Miracle-dz/UniFAR-FTUs huggingface dataset:
SeSaMe: A Framework to Simulate Self-Reported Ground Truth for Mental Health Sensing Studies

Advances in mobile and wearable technologies have enabled the potential to passively monitor a person's mental, behavioral, and affective health. These approaches typically rely on longitudinal collection of self-reported outcomes, e.g., depression, stress, and anxiety, to train machine learning (ML) models. However, the need to continuously self-report adds a significant burden on the participants, often resulting in attrition, missing labels, or insincere responses. In this work, we introduce the Scale Scores Simulation using Mental Models (SeSaMe) framework to alleviate participants' burden in digital mental health studies. By leveraging pre-trained large language models (LLMs), SeSaMe enables the simulation of participants' responses on psychological scales. In SeSaMe, researchers can prompt LLMs with information on participants' internal behavioral dispositions, enabling LLMs to construct mental models of participants to simulate their responses on psychological scales. We demonstrate an application of SeSaMe, where we use GPT-4 to simulate responses on one scale using responses from another as behavioral information. We also evaluate the alignment between human and SeSaMe-simulated responses to psychological scales. Then, we present experiments to inspect the utility of SeSaMe-simulated responses as ground truth in training ML models by replicating established depression and anxiety screening tasks from a previous study. Our results indicate SeSaMe to be a promising approach, but its alignment may vary across scales and specific prediction objectives. We also observed that model performance with simulated data was on par with using the real data for training in most evaluation scenarios. We conclude by discussing the potential implications of SeSaMe in addressing some challenges researchers face with ground-truth collection in passive sensing studies.
Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction

In a great number of tasks in science and engineering, the goal is to infer an unknown image from a small number of measurements collected from a known forward model describing certain sensing or imaging modality. Due to resource constraints, this task is often extremely ill-posed, which necessitates the adoption of expressive prior information to regularize the solution space. Score-based diffusion models, due to its impressive empirical success, have emerged as an appealing candidate of an expressive prior in image reconstruction. In order to accommodate diverse tasks at once, it is of great interest to develop efficient, consistent and robust algorithms that incorporate {\em unconditional} score functions of an image prior distribution in conjunction with flexible choices of forward models. This work develops an algorithmic framework for employing score-based diffusion models as an expressive data prior in general nonlinear inverse problems. Motivated by the plug-and-play framework in the imaging community, we introduce a diffusion plug-and-play method (\textsf{DPnP}) that alternatively calls two samplers, a proximal consistency sampler based solely on the likelihood function of the forward model, and a denoising diffusion sampler based solely on the score functions of the image prior. The key insight is that denoising under white Gaussian noise can be solved {\em rigorously} via both stochastic (i.e., DDPM-type) and deterministic (i.e., DDIM-type) samplers using the unconditional score functions. We establish both asymptotic and non-asymptotic performance guarantees of \textsf{DPnP}, and provide numerical experiments to illustrate its promise in solving both linear and nonlinear image reconstruction tasks. To the best of our knowledge, \textsf{DPnP} is the first provably-robust posterior sampling method for nonlinear inverse problems using unconditional diffusion priors.
A Study in Dataset Pruning for Image Super-Resolution

In image Super-Resolution (SR), relying on large datasets for training is a double-edged sword. While offering rich training material, they also demand substantial computational and storage resources. In this work, we analyze dataset pruning as a solution to these challenges. We introduce a novel approach that reduces a dataset to a core-set of training samples, selected based on their loss values as determined by a simple pre-trained SR model. By focusing the training on just 50% of the original dataset, specifically on the samples characterized by the highest loss values, we achieve results comparable to or even surpassing those obtained from training on the entire dataset. Interestingly, our analysis reveals that the top 5% of samples with the highest loss values negatively affect the training process. Excluding these samples and adjusting the selection to favor easier samples further enhances training outcomes. Our work opens new perspectives to the untapped potential of dataset pruning in image SR. It suggests that careful selection of training data based on loss-value metrics can lead to better SR models, challenging the conventional wisdom that more data inevitably leads to better performance.
New Physics Pathways from B Processes

We re-consider recent measures of $R_{K}$ and $R_{K^*}$, now compatible with the Standard Model expectations, as well as the results for the process $\text{BR}(B_s \rightarrow \mu^+ \mu^-)$ alongside earlier determinations of $R_{D^{(\ast)}}$ and $\text{BR}(B_c \rightarrow \tau \nu)$. We provide analytic constraints on the associated Wilson coefficients in both the $b \to s$ and the $b \to c$ sectors. These allow us to estimate the scale of potential New Physics for generic extensions of the Standard Model. We then use the results to constrain the leptoquark landscape.
Demineralization of enamel in relation to the fluoride release of materials.

