Miracle-dz/UniFAR-FTUs huggingface dataset:
Leptin prevents aberrant targeting of tau to hippocampal synapses via PI 3 kinase driven inhibition of GSK3β

Amyloid‐β (Aβ) and hyper‐phosphorylated tau are key hallmarks of Alzheimer's disease (AD), with an accumulation of both proteins linked to hippocampal synaptic dysfunction. Recent evidence indicates that Aβ drives mis‐localisation of tau from axons to synapses, resulting in AMPA receptor (AMPAR) internalisation and impaired excitatory synaptic function. These tau‐driven synaptic impairments are thought to underlie the cognitive deficits in AD. Consequently, limiting the synapto‐toxic effects of tau may prevent AD‐related cognitive deficits. Increasing evidence links leptin dysfunction with higher AD risk, and numerous studies have identified neuroprotective properties of leptin in AD models of Aβ‐induced toxicity. However, it is unclear if leptin protects against tau‐related synaptic dysfunction. Here we show that Aβ1‐42 significantly increases dendritic and synaptic levels of tau and p‐tau in hippocampal neurons, and these effects were blocked by leptin. In accordance with GSK‐3β being involved in tau phosphorylation, the protective effects of leptin involve PI 3‐kinase (PI3K) activation and inhibition of GSK‐3β. Aβ1‐42‐driven synaptic targeting of tau was associated with the removal of GluA1‐containing AMPARs from synapses, which was also inhibited by leptin‐driven inhibition of GSK‐3β. Direct application of oligomeric tau to hippocampal neurons caused internalisation of GluA1‐containing AMPARs and this effect was blocked by prior application of leptin. Similarly, leptin prevented the ability of tau to block induction of activity‐dependent long‐term potentiation (LTP) at hippocampal SC‐CA1 synapses. These findings increase our understanding of the neuroprotective actions of leptin in the early pre‐clinical stages of AD and further validate the leptin system as a therapeutic target in AD.
Značajke biološkog (demografskog) sastava stanovništva Hrvatske

This paper studies the biological structure (age and sex) of the population in Croatia. The three successive census data are being analysed, and the focus is on the 2001-year census. Croatia is compared with Europe, the characteristics according to the settlement type (urban and other) are considered, a spatial differentiation of basic features (on the county level) is presented, and demographic ageing and age degree of the population in Croatia are analysed. First, the sex and age structures are considered separately, then together, by means of the so-called age-sex pyramid.
Effectiveness of a single low‐dose computed tomography screening for lung cancer: A population‐based perspective cohort study in China

The purpose of this perspective cohort study was to evaluate the effectiveness of low‐dose computed tomography (LDCT) screening for lung cancer in China. This study was conducted under the China Urban Cancer Screening Program (CanSPUC). The analysis was based on participants aged 40 to 74 years from 2012 to 2019. A total of 255 569 eligible participants were recruited in the study. Among the 58 136 participants at high risk of lung cancer, 20 346 (35.00%) had a single LDCT scan (defined as the screened group) and 37 790 (65.00%) not (defined as the non‐screened group). Overall, 1162 participants were diagnosed with lung cancer at median follow‐up time of 5.25 years. The screened group had the highest cumulative incidence of lung cancer and the non‐screened group had the highest cumulative lung cancer mortality and all‐cause cumulative mortality. We performed inverse probability weighting (IPW) to account for potential imbalances, and Cox proportional hazards model to estimate the weighted association between mortality and LDCT scans. After IPW adjusted with baseline characteristics, the lung cancer incidence density was significantly increased (37.0% increase) (HR1.37 [95%CI 1.12‐1.69]), lung cancer mortality was decreased (31.0% decrease) (HR0.69 [95%CI 0.49‐0.97]), and the all‐cause mortality was significantly decreased (23.0% lower) (HR0.77 [95% CI 0.68‐0.87]) in the screened group. In summary, a single LDCT for lung cancer screening will reduce the mortality of lung cancer and all‐cause mortality in China.
Sorting it out in Hardware: A State-of-the-Art Survey

Sorting is a fundamental operation in various applications and a traditional research topic in computer science. Improving the performance of sorting operations can have a significant impact on many application domains. For high-performance sorting, much attention has been paid to hardware-based solutions. These are often realized with application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs). Recently, in-memory sorting solutions have also been proposed to address the movement cost issue between memory and processing units, also known as Von Neumann bottleneck. Due to the complexity of the sorting algorithms, achieving an efficient hardware implementation for sorting data is challenging. A large body of prior solutions is built on compare-and-swap (CAS) units. These are categorized as comparison-based sorting. Some recent solutions offer comparison-free sorting. In this survey, we review the latest works in the area of hardware-based sorting. We also discuss the recent hardware solutions for partial and stream sorting. Finally, we will discuss some important concerns that need to be considered in the future designs of sorting systems.
Self acceleration from spectral geometry in dissipative quantum-walk dynamics

