Miracle-dz/UniFAR-FTUs huggingface dataset:
AI and Non AI Assessments for Dementia

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.
Mitigating Bias: Enhancing Image Classification by Improving Model Explanations

Deep learning models have demonstrated remarkable capabilities in learning complex patterns and concepts from training data. However, recent findings indicate that these models tend to rely heavily on simple and easily discernible features present in the background of images rather than the main concepts or objects they are intended to classify. This phenomenon poses a challenge to image classifiers as the crucial elements of interest in images may be overshadowed. In this paper, we propose a novel approach to address this issue and improve the learning of main concepts by image classifiers. Our central idea revolves around concurrently guiding the model's attention toward the foreground during the classification task. By emphasizing the foreground, which encapsulates the primary objects of interest, we aim to shift the focus of the model away from the dominant influence of the background. To accomplish this, we introduce a mechanism that encourages the model to allocate sufficient attention to the foreground. We investigate various strategies, including modifying the loss function or incorporating additional architectural components, to enable the classifier to effectively capture the primary concept within an image. Additionally, we explore the impact of different foreground attention mechanisms on model performance and provide insights into their effectiveness. Through extensive experimentation on benchmark datasets, we demonstrate the efficacy of our proposed approach in improving the classification accuracy of image classifiers. Our findings highlight the importance of foreground attention in enhancing model understanding and representation of the main concepts within images. The results of this study contribute to advancing the field of image classification and provide valuable insights for developing more robust and accurate deep-learning models.
Comparing dendritic trees with actual trees

Since they became observable, neuron morphologies have been informally compared with biological trees but they are studied by distinct communities, neuroscientists, and ecologists. The apparent structural similarity suggests there may be common quantitative rules and constraints. However, there are also reasons to believe they should be different. For example, while the environments of trees may be relatively simple, neurons are constructed by a complex iterative program where synapses are made and pruned. This complexity may make neurons less self-similar than trees. Here we test this hypothesis by comparing the features of segmented sub-trees with those of the whole tree. We indeed find more self-similarity within actual trees than neurons. At the same time, we find that many other features are somewhat comparable across the two. Investigation of shapes and behaviors promises new ways of conceptualizing the form-function link.
Introduction to Protein Folding

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation&dynamics. In this chapter we explore basic physical and chemical concepts required to understand protein folding. We introduce major (de)stabilising factors of folded protein structures such as the hydrophobic effect and backbone entropy. In addition, we consider different states along the folding pathway, as well as natively disordered proteins and aggregated protein states. In this chapter, an intuitive understanding is provided about the protein folding process, to prepare for the next chapter on the thermodynamics of protein folding. In particular, it is emphasized that protein folding is a stochastic process and that proteins unfold and refold in a dynamic equilibrium. The effect of temperature on the stability of the folded and unfolded states is also explained.
Multi-Task Learning Improves Performance In Deep Argument Mining Models

The successful analysis of argumentative techniques from user-generated text is central to many downstream tasks such as political and market analysis. Recent argument mining tools use state-of-the-art deep learning methods to extract and annotate argumentative techniques from various online text corpora, however each task is treated as separate and different bespoke models are fine-tuned for each dataset. We show that different argument mining tasks share common semantic and logical structure by implementing a multi-task approach to argument mining that achieves better performance than state-of-the-art methods for the same problems. Our model builds a shared representation of the input text that is common to all tasks and exploits similarities between tasks in order to further boost performance via parameter-sharing. Our results are important for argument mining as they show that different tasks share substantial similarities and suggest a holistic approach to the extraction of argumentative techniques from text.
Technical Report for Ego4D Long Term Action Anticipation Challenge 2023

In this report, we describe the technical details of our approach for the Ego4D Long-Term Action Anticipation Challenge 2023. The aim of this task is to predict a sequence of future actions that will take place at an arbitrary time or later, given an input video. To accomplish this task, we introduce three improvements to the baseline model, which consists of an encoder that generates clip-level features from the video, an aggregator that integrates multiple clip-level features, and a decoder that outputs Z future actions. 1) Model ensemble of SlowFast and SlowFast-CLIP; 2) Label smoothing to relax order constraints for future actions; 3) Constraining the prediction of the action class (verb, noun) based on word co-occurrence. Our method outperformed the baseline performance and recorded as second place solution on the public leaderboard.
ZotCare: a flexible, personalizable, and affordable mhealth service provider

