Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Constraint Relaxation on the Minimum Vertex Cover Problem in Random Graphs

A statistical-mechanical study of the effect of constraint relaxation on the minimum vertex cover problem in Erd\H{o}s-R\'enyi random graphs is presented. Using a penalty-method formulation for constraint relaxation, typical properties of solutions, including infeasible solutions that violate the constraints, are analyzed by means of the replica method and cavity method. The problem involves a competition between reducing the number of vertices to be covered and satisfying the edge constraints. The analysis under the replica-symmetric (RS) ansatz clarifies that the competition leads to degeneracies in the vertex and edge states, which determine the quantitative properties of the system, such as the cover and penalty ratios. A precise analysis of these effects improves the accuracy of RS approximation for the minimum cover ratio in the replica symmetry breaking (RSB) region. Furthermore, the analysis based on the RS cavity method indicates that the RS/RSB boundary of the ground states with respect to the mean degree of the graphs is expanded, and the critical temperature is lowered by constraint relaxation.
Multimodal Identification of Alzheimer's Disease: A Review

Alzheimer's disease is a progressive neurological disorder characterized by cognitive impairment and memory loss. With the increasing aging population, the incidence of AD is continuously rising, making early diagnosis and intervention an urgent need. In recent years, a considerable number of teams have applied computer-aided diagnostic techniques to early classification research of AD. Most studies have utilized imaging modalities such as magnetic resonance imaging (MRI), positron emission tomography (PET), and electroencephalogram (EEG). However, there have also been studies that attempted to use other modalities as input features for the models, such as sound, posture, biomarkers, cognitive assessment scores, and their fusion. Experimental results have shown that the combination of multiple modalities often leads to better performance compared to a single modality. Therefore, this paper will focus on different modalities and their fusion, thoroughly elucidate the mechanisms of various modalities, explore which methods should be combined to better harness their utility, analyze and summarize the literature in the field of early classification of AD in recent years, in order to explore more possibilities of modality combinations.
Covariance alignment: from maximum likelihood estimation to Gromov-Wasserstein

Feature alignment methods are used in many scientific disciplines for data pooling, annotation, and comparison. As an instance of a permutation learning problem, feature alignment presents significant statistical and computational challenges. In this work, we propose the covariance alignment model to study and compare various alignment methods and establish a minimax lower bound for covariance alignment that has a non-standard dimension scaling because of the presence of a nuisance parameter. This lower bound is in fact minimax optimal and is achieved by a natural quasi MLE. However, this estimator involves a search over all permutations which is computationally infeasible even when the problem has moderate size. To overcome this limitation, we show that the celebrated Gromov-Wasserstein algorithm from optimal transport which is more amenable to fast implementation even on large-scale problems is also minimax optimal. These results give the first statistical justification for the deployment of the Gromov-Wasserstein algorithm in practice.
SD-NAE: Generating Natural Adversarial Examples with Stable Diffusion

Robustly evaluating deep learning image classifiers is challenging due to some limitations of standard datasets. Natural Adversarial Examples (NAEs), arising naturally from the environment and capable of deceiving classifiers, are instrumental in identifying vulnerabilities in trained models. Existing works collect such NAEs by filtering from a huge set of real images, a process that is passive and lacks control. In this work, we propose to actively synthesize NAEs with the state-of-the-art Stable Diffusion. Specifically, our method formulates a controlled optimization process, where we perturb the token embedding that corresponds to a specified class to synthesize NAEs. The generation is guided by the gradient of loss from the target classifier so that the created image closely mimics the ground-truth class yet fools the classifier. Named SD-NAE (Stable Diffusion for Natural Adversarial Examples), our innovative method is effective in producing valid and useful NAEs, which is demonstrated through a meticulously designed experiment. Our work thereby provides a valuable method for obtaining challenging evaluation data, which in turn can potentially advance the development of more robust deep learning models. Code is available at https://github.com/linyueqian/SD-NAE.
From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?