Dynamic behaviors of a physical system often originate from its spectral properties. In open systems, where the effective non-Hermitian description enables a wealth of spectral structures on the complex plane, the concomitant dynamics is significantly enriched, whereas the identification and comprehension of the underlying connections are challenging. Here we experimentally demonstrate the correspondence between the transient self acceleration of local excitations and the non-Hermitian spectral topology using lossy photonic quantum walks. Focusing first on one-dimensional quantum walks, we show that the measured short-time acceleration of the wave function is proportional to the area enclosed by the eigenspectrum. We then reveal similar correspondence in two-dimension quantum walks, where the self acceleration is proportional to the volume enclosed by the eigenspectrum in the complex parameter space. In both dimensions, the transient self acceleration crosses over to a long-time behavior dominated by a constant flow at the drift velocity. Our results unveil the universal correspondence between spectral topology and transient dynamics, and offer a sensitive probe for phenomena in non-Hermitian systems that originate from spectral geometry.
GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Models

While multi-modal models have successfully integrated information from image, video, and audio modalities, integrating graph modality into large language models (LLMs) remains unexplored. This discrepancy largely stems from the inherent divergence between structured graph data and unstructured text data. Incorporating graph knowledge provides a reliable source of information, enabling potential solutions to address issues in text generation, e.g., hallucination, and lack of domain knowledge. To evaluate the integration of graph knowledge into language models, a dedicated dataset is needed. However, there is currently no benchmark dataset specifically designed for multimodal graph-language models. To address this gap, we propose GraphextQA, a question answering dataset with paired subgraphs, retrieved from Wikidata, to facilitate the evaluation and future development of graph-language models. Additionally, we introduce a baseline model called CrossGNN, which conditions answer generation on the paired graphs by cross-attending question-aware graph features at decoding. The proposed dataset is designed to evaluate graph-language models' ability to understand graphs and make use of it for answer generation. We perform experiments with language-only models and the proposed graph-language model to validate the usefulness of the paired graphs and to demonstrate the difficulty of the task.
Yuga: Automatically Detecting Lifetime Annotation Bugs in the Rust Language

The Rust programming language is becoming increasingly popular among systems programmers due to its efficient performance and robust memory safety guarantees. Rust employs an ownership model to ensure this guarantee by allowing each value to be owned by only one identifier at a time. Additionally, it introduces the concept of borrowing and lifetimes to enable other variables to borrow the values under certain conditions temporarily. Despite its benefits, security vulnerabilities have been reported in Rust projects, often attributed to the use of"unsafe"Rust code. These vulnerabilities, in part, arise from incorrect lifetime annotations on function signatures. However, existing tools fail to detect these bugs, primarily because such bugs are rare, challenging to detect through dynamic analysis, and require explicit memory models. To overcome these limitations, first, we characterize incorrect lifetime annotations as a source of memory safety bugs and leverage this understanding to devise a novel static analysis tool, Yuga, to detect potential lifetime annotation bugs. Yuga uses a multi-phase analysis approach, starting with a quick pattern-matching algorithm to identify potential buggy components and then conducting a flow and field-sensitive alias analysis to confirm the bugs. We also curate new datasets of lifetime annotation bugs. Yuga successfully detects bugs with good precision on these datasets, and we make the code and datasets publicly available for review.
Sparse critical graphs for defective $(1,3)$-coloring

A graph $G$ is $(1,3)$-colorable if its vertices can be partitioned into subsets $V_1$ and $V_2$ so that every vertex in $G[V_1]$ has degree at most $1$ and every vertex in $G[V_2]$ has degree at most $3$. We prove that every graph with maximum average degree at most 28/9 is $(1, 3)$-colorable.
Cut-Cell Microstructures for Two-scale Structural Optimization

Two-scale topology optimization, combined with the design of microstructure families with a broad range of effective material parameters, is increasingly widely used in many fabrication applications to achieve a target deformation behavior for a variety of objects. The main idea of this approach is to optimize the distribution of material properties in the object partitioned into relatively coarse cells, and then replace each cell with microstructure geometry that mimics these material properties. In this paper, we focus on adapting this approach to complex shapes in situations when preserving the shape's surface is important. Our approach extends any regular (i.e. defined on a regular lattice grid) microstructure family to complex shapes, by enriching it with individually optimized cut-cell tiles adapted to the geometry of the cut-cell. We propose an automated and robust pipeline based on this approach, and we show that the performance of the regular microstructure family is only minimally affected by our extension while allowing its use on 2D and 3D shapes of high complexity.
Recurrent networks recognize patterns with low-dimensional oscillations

