Miracle-dz/UniFAR-FTUs huggingface dataset:
Hippocampal grading provides higher classification accuracy for those in the AD trajectory than hippocampal volume

Much research has focused on neurodegeneration in aging and Alzheimer's disease (AD). We developed Scoring by Nonlocal Image Patch Estimator (SNIPE), a non‐local patch‐based measure of anatomical similarity and hippocampal segmentation to measure hippocampal change. While SNIPE shows enhanced predictive power over hippocampal volume, it is unknown whether SNIPE is more strongly associated with group differences between normal controls (NC), early MCI (eMCI), late (lMCI), and AD than hippocampal volume. Alzheimer's Disease Neuroimaging Initiative older adults were included in the first analyses (N = 1666, 513 NCs, 269 eMCI, 556 lMCI, and 328 AD). Sub‐analyses investigated amyloid positive individuals (N = 834; 179 NC, 148 eMCI, 298 lMCI, and 209 AD) to determine accuracy in those on the AD trajectory. We compared SNIPE grading, SNIPE volume, and Freesurfer volume as features in seven different machine learning techniques classifying participants into their correct cohort using 10‐fold cross‐validation. The best model was then validated in the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). SNIPE grading provided the highest classification accuracy for all classifications in both the full and amyloid positive sample. When classifying NC:AD, SNIPE grading provided an 89% accuracy (full sample) and 87% (amyloid positive sample). Freesurfer volume provided much lower accuracies of 65% (full sample) and 46% (amyloid positive sample). In the AIBL validation cohort, SNIPE grading provided a 90% classification accuracy for NC:AD. These findings suggest SNIPE grading provides increased classification accuracy over both SNIPE and Freesurfer volume. SNIPE grading offers promise to accurately identify people with and without AD.
Nose to brain delivery of naringin-loaded chitosan nanoparticles for potential use in oxaliplatin-induced chemobrain in rats: impact on oxidative stress, cGAS/STING and HMGB1/RAGE/TLR2/MYD88 inflammatory axes

ABSTRACT Objectives Oxaliplatin induces chemobrain in cancer patients/survivors. Nutraceutical naringin has antioxidant and anti-inflammatory properties with low oral bioavailability. Our aim was to formulate naringin in chitosan nanoparticles for nose to brain delivery and assess its neuroprotective effect against oxaliplatin-induced chemobrain in rats. Methods Naringin chitosan nanoparticles were prepared by ionic gelation. Rats were administered oral naringin (80 mg/kg), intranasal naringin (0.3 mg/kg) or intranasal naringin-loaded chitosan nanoparticles (0.3 mg/kg). Naringin’s neuroprotective efficacy was assessed based on behavioral tests, histopathology, and measuring oxidative stress and inflammatory markers. Results Selected nanoparticles formulation showed drug loading of 5%, size of 150 nm and were cationic. Intranasal naringin administration enhanced memory function, inhibited hippocampal acetylcholinesterase activity, and corrected oxaliplatin-induced histological changes. Moreover, it reduced malondialdehyde and elevated reduced glutathione hippocampal levels. Furthermore, it decreased levels of inflammatory markers: NF-kB and TNF-α by 1.25-fold. Upstream to this inflammatory status, intranasal naringin downregulated the hippocampal protein levels of two pathways: cGAS/STING and HMGB1/RAGE/TLR2/MYD88. Conclusion Intranasal naringin-loaded chitosan nanoparticles showed superior amelioration of oxaliplatin-induced chemobrain in rats at a dose 267-fold lower to that administered orally. The potential involvement of cGAS/STING and HMGB1/RAGE/TLR2/MYD88 pathways in the mechanistic process of either oxaliplatin-induced chemobrain or naringin-mediated neuroprotection was evidenced.
On Reconfiguration Problems: Structure and Tractability

