover, canonical projections of spheres naturally embedded in the $1$-dimensional higher Euclidean spaces (affine spaces) and some natural functions on projective spaces, Lie groups and their quotient spaces are important examples of real algebraic funtions. However, in general, it is very important to construct explicit examples. In addition, in considerable cases, the structures of the functions and maps are hard to understand. We construct examples by answering to Sharko's problems and author's suitably revised ones in improved styles. They ask whether we can find nice smooth functions with prescribed preimages. We have previously given an answer with real algebraic functions and this result is one of key ingredients.
An introduction to group sequential methods: planning and multi-aspect optimization

A group sequential clinical trial design can be an attractive option when planning a pivotal trial as this approach has the ability to stop the trial early for success, whilst also being well accepted from a regulatory review perspective. Compared to a single stage design there are more moving parts to consider and optimise when planning a group sequential trial. This tutorial briefly outlines the group sequential methodology before detailing some of the key operating characteristics and how these can be estimated, optimised and ultimately presented to decision makers when aligning on a final study design.
Dropout Reduces Underfitting

Introduced by Hinton et al. in 2012, dropout has stood the test of time as a regularizer for preventing overfitting in neural networks. In this study, we demonstrate that dropout can also mitigate underfitting when used at the start of training. During the early phase, we find dropout reduces the directional variance of gradients across mini-batches and helps align the mini-batch gradients with the entire dataset's gradient. This helps counteract the stochasticity of SGD and limit the influence of individual batches on model training. Our findings lead us to a solution for improving performance in underfitting models - early dropout: dropout is applied only during the initial phases of training, and turned off afterwards. Models equipped with early dropout achieve lower final training loss compared to their counterparts without dropout. Additionally, we explore a symmetric technique for regularizing overfitting models - late dropout, where dropout is not used in the early iterations and is only activated later in training. Experiments on ImageNet and various vision tasks demonstrate that our methods consistently improve generalization accuracy. Our results encourage more research on understanding regularization in deep learning and our methods can be useful tools for future neural network training, especially in the era of large data. Code is available at https://github.com/facebookresearch/dropout.
Representing Tate cohomology of G-spaces

Tate cohomology of finite groups [5] is very good at emphasising periodic cohomological behaviour and hence at the study of free actions on spheres [8]. Tate cohomology of spaces was introduced by Swan [10] for finite dimensional spaces to systematically ignore free actions, and hence to simplify various arguments in fixed point theory.
Fluxonium qubits in a flip-chip package

The strong anharmonicity and high coherence times inherent to fluxonium superconducting circuits are beneficial for quantum information processing. In addition to requiring high-quality physical qubits, a quantum processor needs to be assembled in a manner that minimizes crosstalk and decoherence. In this paper, we report work on fluxonium qubits packaged in a flip-chip architecture, where a classical control and readout chip is bump-bonded to the quantum chip, forming a multi-chip module (MCM). The modular approach allows for improved connectivity between the qubits and control/readout elements, and separate fabrication processes. We characterize the coherence properties of the individual fluxonium qubits, demonstrate high fidelity single-qubit gates with 6 ns microwave pulses (without DRAG), and identify the main decoherence mechanisms to improve on the reported results.
Beam-wave interaction analysis of a magnetically insulated line oscillator

Magnetically insulated line oscillator (MILO) using a metal disc-loaded coaxial cylindrical waveguide as its RF interaction structure is field analyzed for the beam-wave interaction using the linearized Vlasov equation. The beam present dispersion relation of the device is obtained applying the modal matching technique which is further used to estimate the oscillation frequency, temporal growth rate, output energy, and other device parameters. Further, MILO is simulated using commercial pic code “magic,” and the electron momentum and energy plots are found to be in agreement with those obtained through the present analysis within 5%. Furthermore, the device temporal growth rate reported in the literature for the experimental MILO device is also compared with the present analytical theory and found in agreement ∼5%.
BikeDNA: A Tool for Bicycle Infrastructure Data & Network Assessment

High-quality data on existing bicycle infrastructure are a requirement for evidence-based bicycle network planning, which supports a green transition of human mobility. However, this requirement is rarely met: Data from governmental agencies or crowdsourced projects like OpenStreetMap often suffer from unknown, heterogeneous, or low quality. Currently available tools for road network data quality assessment often fail to account for network topology, spatial heterogeneity, and bicycle-specific data characteristics. To fill these gaps, we introduce BikeDNA, an open-source tool for reproducible quality assessment tailored to bicycle infrastructure data with a focus on network structure and connectivity. BikeDNA performs either a standalone analysis of one data set or a comparative analysis between OpenStreetMap and a reference data set, including feature matching. Data quality metrics are considered both globally for the entire study area and locally on grid cell level, thus exposing spatial variation in data quality. Interactive maps and HTML/PDF reports are generated to facilitate the visual exploration and communication of results. BikeDNA supports quality assessments of bicycle infrastructure data for a wide range of applications -- from urban planning to OpenStreetMap data improvement or network research for sustainable mobility.
Stochastic discrete optimization

In this paper a stochastic search method is proposed for finding a global solution to the stochastic discrete optimization problem in which the objective function must be estimated by Monte Carlo simulation. Although there are many practical problems of this type in the fields of manufacturing engineering, operations research, and management science, there have not been any nonheuristic methods proposed for such discrete problems with stochastic infrastructure. The proposed method is very simple, yet it finds a global optimum solution. The method exploits the randomness of Monte Carlo simulation and generates a sequence of solution estimates. This generated sequence turns out to be a nonstationary Markov chain, and it is shown under mild conditions that the Markov chain is strongly ergodic and that the probability that the current solution estimate is global optimum converges to one. Furthermore, the speed of convergence is also analyzed.
Robust, High-Precision GNSS Carrier-Phase Positioning with Visual-Inertial Fusion