This study proposes a novel dynamical mechanism for pattern recognition discovered by interpreting a recurrent neural network (RNN) trained on a simple task inspired by the SET card game. We interpreted the trained RNN as recognizing patterns via phase shifts in a low-dimensional limit cycle in a manner analogous to transitions in a finite state automaton (FSA). We further validated this interpretation by handcrafting a simple oscillatory model that reproduces the dynamics of the trained RNN. Our findings not only suggest of a potential dynamical mechanism capable of pattern recognition, but also suggest of a potential neural implementation of FSA. Above all, this work contributes to the growing discourse on deep learning model interpretability.
Sudden drop of seismic velocity after the 2004 Mw 6.6 mid-Niigata earthquake, Japan, observed with Passive Image Interferometry

[1] Passive Image Interferometry (PII) is a seismological method employing ambient seismic noise to monitor the temporal evolution of mean shear wave velocity within the Earth. First, the elastic Green's tensor between two seismometers is computed from the cross correlation of seismic noise recorded during a certain period. Second, the constructed seismograms of different time periods are treated as earthquake multiplets, and small time shifts in their coda are used to invert a relative change in mean shear wave velocity. When applied to the source region of the 2004 Mw = 6.6 mid-Niigata earthquake, Japan (centroid depth 5 km), we used noise recorded at six seismometers located at a distance of less than 25 km from the epicenter. Daily inversions during the 2 months before and after the earthquake show a sudden drop of mean shear wave velocity of some tenths of a percent at the time of the mid-Niigata earthquake. Using noise in two frequency bands, 0.1–0.5 Hz and 2–8 Hz, we find similar amplitudes for the velocity drop, which indicates that changes are not restricted to the shallow subsurface. Possible interpretations of this velocity decrease are a decrease of crustal stress after the earthquake, a nonlinear site response in the shallow subsurface layer due to strong ground motion, or structural weakening due to the creation of new fractures in the source area of the earthquake.
Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models

Exploring variations of 3D shapes is a time-consuming process in traditional 3D modeling tools. Deep generative models of 3D shapes often feature continuous latent spaces that can, in principle, be used to explore potential variations starting from a set of input shapes; in practice, doing so can be problematic—latent spaces are high dimensional and hard to visualize, contain shapes that are not relevant to the input shapes, and linear paths through them often lead to sub-optimal shape transitions. Furthermore, one would ideally be able to explore variations in the original high-quality meshes used to train the generative model, not its lower-quality output geometry. In this paper, we present a method to explore variations among a given set of landmark shapes by constructing a mapping from an easily-navigable 2D exploration space to a subspace of a pre-trained generative model. We first describe how to find a mapping that spans the set of input landmark shapes and exhibits smooth variations between them. We then show how to turn the variations in this subspace into deformation fields, to transfer those variations to high-quality meshes for the landmark shapes. Our results show that our method can produce visually-pleasing and easily-navigable 2D exploration spaces for several different shape categories, especially as compared to prior work on learning deformation spaces for 3D shapes. https://github.com/ArmanMaesumi/generative-mesh-subspaces
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning

This work identifies a simple pre-training mechanism that leads to representations exhibiting better continual and transfer learning. This mechanism -- the repeated resetting of weights in the last layer, which we nickname"zapping"-- was originally designed for a meta-continual-learning procedure, yet we show it is surprisingly applicable in many settings beyond both meta-learning and continual learning. In our experiments, we wish to transfer a pre-trained image classifier to a new set of classes, in a few shots. We show that our zapping procedure results in improved transfer accuracy and/or more rapid adaptation in both standard fine-tuning and continual learning settings, while being simple to implement and computationally efficient. In many cases, we achieve performance on par with state of the art meta-learning without needing the expensive higher-order gradients, by using a combination of zapping and sequential learning. An intuitive explanation for the effectiveness of this zapping procedure is that representations trained with repeated zapping learn features that are capable of rapidly adapting to newly initialized classifiers. Such an approach may be considered a computationally cheaper type of, or alternative to, meta-learning rapidly adaptable features with higher-order gradients. This adds to recent work on the usefulness of resetting neural network parameters during training, and invites further investigation of this mechanism.
Impact of Co-occurrence on Factual Knowledge of Large Language Models