The gameplay of strategic board games such as chess, Go and Hex is often characterized by combinatorial, relational structures -- capturing distinct interactions and non-local patterns -- and not just images. Nonetheless, most common self-play reinforcement learning (RL) approaches simply approximate policy and value functions using convolutional neural networks (CNN). A key feature of CNNs is their relational inductive bias towards locality and translational invariance. In contrast, graph neural networks (GNN) can encode more complicated and distinct relational structures. Hence, we investigate the crucial question: Can GNNs, with their ability to encode complex connections, replace CNNs in self-play reinforcement learning? To this end, we do a comparison with Hex -- an abstract yet strategically rich board game -- serving as our experimental platform. Our findings reveal that GNNs excel at dealing with long range dependency situations in game states and are less prone to overfitting, but also showing a reduced proficiency in discerning local patterns. This suggests a potential paradigm shift, signaling the use of game-specific structures to reshape self-play reinforcement learning.
NERIF: GPT-4V for Automatic Scoring of Drawn Models

Scoring student-drawn models is time-consuming. Recently released GPT-4V provides a unique opportunity to advance scientific modeling practices by leveraging the powerful image processing capability. To test this ability specifically for automatic scoring, we developed a method NERIF (Notation-Enhanced Rubric Instruction for Few-shot Learning) employing instructional note and rubrics to prompt GPT-4V to score students' drawn models for science phenomena. We randomly selected a set of balanced data (N = 900) that includes student-drawn models for six modeling assessment tasks. Each model received a score from GPT-4V ranging at three levels: 'Beginning,' 'Developing,' or 'Proficient' according to scoring rubrics. GPT-4V scores were compared with human experts' scores to calculate scoring accuracy. Results show that GPT-4V's average scoring accuracy was mean =. 51, SD = . 037. Specifically, average scoring accuracy was . 64 for the 'Beginning' class, . 62 for the 'Developing' class, and . 26 for the 'Proficient' class, indicating that more proficient models are more challenging to score. Further qualitative study reveals how GPT-4V retrieves information from image input, including problem context, example evaluations provided by human coders, and students' drawing models. We also uncovered how GPT-4V catches the characteristics of student-drawn models and narrates them in natural language. At last, we demonstrated how GPT-4V assigns scores to student-drawn models according to the given scoring rubric and instructional notes. Our findings suggest that the NERIF is an effective approach for employing GPT-4V to score drawn models. Even though there is space for GPT-4V to improve scoring accuracy, some mis-assigned scores seemed interpretable to experts. The results of this study show that utilizing GPT-4V for automatic scoring of student-drawn models is promising.
Effect of arable crops on the growth of poplar (Populus deltoides) tree in agroforestry system

Poplar AY-48 was planted at the density of 1075 trees per hectare and at the spacing of 1.52 x 6.10m and 3.05 x 3.05m under intercropped and pure stand agroforestry systems. Intercropped agroforestry system gave higher diameter (dbh), dbh increment, crown width and wood volume (m3/ha) compared to pure tree stand agroforestry system after 8 years of planting. Intercropping agroforestry system was more economical at the rotation of 4 & 6 years compared to pure poplar stand. The rotation age of 8 years was found to be uneconomical under both the agroforesty systems.
Pathogen-prey-predator relations of avian raptors during epizootics of highly pathogenic avian influenza virus HPAIV H5N1 (clade 2.3.4.4b) in Germany

Transition of highly pathogenic clade 2.3. 4.4b H5 avian influenza virus (HPAIV) from epizootic to enzootic status in Northern European countries was associated with severe losses and even mass mortalities among various wild bird species. Both avian and mammalian raptors hunting infected debilitated birds or scavenging on virus-contaminated avian carcasses contracted HPAIV infection. This precarious pathogen-prey-predator relation further worsened when in 2021 and 2022 outbreaks in Germany overlapped with the hatching season of avian raptor species. Retro- and prospective surveillance revealed avian raptors as important indicators of HPAIV and its genetic diversity on the one hand. On the other hand, their role as victims of HPAIV is stipulated. The first case of an HPAIV H5N1-related death of a white-tailed sea eagle (Haliaeetus albicilla; WTSE) hatch in Germany, 2021, followed by several such cases in 2022, and a low overall seropositivity rate of 5.0-7.9% among WTSE nestlings, raised fears of a serious negative impact on reproduction rates of WTSEs and other birds of prey when HPAIV becomes enzootic in an ecosystem. However, comparably stable breeding success of WTSE in the study area in 2022 and a potentially evolving natural immunity raises hope for a less severe long-term impact. Article impact statement Adapted surveillance measures were developed to assess risks for the conservation of avian raptors due to the panzootic spread of HPAIV.
Neural correlates of modality-specific and modality-invariant object recognition in the perirhinal cortex