The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHealth) applications, which present significant technical challenges for researchers. While existing mHealth solutions have made progress in addressing some of these challenges, they often fall short in terms of time-to-use, affordability, and flexibility for personalization and adaptation. ZotCare aims to address these limitations by offering ready-to-use and flexible services, providing researchers with an accessible, cost-effective, and adaptable solution for their mHealth studies. This article focuses on ZotCare’s service orchestration and highlights its capabilities in creating a programmable environment for mHealth research. Additionally, we showcase several successful research use cases that have utilized ZotCare, both in the past and in ongoing projects. Furthermore, we provide resources and information for researchers who are considering ZotCare as their mHealth research solution.
ULTIMATE: a deployable multiple integral field unit for Subaru

ULTIMATE is an instrument concept under development at the AAO, for the Subaru Telescope, which will have the unique combination of ground layer adaptive optics feeding multiple deployable integral field units. This will allow ULTIMATE to probe unexplored parameter space, enabling science cases such as the evolution of galaxies at z ~ 0:5 to 1.5, and the dark matter content of the inner part of our Galaxy. ULTIMATE will use Starbugs to position between 7 and 13 IFUs over a 14 × 8 arcmin field-of-view, pro- vided by a new wide-field corrector. All Starbugs can be positioned simultaneously, to an accuracy of better than 5 milli-arcsec within the typical slew-time of the telescope, allowing for very efficient re-configuration between observations. The IFUs will feed either the near-infrared nuMOIRCS or the visible/ near-infrared PFS spectrographs, or both. Future possible upgrades include the possibility of purpose built spectrographs and incorporating OH suppression using fibre Bragg gratings. We describe the science case and resulting design requirements, the baseline instrument concept, and the expected performance of the instrument.
RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation

Along with the nearing completion of the Square Kilometre Array (SKA), comes an increasing demand for accurate and reliable automated solutions to extract valuable information from the vast amount of data it will allow acquiring. Automated source finding is a particularly important task in this context, as it enables the detection and classification of astronomical objects. Deep-learning-based object detection and semantic segmentation models have proven to be suitable for this purpose. However, training such deep networks requires a high volume of labeled data, which is not trivial to obtain in the context of radio astronomy. Since data needs to be manually labeled by experts, this process is not scalable to large dataset sizes, limiting the possibilities of leveraging deep networks to address several tasks. In this work, we propose RADiff, a generative approach based on conditional diffusion models trained over an annotated radio dataset to generate synthetic images, containing radio sources of different morphologies, to augment existing datasets and reduce the problems caused by class imbalances. We also show that it is possible to generate fully-synthetic image-annotation pairs to automatically augment any annotated dataset. We evaluate the effectiveness of this approach by training a semantic segmentation model on a real dataset augmented in two ways: 1) using synthetic images obtained from real masks, and 2) generating images from synthetic semantic masks. We show an improvement in performance when applying augmentation, gaining up to 18% in performance when using real masks and 4% when augmenting with synthetic masks. Finally, we employ this model to generate large-scale radio maps with the objective of simulating Data Challenges.
Independent Sets in Elimination Graphs with a Submodular Objective

Maximum weight independent set (MWIS) admits a $\frac1k$-approximation in inductively $k$-independent graphs and a $\frac{1}{2k}$-approximation in $k$-perfectly orientable graphs. These are a a parameterized class of graphs that generalize $k$-degenerate graphs, chordal graphs, and intersection graphs of various geometric shapes such as intervals, pseudo-disks, and several others. We consider a generalization of MWIS to a submodular objective. Given a graph $G=(V,E)$ and a non-negative submodular function $f: 2^V \rightarrow \mathbb{R}_+$, the goal is to approximately solve $\max_{S \in \mathcal{I}_G} f(S)$ where $\mathcal{I}_G$ is the set of independent sets of $G$. We obtain an $\Omega(\frac1k)$-approximation for this problem in the two mentioned graph classes. The first approach is via the multilinear relaxation framework and a simple contention resolution scheme, and this results in a randomized algorithm with approximation ratio at least $\frac{1}{e(k+1)}$. This approach also yields parallel (or low-adaptivity) approximations. Motivated by the goal of designing efficient and deterministic algorithms, we describe two other algorithms for inductively $k$-independent graphs that are inspired by work on streaming algorithms: a preemptive greedy algorithm and a primal-dual algorithm. In addition to being simpler and faster, these algorithms, in the monotone submodular case, yield the first deterministic constant factor approximations for various special cases that have been previously considered such as intersection graphs of intervals, disks and pseudo-disks.
A generalization of formal multiple zeta values related to multiple Eisenstein series and multiple q-zeta values