Given an n-vertex graph G and two vertices s and t in G, determining whether there exists a path and computing the length of the shortest path between s and t are two of the most fundamental graph problems. In the classical battle of P versus NP or “easy” versus “hard”, both of these problems are on the easy side. That is, they can be solved in poly(n) time, where poly is any polynomial function. But what if our input consisted of a 2-vertex graph? Of course, we can no longer assume G to be part of the input, as reading the input alone requires more than poly(n) time. Instead, we are given an oracle encoded using poly(n) bits and that can, in constant or poly(n) time, answer queries of the form “is u a vertex in G” or “is there an edge between u and v? ”. Given such an oracle and two vertices of the 2-vertex graph, can we still determine if there is a path or compute the length of the shortest path between s and t in poly(n) time? A slightly different, but equally insightful, formulation of the question above is as follows. Given a set S of n objects, consider the graph R(S) which contains one vertex for each set in the power set of S, 2, and two vertices are adjacent in R(S) whenever the size of their symmetric difference is equal to one. Clearly, this graph contains 2 vertices and can be easily encoded in poly(n) bits using the oracle described above. It is not hard to see that there exists a path between any two vertices of R(S). Moreover, computing the length of a shortest path can be accomplished in constant time; it is equal to the size of the symmetric difference of the two underlying sets. If the vertex set of R(S) were instead restricted to a subset of 2, both of our problems can become NP-complete or even PSPACEcomplete. Therefore, another interesting question is whether we can determine what types of “restriction” on the vertex set of R(S) induce such variations in the complexity of the two problems. These two seemingly artificial questions are in fact quite natural and appear in many practical and theoretical problems. In particular, these are exactly the types of questions asked under the reconfiguration framework, the main subject of this thesis. Under the reconfiguration framework, instead of finding a feasible solution to some instance I of a search problem Q, we are interested in structural and algorithmic questions related to the solution space of Q. Naturally, given some adjacency relation A defined over feasible solutions of Q, size of the symmetric difference being one such relation, the solution space can be represented using a graph RQ(I). RQ(I) contains one vertex for each feasible solution of Q on instance I and two vertices share an edge whenever their corresponding solutions are adjacent under A. An edge in RQ(I) corresponds to a reconfiguration step, a walk in RQ(I) is a sequence of such steps, a reconfiguration sequence, and RQ(I) is a reconfiguration graph. Studying problems related to reconfiguration graphs has received
Reversibility of Aortic Stiffening During the First Waves of COVID-19

Acute and chronic in ﬂ ammation can alter arterial physiology and lead to micro-and macro-vascular dysfunction (i.e., endothelial dysfunction, glomerular injury, elastic and muscular arteries stiffening) that, in turn, increase the risk for acute myocardial infarction and stroke. 1,2 The ﬁ rst waves of Coronavirus Disease 2019 (COVID)-19 were characterized by an acute peak of severe in ﬂ ammation, a rise in cardiovascular (CV) risk and the persistence of several symptoms for >12 weeks after the resolution of the acute phase of the disease. 3 In this setting, increased arterial stiffness may be a mediator between severe in ﬂ ammation and increased CV risk. To date, only a few studies have evaluated the arterial stiffness in subjects with COVID-19. In an article published in this issue of Angiology , 4 Can et al enrolled 70 subjects (age 38 ± 10 years, female sex 59%) and measured, 1 and 7 months after the acute phase of COVID-19, the cardio-ankle vascular index (CAVI), a measure of the overall stiffness of muscular and elastic arteries. 5 This study 4 is of interest for several reasons. First, most of the previously published articles that have reported a vascular dysfunction in COVID-19 patients were cross-sectional and can only hypothesize a causal relationship between COVID-19 infection and arterial stiffening. The longitudinal design of the Can et al 4 study allows to reveal a worsening of the vascular function (i.e., an increase in CAVI) 7 months after the resolution of the acute phase of COVID-19. This ﬁ nding should be considered
Improving the selection of changing-look AGNs through multiwavelength photometric variability