Large language models (LLMs) often make factually incorrect responses despite their success in various applications. In this paper, we hypothesize that relying heavily on simple co-occurrence statistics of the pre-training corpora is one of the main factors that cause factual errors. Our results reveal that LLMs are vulnerable to the co-occurrence bias, defined as preferring frequently co-occurred words over the correct answer. Consequently, LLMs struggle to recall facts whose subject and object rarely co-occur in the pre-training dataset although they are seen during finetuning. We show that co-occurrence bias remains despite scaling up model sizes or finetuning. Therefore, we suggest finetuning on a debiased dataset to mitigate the bias by filtering out biased samples whose subject-object co-occurrence count is high. Although debiased finetuning allows LLMs to memorize rare facts in the training set, it is not effective in recalling rare facts unseen during finetuning. Further research in mitigation will help build reliable language models by preventing potential errors. The code is available at \url{https://github.com/CheongWoong/impact_of_cooccurrence}.
LimberJack.jl: auto-differentiable methods for angular power spectra analyses

We present LimberJack.jl, a fully auto-differentiable code for cosmological analyses of 2 point auto- and cross-correlation measurements from galaxy clustering, CMB lensing and weak lensing data written in Julia. Using Julia's auto-differentiation ecosystem, LimberJack.jl can obtain gradients for its outputs up to an order of magnitude faster than traditional finite difference methods. This makes LimberJack.jl greatly synergistic with gradient-based sampling methods, such as Hamiltonian Monte Carlo, capable of efficiently exploring parameter spaces with hundreds of dimensions. We first prove LimberJack.jl's reliability by reanalysing the DES Y1 3$\times$2-point data. We then showcase its capabilities by using a O(100) parameters Gaussian Process to reconstruct the cosmic growth from a combination of DES Y1 galaxy clustering and weak lensing data, eBOSS QSO's, CMB lensing and redshift-space distortions. Our Gaussian process reconstruction of the growth factor is statistically consistent with the $\Lambda$CDM Planck 2018 prediction at all redshifts. Moreover, we show that the addition of RSD data is extremely beneficial to this type of analysis, reducing the uncertainty in the reconstructed growth factor by $20\%$ on average across redshift. LimberJack.jl is a fully open-source project available on Julia's general repository of packages and GitHub.
GeNIOS: an (almost) second-order operator-splitting solver for large-scale convex optimization

We introduce the GEneralized Newton Inexact Operator Splitting solver (GeNIOS) for large-scale convex optimization. GeNIOS speeds up ADMM by approximately solving approximate subproblems: it uses a second-order approximation to the most challenging ADMM subproblem and solves it inexactly with a fast randomized solver. Despite these approximations, GeNIOS retains the convergence rate of classic ADMM and can detect primal and dual infeasibility from the algorithm iterates. At each iteration, the algorithm solves a positive-definite linear system that arises from a second-order approximation of the first subproblem and computes an approximate proximal operator. GeNIOS solves the linear system using an indirect solver with a randomized preconditioner, making it particularly useful for large-scale problems with dense data. Our high-performance open-source implementation in Julia allows users to specify convex optimization problems directly (with or without conic reformulation) and allows extensive customization. We illustrate GeNIOS's performance on a variety of problem types. Notably, GeNIOS is more than ten times faster than existing solvers on large-scale, dense problems.
Open-Set Knowledge-Based Visual Question Answering with Inference Paths

Given an image and an associated textual question, the purpose of Knowledge-Based Visual Question Answering (KB-VQA) is to provide a correct answer to the question with the aid of external knowledge bases. Prior KB-VQA models are usually formulated as a retriever-classifier framework, where a pre-trained retriever extracts textual or visual information from knowledge graphs and then makes a prediction among the candidates. Despite promising progress, there are two drawbacks with existing models. Firstly, modeling question-answering as multi-class classification limits the answer space to a preset corpus and lacks the ability of flexible reasoning. Secondly, the classifier merely consider"what is the answer"without"how to get the answer", which cannot ground the answer to explicit reasoning paths. In this paper, we confront the challenge of \emph{explainable open-set} KB-VQA, where the system is required to answer questions with entities at wild and retain an explainable reasoning path. To resolve the aforementioned issues, we propose a new retriever-ranker paradigm of KB-VQA, Graph pATH rankER (GATHER for brevity). Specifically, it contains graph constructing, pruning, and path-level ranking, which not only retrieves accurate answers but also provides inference paths that explain the reasoning process. To comprehensively evaluate our model, we reformulate the benchmark dataset OK-VQA with manually corrected entity-level annotations and release it as ConceptVQA. Extensive experiments on real-world questions demonstrate that our framework is not only able to perform open-set question answering across the whole knowledge base but provide explicit reasoning path.