We introduce the algebra $\mathcal{G}^{\operatorname{f}}$, which is equipped with natural maps into the algebra of (combinatorial) multiple Eisenstein series and that of multiple q-zeta values. Similar to Racinet's approach to formal multiple zeta values, we consider an affine scheme BM corresponding to the algebra $\mathcal{G}^{\operatorname{f}}$. We show that Racinet's affine scheme DM represented by the algebra of formal multiple zeta values embeds into this affine scheme BM. This leads to a projection from the algebra $\mathcal{G}^{\operatorname{f}}$ onto the algebra of formal multiple zeta values. Via the above natural maps, this projection corresponds to taking the constant terms of multiple Eisenstein series or the limit $q\to1$ of multiple q-zeta values.
Monotone Cubic B-Splines with a Neural-Network Generator

We present a method for fitting monotone curves using cubic B-splines, which is equivalent to putting a monotonicity constraint on the coefficients. We explore different ways of enforcing this constraint and analyze their theoretical and empirical properties. We propose two algorithms for solving the spline fitting problem: one that uses standard optimization techniques and one that trains a Multi-Layer Perceptrons (MLP) generator to approximate the solutions under various settings and perturbations. The generator approach can speed up the fitting process when we need to solve the problem repeatedly, such as when constructing confidence bands using bootstrap. We evaluate our method against several existing methods, some of which do not use the monotonicity constraint, on some monotone curves with varying noise levels. We demonstrate that our method outperforms the other methods, especially in high-noise scenarios. We also apply our method to analyze the polarization-hole phenomenon during star formation in astrophysics. The source code is accessible at \texttt{\url{https://github.com/szcf-weiya/MonotoneSplines.jl}}.
Coping with seasons: evolutionary dynamics of gene networks in a changing environment

In environments that vary frequently and unpredictably, bet-hedgers can overtake the population. Diversifying bet-hedgers have a diverse set of offspring so that, no matter the conditions they find themselves in, at least some offspring will have high fitness. In contrast, conservative bet-hedgers have a set of offspring that all have an in-between phenotype compared to the specialists. Here, we use an evolutionary algorithm of gene regulatory networks to de novo evolve the two strategies and investigate their relative success in different parameter settings. We found that diversifying bet-hedgers almost always evolved first, but then eventually got outcompeted by conservative bet-hedgers. We argue that even though similar selection pressures apply to the two bet-hedger strategies, conservative bet-hedgers could win due to the robustness of their evolved networks, in contrast to the sensitive networks of the diversifying bet-hedgers. These results reveal an unexplored aspect of the evolution of bet-hedging that could shed more light on the principles of biological adaptation in variable environmental conditions.
Multi-task learning with summary statistics

Multi-task learning has emerged as a powerful machine learning paradigm for integrating data from multiple sources, leveraging similarities between tasks to improve overall model performance. However, the application of multi-task learning to real-world settings is hindered by data-sharing constraints, especially in healthcare settings. To address this challenge, we propose a flexible multi-task learning framework utilizing summary statistics from various sources. Additionally, we present an adaptive parameter selection approach based on a variant of Lepski's method, allowing for data-driven tuning parameter selection when only summary statistics are available. Our systematic non-asymptotic analysis characterizes the performance of the proposed methods under various regimes of the sample complexity and overlap. We demonstrate our theoretical findings and the performance of the method through extensive simulations. This work offers a more flexible tool for training related models across various domains, with practical implications in genetic risk prediction and many other fields.
Shadows and photon rings of a spherically accreting Kehagias-Sfetsos black hole

