Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of different retinal regions-of-interest imaged by OCT for the classification of intermediate AMD

Purpose: To study whether it is possible to differentiate intermediate age-related macular degeneration (AMD) from healthy controls using partial optical coherence tomography (OCT) data, that is, restricting the input B-scans to certain pre-defined regions of interest (ROIs). Methods: A total of 15744 B-scans (Bioptigen SD-OCT, NC, USA) from 269 intermediate AMD patients and 115 normal subjects, selected from a publicly available dataset, were used in this study (split on subject level in 80% train, 10% validation and 10% test). From each OCT B-scan, three ROIs were extracted: retina, complex between retinal pigment epithelium (RPE) and Bruch’s membrane (BM), and choroid (CHO). These ROIs were obtained using two different methods: masking and cropping. In addition to the six ROIs, the whole OCT B-scan and the binary mask corresponding to the segmentation of the RPE-BM complex were used. For each subset, a convolutional neural network (based on VGG16 architecture and pre-trained on ImageNet) was trained and tested. The performance of the models was evaluated using the area under the 11 receiver operating characteristic (AUROC), accuracy, sensitivity, and specificity. Results: All trained models presented an AUROC, accuracy, sensitivity, and specificity equal to or higher than 0.884, 0.816, 0.685, and 0.644, respectively. The model trained on the whole OCT B-scan presented the best performance (AUROC = 0.983, accuracy = 0.927, sensitivity = 0.862, specificity = 0.913). The models trained on the ROIs obtained with the cropping method led to significantly higher outcomes than those obtained with masking, with the exception of the retinal tissue, where no statistically significant difference was observed between cropping and masking (p = 0.47). The binary mask of the RPE-BM complex was enough to obtain an AUROC of 0.932. Conclusion: This study demonstrated that while using the complete OCT B-scan provided the highest accuracy in classifying intermediate AMD, models trained on specific ROIs such as the RPE-BM complex or the choroid can still achieve high performance. Notably, within the RPE-BM complex, the RPE-BM morphology was found to be the most significant factor for accurate classification
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge

While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks – when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selection strategy, the criteria by which rare-class examples are chosen, has not been systematically evaluated. Further, transformers enable iterative transfer-learning approaches. We propose and investigate transfer- and active learning solutions to the rare class problem of dissonance detection through utilizing models trained on closely related tasks and the evaluation of acquisition strategies, including a proposed probability-of-rare-class (PRC) approach. We perform these experiments for a specific rare-class problem: collecting language samples of cognitive dissonance from social media. We find that PRC is a simple and effective strategy to guide annotations and ultimately improve model accuracy while transfer-learning in a specific order can improve the cold-start performance of the learner but does not benefit iterations of active learning.
NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads

We focus on reconstructing high-fidelity radiance fields of human heads, capturing their animations over time, and synthesizing re-renderings from novel viewpoints at arbitrary time steps. To this end, we propose a new multi-view capture setup composed of 16 calibrated machine vision cameras that record time-synchronized images at 7.1 MP resolution and 73 frames per second. With our setup, we collect a new dataset of over 4700 high-resolution, high-framerate sequences of more than 220 human heads, from which we introduce a new human head reconstruction benchmark1. The recorded sequences cover a wide range of facial dynamics, including head motions, natural expressions, emotions, and spoken language. In order to reconstruct high-fidelity human heads, we propose Dynamic Neural Radiance Fields using Hash Ensembles (NeRSemble). We represent scene dynamics by combining a deformation field and an ensemble of 3D multi-resolution hash encodings. The deformation field allows for precise modeling of simple scene movements, while the ensemble of hash encodings helps to represent complex dynamics. As a result, we obtain radiance field representations of human heads that capture motion over time and facilitate re-rendering of arbitrary novel viewpoints. In a series of experiments, we explore the design choices of our method and demonstrate that our approach outperforms state-of-the-art dynamic radiance field approaches by a significant margin.
Probabilistic neural networks for improved analyses with phenomenological models

Physics models typically contain adjustable parameters to reproduce measured data. While some parameters correspond directly to measured features in the data, others are unobservable. These unobservables can, in some cases, cause ambiguities in the extraction of observables from measured data, or lead to questions on the physical interpretation of fits that require these extra parameters. We propose a method based on deep learning to extract values of observables directly from the data without the need for unobservables. The key to our approach is to label the training data for the deep learning model with only the observables. After training, the deep learning model can determine the values of observables from measured data with no ambiguities arising from unobservables. We demonstrate this method on the phenomenological R-matrix that is widely utilized in nuclear physics to extract resonance parameters from cross section data. Our deep learning model based on Transformers successfully predicts nuclear properties from measurements with no need for the channel radius and background pole parameters required in traditional R-matrix analyses. Details and limitations of this method, which may be useful for studies of a wide range of phenomena, are discussed.
Inference at Scale: Significance Testing for Large Search and Recommendation Experiments