PURPOSE To examine the reduction in enamel demineralization provided by fluoride release from a conventional glass-ionomer, a resin-modified glass-ionomer and an experimental fluoride-releasing resin-based composite compared to a conventional resin-based composite control, and to correlate the level of fluoride release with demineralization. MATERIALS AND METHODS Enamel surfaces of extracted human incisors had a 0.4 mm thick layer of the specified test material carefully placed in a band across the mid-facial enamel to simulate a cement layer beneath an orthodontic bracket. The top surface of the test material was covered with nail varnish, leaving only the edges of the material exposed to release fluoride. The teeth were additionally covered with nail varnish to within 1 mm of the test material. Each group of teeth was placed into separate volumes of unstirred demineralizing solution at a pH of 4.7 for 4 days. The specimens were sectioned and examined by polarized light microscopy. Lesion areas were measured at distances from 100-800 microm away from the test material. Fluoride release for the test materials was measured for periods up to 5 months. RESULTS All of the fluoride-releasing materials demonstrated a statistically significant (P< 0.05) degree of protection of enamel from demineralization compared to the non-fluoride control material. The degree of protection was greatest near the material, but lesion areas increased with distance in an inverse relationship to the amount of fluoride release. Lesions were displaced from the region near the materials and the mean displacement was directly related to amount of fluoride release. The mean lesion areas for each distance decreased with the logarithm of the cumulative fluoride release.
Hippocampal commissure defects in crosses of four inbred mouse strains with absent corpus callosum

It is known that four common inbred mouse strains show defects of the forebrain commissures. The BALB/cJ strain has a low frequency of abnormally small corpus callosum, whereas the 129 strains have many animals with deficient corpus callosum. The I/LnJ and BTBR T+ tf/J strains never have a corpus callosum, whereas half of I/LnJ and almost all BTBR show severely reduced size of the hippocampal commissure. Certain F1 hybrid crosses among these strains are known to be less severely abnormal than the inbred parents, suggesting that the parent strains have different genetic causes of commissure defects. In this study, all hybrid crosses among the four strains were investigated. The BTBR × I/Ln hybrid expressed almost no defects of the hippocampal commissure, unlike its inbred parent strains. Numerous three‐way crosses among the four strains yielded many mice with no corpus callosum and severely reduced hippocampal commissure, which shows that the phenotypic defect can result from several different combinations of genetic alleles. The F2 and F3 hybrid crosses of BTBR and I/LnJ had almost 100% absence of the corpus callosum but about 50% frequency of deficient hippocampal commissure. The four‐way hybrid cross among all four abnormal strains involved highly fertile parents and yielded a very wide phenotypic range of defects from almost no hippocampal commissure to totally normal forebrain commissures. The F2 and F3 crosses as well as the four‐way cross provide excellent material for studies of genetic linkage and behavioral consequences of commissure defects.
High performance wide area data transfers over high performance networks

This paper introduces a new user-level communication protocol designed to provide high-performance data transfers across high-bandwidth, high-delay, networks. The protocol incorporates the most important enhancements defined by the networking community to improve the performance to TCP for this environment, and also defines enhancements unique to this protocol. In terms of the so-called "Large Window" extensions to TCP, this protocol implements a communication window that is essentially infinite, and provides a selective acknowledgement window that spans the entire data transfer. In terms of user-level extensions to TCP, it implements a user-defined acknowledgement frequency, a user-defined "batch sending" window, and a simple framework within which the user can define the algorithm that determines the next data packet to be sent out across all eligible packets. We present experimental results demonstrating data throughput on the order of 85% - 92% of the maximum available bandwidth across both short haul and long haul high-performance networks.
Deep functional multiple index models with an application to SER

Speech Emotion Recognition (SER) plays a crucial role in advancing human-computer interaction and speech processing capabilities. We introduce a novel deep-learning architecture designed specifically for the functional data model known as the multiple-index functional model. Our key innovation lies in integrating adaptive basis layers and an automated data transformation search within the deep learning framework. Simulations for this new model show good performances. This allows us to extract features tailored for chunk-level SER, based on Mel Frequency Cepstral Coefficients (MFCCs). We demonstrate the effectiveness of our approach on the benchmark IEMOCAP database, achieving good performance compared to existing methods.
Generic dimensional and dynamical properties of invariant measures of full-shift systems over countable alphabets