Robust, high-precision global localization is fundamental to a wide range of outdoor robotics applications. Conventional fusion methods use low-accuracy pseudorange based GNSS measurements ($>>5m$ errors) and can only yield a coarse registration to the global earth-centered-earth-fixed (ECEF) frame. In this paper, we leverage high-precision GNSS carrier-phase positioning and aid it with local visual-inertial odometry (VIO) tracking using an extended Kalman filter (EKF) framework that better resolves the integer ambiguity concerned with GNSS carrier-phase. %to achieve centimeter-level accuracy in the ECEF frame. We also propose an algorithm for accurate GNSS-antenna-to-IMU extrinsics calibration to accurately align VIO to the ECEF frame. Together, our system achieves robust global positioning demonstrated by real-world hardware experiments in severely occluded urban canyons, and outperforms the state-of-the-art RTKLIB by a significant margin in terms of integer ambiguity solution fix rate and positioning RMSE accuracy.
Theory of magnetospheric standing hydromagnetic waves with large azimuthal wave number: 4. Standing waves in the ring current region

There is a rich variety of observations of long-period pulsations in the ring current region. One class of such pulsations is characterized by large azimuthal wave numbers and close confinement to a field line. In this paper, computations are made of the eigenfrequencies and fields of standing oscillations which can exist in a realistic self-consistent magnetohydrodynamic model of such a region. The model, which depends on a few simple parameters, allows for the shape of the field line to be distorted from a dipole. In this paper it is limited to isotropic pressure. The magnetic field and transverse gradient of pressure can be defined. It is assumed that growth and loss processes are small and in balance because the plasma has reached a quasi-linear equilibrium state. The computations show that a rich variety of compressional modes can exist. They are much more complex than simple coupled Alfven and magnetosonic modes which are often invoked in such circumstances. At some points along the field line, such modes may be closely coupled while at other points they are essentially uncoupled with different wavelengths. This leads to modes with complex nonharmonic structures. It is noted that the transverse pressure gradient allows an MHD instability, related to the flute instability, to exist in appropriate circumstances. Such calculations should allow good comparison of theory and observation.
PANACEA: An Automated Misinformation Detection System on COVID-19

In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available.
A Pathway Towards Responsible AI Generated Content

AI Generated Content (AIGC) has received tremendous attention within the past few years, with content ranging from image, text, to audio, video, etc. Meanwhile, AIGC has become a double-edged sword and recently received much criticism regarding its responsible usage. In this article, we focus on three main concerns that may hinder the healthy development and deployment of AIGC in practice, including risks from privacy; bias, toxicity, misinformation; and intellectual property (IP). By documenting known and potential risks, as well as any possible misuse scenarios of AIGC, the aim is to sound the alarm of potential risks and misuse, help society to eliminate obstacles, and promote the more ethical and secure deployment of AIGC.
MoSFPAD: An end-to-end Ensemble of MobileNet and Support Vector Classifier for Fingerprint Presentation Attack Detection

Automatic fingerprint recognition systems are the most extensively used systems for person authentication although they are vulnerable to Presentation attacks. Artificial artifacts created with the help of various materials are used to deceive these systems causing a threat to the security of fingerprint-based applications. This paper proposes a novel end-to-end model to detect fingerprint Presentation attacks. The proposed model incorporates MobileNet as a feature extractor and a Support Vector Classifier as a classifier to detect presentation attacks in cross-material and cross-sensor paradigms. The feature extractor's parameters are learned with the loss generated by the support vector classifier. The proposed model eliminates the need for intermediary data preparation procedures, unlike other static hybrid architectures. The performance of the proposed model has been validated on benchmark LivDet 2011, 2013, 2015, 2017, and 2019 databases, and overall accuracy of 98.64%, 99.50%, 97.23%, 95.06%, and 95.20% is achieved on these databases, respectively. The performance of the proposed model is compared with state-of-the-art methods and the proposed method outperforms in cross-material and cross-sensor paradigms in terms of average classification error.
Open-World Object Manipulation using Pre-trained Vision-Language Models

For robots to follow instructions from people, they must be able to connect the rich semantic information in human vocabulary, e.g."can you get me the pink stuffed whale? "to their sensory observations and actions. This brings up a notably difficult challenge for robots: while robot learning approaches allow robots to learn many different behaviors from first-hand experience, it is impractical for robots to have first-hand experiences that span all of this semantic information. We would like a robot's policy to be able to perceive and pick up the pink stuffed whale, even if it has never seen any data interacting with a stuffed whale before. Fortunately, static data on the internet has vast semantic information, and this information is captured in pre-trained vision-language models. In this paper, we study whether we can interface robot policies with these pre-trained models, with the aim of allowing robots to complete instructions involving object categories that the robot has never seen first-hand. We develop a simple approach, which we call Manipulation of Open-World Objects (MOO), which leverages a pre-trained vision-language model to extract object-identifying information from the language command and image, and conditions the robot policy on the current image, the instruction, and the extracted object information. In a variety of experiments on a real mobile manipulator, we find that MOO generalizes zero-shot to a wide range of novel object categories and environments. In addition, we show how MOO generalizes to other, non-language-based input modalities to specify the object of interest such as finger pointing, and how it can be further extended to enable open-world navigation and manipulation. The project's website and evaluation videos can be found at https://robot-moo.github. io/