A number of information retrieval studies have been done to assess which statistical techniques are appropriate for comparing systems. However, these studies are focused on TREC-style experiments, which typically have fewer than 100 topics. There is no similar line of work for large search and recommendation experiments; such studies typically have thousands of topics or users and much sparser relevance judgements, so it is not clear if recommendations for analyzing traditional TREC experiments apply to these settings. In this paper, we empirically study the behavior of significance tests with large search and recommendation evaluation data. Our results show that the Wilcoxon and Sign tests show significantly higher Type-1 error rates for large sample sizes than the bootstrap, randomization and t-tests, which were more consistent with the expected error rate. While the statistical tests displayed differences in their power for smaller sample sizes, they showed no difference in their power for large sample sizes. We recommend the sign and Wilcoxon tests should not be used to analyze large scale evaluation results. Our result demonstrate that with Top-N recommendation and large search evaluation data, most tests would have a 100% chance of finding statistically significant results. Therefore, the effect size should be used to determine practical or scientific significance.
Entity Tracking in Language Models

Keeping track of how states of entities change as a text or dialog unfolds is a key prerequisite to discourse understanding. Yet, there have been few systematic investigations into the ability of large language models (LLMs) to track discourse entities. In this work, we present a task probing to what extent a language model can infer the final state of an entity given an English description of the initial state and a series of state-changing operations. We use this task to first investigate whether Flan-T5, GPT-3 and GPT-3.5 can track the state of entities, and find that only GPT-3.5 models, which have been pretrained on large amounts of code, exhibit this ability. We then investigate whether smaller models pretrained primarily on text can learn to track entities, through finetuning T5 on several training/evaluation splits. While performance degrades for more complex splits, we find that even when evaluated on a different set of entities from training or longer operation sequences, a finetuned model can perform non-trivial entity tracking. Taken together, these results suggest that language models can learn to track entities but pretraining on text corpora alone does not make this capacity surface.
Cuttlefish: Low-Rank Model Training without All the Tuning

Recent research has shown that training low-rank neural networks can effectively reduce the total number of trainable parameters without sacrificing predictive accuracy, resulting in end-to-end speedups. However, low-rank model training necessitates adjusting several additional factorization hyperparameters, such as the rank of the factorization at each layer. In this paper, we tackle this challenge by introducing Cuttlefish, an automated low-rank training approach that eliminates the need for tuning factorization hyperparameters. Cuttlefish leverages the observation that after a few epochs of full-rank training, the stable rank (i.e., an approximation of the true rank) of each layer stabilizes at a constant value. Cuttlefish switches from full-rank to low-rank training once the stable ranks of all layers have converged, setting the dimension of each factorization to its corresponding stable rank. Our results show that Cuttlefish generates models up to 5.6 times smaller than full-rank models, and attains up to a 1.2 times faster end-to-end training process while preserving comparable accuracy. Moreover, Cuttlefish outperforms state-of-the-art low-rank model training methods and other prominent baselines. The source code for our implementation can be found at: https://github.com/hwang595/Cuttlefish.
Stimulative Training++: Go Beyond The Performance Limits of Residual Networks

Residual networks have shown great success and become indispensable in recent deep neural network models. In this work, we aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing, and further propose a new training scheme as well as three improved strategies for boosting residual networks beyond their performance limits. Previous research has suggested that residual networks can be considered as ensembles of shallow networks, which implies that the final performance of a residual network is influenced by a group of subnetworks. We identify a previously overlooked problem that is analogous to social loafing, where subnetworks within a residual network are prone to exert less effort when working as part of a group compared to working alone. We define this problem as \textit{network loafing}. Similar to the decreased individual productivity and overall performance as demonstrated in society, network loafing inevitably causes sub-par performance. Inspired by solutions from social psychology, we first propose a novel training scheme called stimulative training, which randomly samples a residual subnetwork and calculates the KL divergence loss between the sampled subnetwork and the given residual network for extra supervision. In order to unleash the potential of stimulative training, we further propose three simple-yet-effective strategies, including a novel KL- loss that only aligns the network logits direction, random smaller inputs for subnetworks, and inter-stage sampling rules. Comprehensive experiments and analysis verify the effectiveness of stimulative training as well as its three improved strategies.
M92 (NGC 6341) Is a Metal-complex Globular Cluster with an Atypical Primordial Population