In this work, we are interested in characterizing typical (generic) dimensional properties of invariant measures associated with the full-shift system, $T$, in a product space whose alphabet is a countable set. More specifically, we show that the set of invariant measures with infinite packing dimension equal to infinity is a dense $G_\delta$ subset of $\mathcal{M}(T)$, the space of $T$-invariant measures endowed with the weak topology, where the alphabet $M$ is a countable Polish metric space. We also show that the set of invariant measures with upper $q$-generalized fractal dimension (with $q>1$) equal to infinity is a dense $G_\delta$ subset of $\mathcal{M}(T)$, where the alphabet $M$ is a countable compact metric space. This improves the results obtained by Carvalho and Condori in \cite{AS} and \cite{AS2}, respectively. Furthermore, we discuss the dynamical consequences of such results, regarding the upper recurrence rates and upper quantitative waiting time indicator for typical orbits, and how the fractal dimensions of invariant measures and such dynamical quantities behave under an $\alpha$-H\"older conjugation.
Conservation of Forest Birds: Evidence of a Shifting Baseline in Community Structure

Background Quantifying changes in forest bird diversity is an essential task for developing effective conservation actions. When subtle changes in diversity accumulate over time, annual comparisons may offer an incomplete perspective of changes in diversity. In this case, progressive change, the comparison of changes in diversity from a baseline condition, may offer greater insight because changes in diversity are assessed over longer periods of times. Our objectives were to determine how forest bird diversity has changed over time and whether those changes were associated with forest disturbance. Methodology/Principal Findings We used North American Breeding Bird Survey data, a time series of Landsat images classified with respect to land cover change, and mixed-effects models to associate changes in forest bird community structure with forest disturbance, latitude, and longitude in the conterminous United States for the years 1985 to 2006. We document a significant divergence from the baseline structure for all birds of similar migratory habit and nest location, and all forest birds as a group from 1985 to 2006. Unexpectedly, decreases in progressive similarity resulted from small changes in richness (<1 species per route for the 22-year study period) and modest losses in abundance (−28.7–−10.2 individuals per route) that varied by migratory habit and nest location. Forest disturbance increased progressive similarity for Neotropical migrants, permanent residents, ground nesting, and cavity nesting species. We also documented highest progressive similarity in the eastern United States. Conclusions/Significance Contemporary forest bird community structure is changing rapidly over a relatively short period of time (e.g., ∼22 years). Forest disturbance and forest regeneration are primary factors associated with contemporary forest bird community structure, longitude and latitude are secondary factors, and forest loss is a tertiary factor. Importantly, these findings suggest some regions of the United States may already fall below the habitat amount threshold where fragmentation effects become important predictors of forest bird community structure.
Learning Collaborative Impedance-Based Robot Behaviors

Research in learning from demonstration has focused on transferring movements from humans to robots. However, a need is arising for robots that do not just replicate the task on their own, but that also interact with humans in a safe and natural way to accomplish tasks cooperatively. Robots with variable impedance capabilities opens the door to new challenging applications, where the learning algorithms must be extended by encapsulating force and vision information. In this paper we propose a framework to transfer impedance-based behaviors to a torque-controlled robot by kinesthetic teaching. The proposed model encodes the examples as a task-parameterized statistical dynamical system, where the robot impedance is shaped by estimating virtual stiffness matrices from the set of demonstrations. A collaborative assembly task is used as testbed. The results show that the model can be used to modify the robot impedance along task execution to facilitate the collaboration, by triggering stiff and compliant behaviors in an on-line manner to adapt to the user's actions.
Magnetic vs. nonmagnetic polymorphs of RuBr$_3$ under pressure

Pressure evolution of the crystal structure and magnetism of the honeycomb $\alpha$-RuBr$_3$ is studied using high-pressure x-ray diffraction, magnetometry, and density-functional band-structure calculations. Hydrostatic compression transforms antiferromagnetic $\alpha$-RuBr$_3$ ($R\bar 3$) into paramagnetic $\alpha'$-RuBr$_3$ ($P\bar 1$) where short Ru-Ru bonds cause magnetism collapse above 1.3 GPa at 0 K and 2.5 GPa at 295 K. Below this critical pressure, the N\'eel temperature of $\alpha$-RuBr$_3$ increases with the slope of 1.8 K/GPa. Pressure tunes $\alpha$-RuBr$_3$ away from the Kitaev limit, whereas increased third-neighbor in-plane coupling and interlayer coupling lead to a further stabilization of the collinear zigzag state. Both $\alpha$- and $\alpha'$-RuBr$_3$ are metastable at ambient pressure, but their transformation into the thermodynamically stable $\beta$-polymorph is kinetically hindered at room temperature.
Mass Concentration of Two-Spinless Fermi Systems with Attractive Interactions