By considering Kehagias-Sfetsos black hole in the framework of the Ho\v{r}ava-Lifshitz gravity, we study the optical appearance of such black holes surrounded by spherical accretion flow. For the static/infalling spherical accretion flow, we compute the observed specific intensity as a function of impact parameter. We also investigate the effect of the Ho\v{r}ava parameter and accreting matter on the luminosity of shadows and photon rings. It is found that an increase in the Ho\v{r}ava parameter decreases the shadow size, while the shadows and photon rings luminosities increase. Moreover, we constrain the Ho\v{r}ava parameter from the observational data reported by the Event Horizon Telescope for M87* and Sgr A*.
ScaIOTA: Scalable Secure Over-the-Air Software Updates for Vehicles

Over-the-Air (OTA) software updates are becoming essential for electric/electronic vehicle architectures in order to reduce recalls amid the increasing software bugs and vulnera-bilities. Current OTA update architectures rely heavily on direct cellular repository-to-vehicle links, which makes the repository a communication bottleneck, and increases the cellular bandwidth utilization cost as well as the software download latency. In this paper, we introduce ScalOTA, an end-to-end scalable OTA software update architecture and secure protocol for modern vehicles. For the first time, we propose using a network of update stations, as part of Electric Vehicle charging stations, to boost the download speed through these stations, and reduce the cellular bandwidth overhead significantly. Our formalized OTA update protocol ensures proven end-to-end chain-of-trust including all stakeholders: manufacturer, suppliers, update stations, and all layers of in-vehicle Electric Control Units (ECUs). The empirical evaluation shows that ScalOTA reduces the bandwidth utilization and download latency up to an order of magnitude compared with current OTA update systems.
SeePrivacy: Automated Contextual Privacy Policy Generation for Mobile Applications

Privacy policies have become the most critical approach to safeguarding individuals' privacy and digital security. To enhance their presentation and readability, researchers propose the concept of contextual privacy policies (CPPs), aiming to fragment policies into shorter snippets and display them only in corresponding contexts. In this paper, we propose a novel multi-modal framework, namely SeePrivacy, designed to automatically generate contextual privacy policies for mobile apps. Our method synergistically combines mobile GUI understanding and privacy policy document analysis, yielding an impressive overall 83.6% coverage rate for privacy-related context detection and an accuracy of 0.92 in extracting corresponding policy segments. Remarkably, 96% of the retrieved policy segments can be correctly matched with their contexts. The user study shows SeePrivacy demonstrates excellent functionality and usability (4.5/5). Specifically, participants exhibit a greater willingness to read CPPs (4.1/5) compared to original privacy policies (2/5). Our solution effectively assists users in comprehending privacy notices, and this research establishes a solid foundation for further advancements and exploration.
APRIL: Approximating Polygons as Raster Interval Lists

The spatial intersection join an important spatial query operation, due to its popularity and high complexity. The spatial join pipeline takes as input two collections of spatial objects (e.g., polygons). In the filter step, pairs of object MBRs that intersect are identified and passed to the refinement step for verification of the join predicate on the exact object geometries. The bottleneck of spatial join evaluation is in the refinement step. We introduce APRIL, a powerful intermediate step in the pipeline, which is based on raster interval approximations of object geometries. Our technique applies a sequence of interval joins on 'intervalized' object approximations to determine whether the objects intersect or not. Compared to previous work, APRIL approximations are simpler, occupy much less space, and achieve similar pruning effectiveness at a much higher speed. Besides intersection joins between polygons, APRIL can directly be applied and has high effectiveness for polygonal range queries, within joins, and polygon-linestring joins. By applying a lightweight compression technique, APRIL approximations may occupy even less space than object MBRs. Furthermore, APRIL can be customized to apply on partitioned data and on polygons of varying sizes, rasterized at different granularities. Our last contribution is a novel algorithm that computes the APRIL approximation of a polygon without having to rasterize it in full, which is orders of magnitude faster than the computation of other raster approximations. Experiments on real data demonstrate the effectiveness and efficiency of APRIL; compared to the state-of-the-art intermediate filter, APRIL occupies 2x-8x less space, is 3.5x-8.5x more time-efficient, and reduces the end-to-end join cost up to 3 times.