We present a multiple stellar population study of the metal-poor globular cluster (GC) M92 (NGC 6341), which is long known for the substantial metallicity dispersion, using our own photometric system. We find two groups with slightly different mean metallicities, the metal-poor (MP) stars with [Fe/H] hk = −2.412 ± 0.03 and the metal-rich (MR) ones with −2.282 ± 0.002. The MP constitutes about 23% of the total mass with a more central concentration. Our populational tagging based on the [C/Fe] ch and [N/Fe] nh provides the mean n(P):n(I):n(E) = 32.2:31.6:36.2 (±2.4), where P, I, and E denote the primordial, intermediate, and extreme populations, respectively. Our populational number ratio is consistent with those of others. However, the MP has a significantly different populational number ratio than the mean value, and the domination of the primordial population in the MP is consistent with observations of Galactic GCs that less massive GCs contain larger fractions of the primordial population. Structural and constituent differences between the MP and MR may indicate that M92 is a merger remnant in a dwarf galaxy environment, consistent with recent suggestions that M92 is a GC in a dwarf galaxy or a remnant nucleus of the progenitor galaxy. Discrepancy between our method and those widely used for the Hubble Space Telescope photometry exists in the primordial population. Significant magnesium and oxygen depletions of −0.8 and −0.3 dex, respectively, and helium enhancement of ΔY ≳ 0.03 are required to explain the presence of this abnormal primordial group. No clear explanation is available with limited information of detailed elemental abundances.
VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets

The anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets. Consequently, illegal markets and their connections are challenging to uncover on the Darknet. To identify relationships between illegal markets and their vendors, we propose VendorLink, an NLP-based approach that examines writing patterns to verify, identify, and link unique vendor accounts across text advertisements (ads) on seven public Darknet markets. In contrast to existing literature, VendorLink utilizes the strength of supervised pre-training to perform closed-set vendor verification, open-set vendor identification, and low-resource market adaption tasks. Through VendorLink, we uncover (i) 15 migrants and 71 potential aliases in the Alphabay-Dreams-Silk dataset, (ii) 17 migrants and 3 potential aliases in the Valhalla-Berlusconi dataset, and (iii) 75 migrants and 10 potential aliases in the Traderoute-Agora dataset. Altogether, our approach can help Law Enforcement Agencies (LEA) make more informed decisions by verifying and identifying migrating vendors and their potential aliases on existing and Low-Resource (LR) emerging Darknet markets.
Classical analogs of generalized purities, entropies, and logarithmic negativity

It has recently been proposed classical analogs of the purity, linear quantum entropy, and von Neumann entropy for classical integrable systems, when the corresponding quantum system is in a Gaussian state. We generalized these results by providing classical analogs of the generalized purities, Bastiaans-Tsallis entropies, R\'enyi entropies, and logarithmic negativity for classical integrable systems. These classical analogs are entirely characterized by the classical covariance matrix. We compute these classical analogs exactly in the cases of linearly coupled harmonic oscillators, a generalized harmonic oscillator chain, and a one-dimensional circular lattice of oscillators. In all of these systems, the classical analogs reproduce the results of their quantum counterparts whenever the system is in a Gaussian state. In this context, our results show that quantum information of Gaussian states can be reproduced by classical information.
Information Needs and Technology Use for Daily Living Activities at Home by People Who Are Blind

People who are blind face unique challenges in performing instrumental activities of daily living (iADLs), which require them to rely on their senses as well as assistive technology. Existing research on the strategies used by people who are blind to conduct different iADLs has focused largely on outdoor activities such as wayfinding and navigation. However, less emphasis has been placed on information needs for indoor activities around the home. We present a mixed-methods approach that combines 16 semi-structured interviews with a follow-up behavioral study to understand current and potential future use of technologies for daily activities around the home, especially for cooking. We identify common practices, challenges, and strategies that exemplify user-specific and task-specific needs for effectively performing iADLs at home. Despite this heterogeneity in user needs, we were able to reveal a near universal preference for tactile over digital aids, which has important implications for the design of future assistive technologies. Our work extends existing research on iADLs at home and identifies barriers to technology adoption. Addressing these barriers will be critical to increasing adoption rates of assistive technologies and improving the overall quality of life for individuals who are blind.
MLHOps: Machine Learning for Healthcare Operations

Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in healthcare settings. This paper provides both a survey of work in this area and guidelines for developers and clinicians to deploy and maintain their own models in clinical practice. We cover the foundational concepts of general machine learning operations, describe the initial setup of MLHOps pipelines (including data sources, preparation, engineering, and tools). We then describe long-term monitoring and updating (including data distribution shifts and model updating) and ethical considerations (including bias, fairness, interpretability, and privacy). This work therefore provides guidance across the full pipeline of MLHOps from conception to initial and ongoing deployment.
On the Relative Contribution of Inertia–Gravity Wave Radiation to Asymmetric Instabilities in Tropical Cyclone–like Vortices

AbstractIntense atmospheric vortices such as tropical cyclones experience various asymmetric instabilities during their life cycles. This study investigates how vortex properties and ambient conditions determine the relative importance of different mechanisms that can simultaneously influence the growth of an asymmetric perturbation. The focus is on three-dimensional disturbances of barotropic vortices with nonmonotonic radial distributions of potential vorticity. The primary modes of instability are examined for Rossby numbers between 10 and 100 and Froude numbers in the broad neighborhood of unity. This parameter regime is deemed appropriate for tropical cyclone perturbations with vertical length scales ranging from the depth of the vortex to moderately smaller scales. At relatively small Froude numbers, the main cause of instability inferred from analysis typically involves the interaction of vortex Rossby waves with each other and/or critical-layer potential vorticity perturbations. As the Froude numb.
Cellular blood flow modelling with HemoCell

Many of the intriguing properties of blood originate from its cellular nature. Bulk effects, such as viscosity, depend on the local shear rates and on the size of the vessels. While empirical descriptions of bulk rheology are available for decades, their validity is limited to the experimental conditions they were observed under. These are typically artificial scenarios (e.g., perfectly straight glass tube or in pure shear with no gradients). Such conditions make experimental measurements simpler, however, they do not exist in real systems (i.e., in a real human circulatory system). Therefore, as we strive to increase our understanding on the cardiovascular system and improve the accuracy of our computational predictions, we need to incorporate a more comprehensive description of the cellular nature of blood. This, however, presents several computational challenges that can only be addressed by high performance computing. In this chapter we describe HemoCell , an open-source high performance cellular blood flow simulation, which implements validated mechanical models for red blood cells and is capable of reproducing the emergent transport characteristics of such a complex cellular system. We discuss the accuracy, the range of validity, and demonstrate applications on a series of human diseases.
Evidence for Large-scale, Rapid Gas Inflows in z ∼ 2 Star-forming Disks

We report high-quality Hα/CO imaging spectroscopy of nine massive (log median stellar mass = 10.65 M ⊙) disk galaxies on the star-forming main sequence (henceforth SFGs), near the peak of cosmic galaxy evolution (z ∼ 1.1–2.5), taken with the ESO Very Large Telescope, IRAM-NOEMA, and Atacama Large Millimeter/submillimeter Array. We fit the major axis position–velocity cuts with beam-convolved, forward models with a bulge, a turbulent rotating disk, and a dark matter (DM) halo. We include priors for stellar and molecular gas masses, optical light effective radii and inclinations, and DM masses from our previous rotation curve analysis of these galaxies. We then subtract the inferred 2D model-galaxy velocity and velocity dispersion maps from those of the observed galaxies. We investigate whether the residual velocity and velocity dispersion maps show indications for radial flows. We also carry out kinemetry, a model-independent tool for detecting radial flows. We find that all nine galaxies exhibit significant nontangential flows. In six SFGs, the inflow velocities (v r ∼ 30–90 km s−1, 10%–30% of the rotational component) are along the minor axis of these galaxies. In two cases the inflow appears to be off the minor axis. The magnitudes of the radial motions are in broad agreement with the expectations from analytic models of gravitationally unstable, gas-rich disks. Gravitational torques due to clump and bar formation, or spiral arms, drive gas rapidly inward and result in the formation of central disks and large bulges. If this interpretation is correct, our observations imply that gas is transported into the central regions on ∼10 dynamical timescales.
Cool Cores in Clusters of Galaxies in the Dark Energy Survey

We search for the presence of cool cores in optically selected galaxy clusters from the Dark Energy Survey (DES) and investigate their prevalence as a function of redshift and cluster richness. Clusters were selected from the redMaPPer analysis of three years of DES observations that have archival Chandra X-ray observations, giving a sample of 99 clusters with a redshift range of 0.11 < z < 0.87 and a richness range of 25 < λ < 207. Using the X-ray data, the core temperature was compared to the outer temperature to identify clusters where the core temperature is a factor of 0.7 or less than the outer temperature. We found a cool core fraction of approximately 20% with no significant trend in the cool core fraction with either redshift or richness.