We study the two-spinless mass-critical Fermi systems with attractive interactions and trapping potentials. We prove that ground states of the system exist, if and only if the strength $a$ of attractive interactions satisfies $0<a<a_2^*$, where $0<a_2^*<+\infty$ is the best constant of a dual finite-rank Lieb-Thirring inequality. By the blow-up analysis of many-fermion systems, we show that ground states of the system concentrate at the flattest minimum points of the trapping potential $V(x)$ as $a\nearrow a_2^*$.
Lis1 regulates dynein by sterically blocking its mechanochemical cycle

Regulation of cytoplasmic dynein's motor activity is essential for diverse eukaryotic functions, including cell division, intracellular transport, and brain development. The dynein regulator Lis1 is known to keep dynein bound to microtubules; however, how this is accomplished mechanistically remains unknown. We have used three-dimensional electron microscopy, single-molecule imaging, biochemistry, and in vivo assays to help establish this mechanism. The three-dimensional structure of the dynein–Lis1 complex shows that binding of Lis1 to dynein's AAA+ ring sterically prevents dynein's main mechanical element, the ‘linker’, from completing its normal conformational cycle. Single-molecule experiments show that eliminating this block by shortening the linker to a point where it can physically bypass Lis1 renders single dynein motors insensitive to regulation by Lis1. Our data reveal that Lis1 keeps dynein in a persistent microtubule-bound state by directly blocking the progression of its mechanochemical cycle. DOI: http://dx.doi. org/10.7554/eLife.03372. 001
Aldosterone and progression of kidney disease

Experimental evidence indicates that aldosterone, besides its mineralcorticoid properties, directly contributes to accelerate renal damage through promotion of cell growth, fibrosis and inflammation. As a consequence, attenuation of growth-promoting and fibroproliferative effects of aldosterone might contribute to slow progression of chronic renal injury. Preliminary clinical observations have documented that aldosterone blockers added to angiotensin-converting enzyme inhibitor- and/or angiotensin receptor blocker-based regimens exerted significant antiproteinuric effects in patients with diabetic and nondiabetic nephropathies. Further studies in larger cohorts are now required to definitively address the safety and efficacy of aldosterone antagonism in patients with chronic kidney diseases.
At 120 msec You Can Spot the Animal but You Don't Yet Know It's a Dog

Earlier studies suggested that the visual system processes information at the basic level (e.g., dog) faster than at the subordinate (e.g., Dalmatian) or superordinate (e.g., animals) levels. However, the advantage of the basic category over the superordinate category in object recognition has been challenged recently, and the hierarchical nature of visual categorization is now a matter of debate. To address this issue, we used a forced-choice saccadic task in which a target and a distractor image were displayed simultaneously on each trial and participants had to saccade as fast as possible toward the image containing animal targets based on different categorization levels. This protocol enables us to investigate the first 100–120 msec, a previously unexplored temporal window, of visual object categorization. The first result is a surprising stability of the saccade latency (median RT ∼155 msec) regardless of the animal target category and the dissimilarity of target and distractor image sets. Accuracy was high (around 80% correct) for categorization tasks that can be solved at the superordinate level but dropped to almost chance levels for basic level categorization. At the basic level, the highest accuracy (62%) was obtained when distractors were restricted to another dissimilar basic category. Computational simulations based on the saliency map model showed that the results could not be predicted by pure bottom–up saliency differences between images. Our results support a model of visual recognition in which the visual system can rapidly access relatively coarse visual representations that provide information at the superordinate level of an object, but where additional visual analysis is required to allow more detailed categorization at the basic level.
Uncertainty Quantification for Gradient-based Explanations in Neural Networks

Explanation methods help understand the reasons for a model's prediction. These methods are increasingly involved in model debugging, performance optimization, and gaining insights into the workings of a model. With such critical applications of these methods, it is imperative to measure the uncertainty associated with the explanations generated by these methods. In this paper, we propose a pipeline to ascertain the explanation uncertainty of neural networks by combining uncertainty estimation methods and explanation methods. We use this pipeline to produce explanation distributions for the CIFAR-10, FER+, and California Housing datasets. By computing the coefficient of variation of these distributions, we evaluate the confidence in the explanation and determine that the explanations generated using Guided Backpropagation have low uncertainty associated with them. Additionally, we compute modified pixel insertion/deletion metrics to evaluate the quality of the generated explanations.
Semi-Supervised Learning for Neural Machine Translation

While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi-supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the source-to-target and target-to-source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the Chinese-English dataset show that our approach achieves significant improvements over state-of-the-art SMT and NMT systems.