We present second epoch optical spectra for 30 changing-look (CL) candidates found by searching for Type-1 optical variability in a sample of active galactic nuclei (AGNs) spectroscopically classified as Type 2. We use a random-forest-based light-curve classifier and spectroscopic follow-up, confirming 50 per cent of candidates as turning-on CLs. In order to improve this selection method and to better understand the nature of the not-confirmed CL candidates, we perform a multiwavelength variability analysis including optical, mid-infrared (MIR), and X-ray data, and compare the results from the confirmed and not-confirmed CLs identified in this work. We find that most of the not-confirmed CLs are consistent with weak Type 1s dominated by host-galaxy contributions, showing weaker optical and MIR variability. On the contrary, the confirmed CLs present stronger optical fluctuations and experience a long (from five to ten years) increase in their MIR fluxes and the colour W1–W2 over time. In the 0.2–2.3 keV band, at least four out of 11 CLs with available SRG/eROSITA detections have increased their flux in comparison with archival upper limits. These common features allow us to select the most promising CLs from our list of candidates, leading to nine sources with similar multiwavelength photometric properties to our CL sample. The use of machine learning algorithms with optical and MIR light curves will be very useful to identify CLs in future large-scale surveys.
Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation

Multimedia content is of predominance in the modern Web era. Many recommender models have been proposed to investigate how users interact with items which are represented in diverse modalities. In real scenarios, different modalities reveal different aspects of item attributes and usually possess different importance to user purchase decisions. However, it is difficult for models to figure out users' true preference towards different modalities since there exists strong statistical correlation between different modalities. Even worse, the strong statistical correlation might mislead models to learn the spurious preference towards inconsequential modalities. As a result, when data (modal features) distribution shifts, the learned spurious preference might not guarantee to be as effective on inference as on the training set. Given that the statistical correlation between different modalities is a major cause of this problem, we propose a novel MOdality DEcorrelating STable learning framework, MODEST for brevity, to learn users' stable preference. Inspired by sample re-weighting techniques, the proposed method aims to estimate a weight for each item, such that the features from different modalities in the weighted distribution are decorrelated. We adopt Hilbert Schmidt Independence Criterion (HSIC) as independence testing measure which is a kernel-based method capable of evaluating the correlation degree between two multi-dimensional and non-linear variables. Moreover, by utilizing adaptive gradient mask, we empower HSIC with the ability to measure task-relevant correlation. Overall, in the training phase, we alternately optimize (1) model parameters via minimizing weighted Bayesian Personalized Ranking (BPR) loss and (2) sample weights via minimizing modal correlation in the weighted distribution (quantified through HSIC loss). In the inference phase, we can directly use the the trained multimedia recommendation models to make recommendations. Our method could be served as a play-and-plug module for existing multimedia recommendation backbones. Extensive experiments on four public datasets and four state-of-the-art multimedia recommendation backbones unequivocally show that our proposed method can improve the performances by a large margin.
CEIL: Generalized Contextual Imitation Learning

In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation \textbf{L}earning~(CEIL), a general and broadly applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight information matching, we derive CEIL by explicitly learning a hindsight embedding function together with a contextual policy using the hindsight embeddings. To achieve the expert matching objective for IL, we advocate for optimizing a contextual variable such that it biases the contextual policy towards mimicking expert behaviors. Beyond the typical learning from demonstrations (LfD) setting, CEIL is a generalist that can be effectively applied to multiple settings including: 1)~learning from observations (LfO), 2)~offline IL, 3)~cross-domain IL (mismatched experts), and 4) one-shot IL settings. Empirically, we evaluate CEIL on the popular MuJoCo tasks (online) and the D4RL dataset (offline). Compared to prior state-of-the-art baselines, we show that CEIL is more sample-efficient in most online IL tasks and achieves better or competitive performances in offline tasks.
GPU-Resident Sparse Direct Linear Solvers for Alternating Current Optimal Power Flow Analysis

Integrating renewable resources within the transmission grid at a wide scale poses significant challenges for economic dispatch as it requires analysis with more optimization parameters, constraints, and sources of uncertainty. This motivates the investigation of more efficient computational methods, especially those for solving the underlying linear systems, which typically take more than half of the overall computation time. In this paper, we present our work on sparse linear solvers that take advantage of hardware accelerators, such as graphical processing units (GPUs), and improve the overall performance when used within economic dispatch computations. We treat the problems as sparse, which allows for faster execution but also makes the implementation of numerical methods more challenging. We present the first GPU-native sparse direct solver that can execute on both AMD and NVIDIA GPUs. We demonstrate significant performance improvements when using high-performance linear solvers within alternating current optimal power flow (ACOPF) analysis. Furthermore, we demonstrate the feasibility of getting significant performance improvements by executing the entire computation on GPU-based hardware. Finally, we identify outstanding research issues and opportunities for even better utilization of heterogeneous systems, including those equipped with GPUs.
Breakdown of $J_{eff}$ = 0 and $J_{eff}$ = 3/2 states and existence of large magnetic anisotropy energy in vacancy ordered 5$d$ antifluorites: K$_2$ReCl$_6$, K$_2$OsCl$_6$, and K$_2$IrCl$_6$

Vacancy-ordered antifluorite materials (A$_2$BX$_6$) are garnering renewed attention as novel magnetic states driven by spin-orbit coupling (SOC) can be realized in them. In this work, by pursuing density functional theory calculations and model studies, we analyze the ground state electronic and magnetic structure of face-centered cubic (fcc) antifluorites K$_2$ReCl$_6$ (KReC, 5$d^3$), K$_2$OsCl$_6$ (KOsC, 5$d^4$), and K$_2$IrCl$_6$ (KIrC, 5$d^5$). We find that KReC stabilizes in the high-spin $S$ = 3/2 state instead of the expected pseudo-spin $J_{eff}$ = 3/2 state. The former occurs due to large exchange-splitting as compared to the SOC strength. On the contrary, the KOsC stabilizes in broken $J_{eff}$ = 0 ($S$ = 1) simple Mott insulating state while KIrC stabilizes in $J_{eff}$ = 1/2 spin-orbit-assisted Mott insulating state. The presence of an isolated metal-chloride octahedron makes these antifluorites weakly coupled magnetic systems with the nearest and next-nearest-neighbor spin-exchange parameters ($J_1$ and $J_2$) are of the order of 1 meV. For KReC and KOsC, the $J_1$ and $J_2$ are estimated to be antiferromagnetic and ferromagnetic, which leads to a Type-I antiferromagnetic ground state, whereas for KIrC, both $J_1$ and $J_2$ are antiferromagnetic, hence, it stabilizes with a Type-III antiferromagnetic state. Interestingly, in their equilibrium structure, these antifluorites possess large magnetic anisotropy energy (0.6-4 meV/transition metal), which is at least one-to-two orders higher than traditional MAE materials like transition metals and multilayers formed out of them. Moreover, with epitaxial tensile/compressive strain, the MAE enhances by one order, becoming giant for KOsC (20-40 meV/Os).
Are Good Explainers Secretly Human-in-the-Loop Active Learners?

Explainable AI (XAI) techniques have become popular for multiple use-cases in the past few years. Here we consider its use in studying model predictions to gather additional training data. We argue that this is equivalent to Active Learning, where the query strategy involves a human-in-the-loop. We provide a mathematical approximation for the role of the human, and present a general formalization of the end-to-end workflow. This enables us to rigorously compare this use with standard Active Learning algorithms, while allowing for extensions to the workflow. An added benefit is that their utility can be assessed via simulation instead of conducting expensive user-studies. We also present some initial promising results.
Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression

Entropy estimation is essential for the performance of learned image compression. It has been demonstrated that a transformer-based entropy model is of critical importance for achieving a high compression ratio, however, at the expense of a significant computational effort. In this work, we introduce the Efficient Contextformer (eContextformer) - a computationally efficient transformer-based autoregressive context model for learned image compression. The eContextformer efficiently fuses the patch-wise, checkered, and channel-wise grouping techniques for parallel context modeling, and introduces a shifted window spatio-channel attention mechanism. We explore better training strategies and architectural designs and introduce additional complexity optimizations. During decoding, the proposed optimization techniques dynamically scale the attention span and cache the previous attention computations, drastically reducing the model and runtime complexity. Compared to the non-parallel approach, our proposal has ~145x lower model complexity and ~210x faster decoding speed, and achieves higher average bit savings on Kodak, CLIC2020, and Tecnick datasets. Additionally, the low complexity of our context model enables online rate-distortion algorithms, which further improve the compression performance. We achieve up to 17% bitrate savings over the intra coding of Versatile Video Coding (VVC) Test Model (VTM) 16.2 and surpass various learning-based compression models.
Differentially Private Decentralized Deep Learning with Consensus Algorithms

Cooperative decentralized deep learning relies on direct information exchange between communicating agents, each with access to a local dataset which should be kept private. The goal is for all agents to achieve consensus on model parameters after training. However, sharing parameters with untrustworthy neighboring agents could leak exploitable information about local datasets. To combat this, we introduce differentially private decentralized learning that secures each agent's local dataset during and after cooperative training. In our approach, we generalize Differentially Private Stochastic Gradient Descent (DP-SGD) -- a popular differentially private training method for centralized deep learning -- to practical subgradient- and ADMM-based decentralized learning methods. Our algorithms' differential privacy guarantee holds for arbitrary deep learning objective functions, and we analyze the convergence properties for strongly convex objective functions. We compare our algorithms against centrally trained models on standard classification tasks and evaluate the relationships between performance, privacy budget, graph connectivity, and degree of training data overlap among agents. We find that differentially private gradient tracking is resistant to performance degradation under sparse graphs and non-uniform data distributions. Furthermore, we show that it is possible to learn a model achieving high accuracies, within 3% of DP-SGD on MNIST under (1, 10^-5)-differential privacy and within 6% of DP-SGD on CIFAR-100 under (10, 10^-5)-differential privacy, without ever sharing raw data with other agents. Open source code can be found at: https://github.com/jbayrooti/dp-dec-learning.
On the syntax and semantics of resumptive pronouns

The paper presents an analysis of the contribution of resumptive pronouns to the structure and interpretation of relative clauses in Hebrew. Traditionally, resumptive pronouns have been treated as overt phonological realizations of gaps, since it was believed that the interpretation of relative clauses with optional resumptive pronouns is unaffected by whether or not they include the pronoun. Yet the paper shows that the presence of a resumptive pronoun modifies the interpretation of a relative clause. The antecedent of the resumptive pronoun must be interpreted as having wider scope than any other noun phrase within the relative clause. Thus a resumptive pronoun, but not a gap, blocks the raising of quantifiers from within the relative clause, and the de-re interpretation of noun phrases in the clause. This is accounted for by treating resumptive pronouns as pronouns, and interpreting them as resumptive only in clauses which do not contain gaps.
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization

In this work, we decouple the iterative bi-level offline RL (value estimation and policy extraction) from the offline training phase, forming a non-iterative bi-level paradigm and avoiding the iterative error propagation over two levels. Specifically, this non-iterative paradigm allows us to conduct inner-level optimization (value estimation) in training, while performing outer-level optimization (policy extraction) in testing. Naturally, such a paradigm raises three core questions that are not fully answered by prior non-iterative offline RL counterparts like reward-conditioned policy: (q1) What information should we transfer from the inner-level to the outer-level? (q2) What should we pay attention to when exploiting the transferred information for safe/confident outer-level optimization? (q3) What are the benefits of concurrently conducting outer-level optimization during testing? Motivated by model-based optimization (MBO), we propose DROP (design from policies), which fully answers the above questions. Specifically, in the inner-level, DROP decomposes offline data into multiple subsets, and learns an MBO score model (a1). To keep safe exploitation to the score model in the outer-level, we explicitly learn a behavior embedding and introduce a conservative regularization (a2). During testing, we show that DROP permits deployment adaptation, enabling an adaptive inference across states (a3). Empirically, we evaluate DROP on various tasks, showing that DROP gains comparable or better performance compared to prior methods.