The perirhinal cortex (PER) supports multimodal object recognition, but how multimodal information of objects is integrated within the PER remains unknown. Here, we recorded single units within the PER while rats performed a PER-dependent multimodal object-recognition task. In this task, audiovisual cues were simultaneously (multimodally) or separately (unimodally) presented. We identified two types of object-selective neurons in the PER: crossmodal cells, showing constant firing patterns for an object irrespective of its modality, and unimodal cells, showing a preference for a specific modality. Unimodal cells further dissociated unimodal and multimodal versions of the object by modulating their firing rates according to the modality condition. A population-decoding analysis confirmed that the PER could perform both modality-invariant and modality-specific object decoding – the former for recognizing an object as the same in various conditions and the latter for remembering modality-specific experiences of the same object.
Chemical and isotopic composition (Os, Pb, Nd, and Sr) of Neogene to Quaternary calc-alkalic, shoshonitic, and ultrapotassic mafic rocks from the Italian peninsula: Inferences on the nature of their mantle sources

During the Neogene and Quaternary, potassic and ultrapotassic magmas erupted in association with shoshonitic and calc-alkalic magmas across much of the Italian penin sula. On the basis of the temporal and spatial distribution of this volcanism, and its mineralogical and compositional characteristics, three different magmatic provinces have been defi ned. The northwesternmost province, the Tuscan Magmatic Province, is domi nated by leucite-free ultrapotassic rocks (lamproite), shoshonite, and minor calcalkalic rocks. The Roman Magmatic Province is dominated by leucite-bearing rocks with variable degrees of silica saturation, from undersaturated (leucitites and plagioleucitites) to strongly undersaturated (kamafugites), but minor amounts of shoshonitic to high-potassium calc-alkalic rocks are still present. The Lucanian Magmatic Province, located at the southeasternmost edge of the volcanic belt, is dominated by foiditic (hauynites and leucitites) and kamafugitic (melilitites) members, all strongly undersaturated in silica. In spite of these petrologic differences, the Neogene Italian potassic and ultrapotassic rocks display similar trace-element patterns. Depletion in high fi eld strength elements with respect to large ion lithophile elements is a common feature. Sr, Nd, and Pb iso topic compositions of mafi c high-MgO rocks range widely, relating mainly to geographic location of eruption and to enrichment in alkalies. The Os isotopic composition of these samples, however, does not clearly correlate with eruption location, but is dependent on the amount of “continental crust component” added to the magmas. Some of the studied samples are compatible with crustal contamination
Tau alters global gene expression affecting chromatin in the early stages of Alzheimer’s disease

Recent research revealed that Tau plays critical roles in various neuronal functions. Our previous work demonstrated that destabilization and nuclear delocalization of Tau can alter the expression of glutamatergic genes, thereby mediating early neuronal damage. Upon analysing gene coexpression of AD temporal regions at different stages and the transcriptional output in differentiated neuroblastoma cells, we discovered that changes in Tau availability are linked to global alterations in gene expression that affect multiple neuronal pathways. Comparison with the human AD temporal region showed that the Tau-dependent modulation of gene expression closely resembles an intermediate stage of AD that precedes the definitive AD condition. Furthermore, we identified the chromatin remodelling pathway as being significantly affected by Tau in both our cellular model and AD brains, with reductions in heterochromatin markers. We propose that Tau is able to globally affect the neuronal transcriptome and that its availability is linked to changes in gene expression during intermediate stages of AD development. In addition, we found that the epigenetic pathway is strongly affected by Tau during the progression of AD.
Unexpected suppression of neural responses to natural foreground versus background sounds in auditory cortex

In everyday hearing, listeners encounter complex auditory scenes containing overlapping sounds that must be grouped into meaningful sources, or streamed, to be perceived accurately. A common example of this problem is the perception of a behaviorally relevant foreground stimulus (speech, vocalizations) in complex background noise (environmental, machine noise). Studies using a foreground/background contrast have shown that high-order areas of auditory cortex in humans pre-attentively form an enhanced representation of the foreground over background stimulus. Achieving this invariant foreground representation requires identifying and grouping the features that comprise the background noise so that they can be removed from the representation of the foreground. To study the cortical computations underlying representation of concurrent background (BG) and foreground (FG) stimuli, we recorded single unit responses in the auditory cortex (AC) of ferrets during presentation of natural sound excerpts from these two categories. In primary and secondary AC, we found overall suppression of responses when BGs and FGs were presented concurrently relative to the sum of responses to the same stimuli in isolation. Surprisingly, and in contrast to percepts that emphasize dynamic FGs, responses to FG sounds were suppressed relative to the paired BG sound. The degree of relative FG suppression could be explained by spectro-temporal statistics unique to each natural sound. Moreover, systematic degradation of the same spectro-temporal features decreased FG suppression as the sound categories became progressively less statistically distinct. The strongly suppressed representation of FG sounds in single units of AC in the presence of BG sound reveals a novel insight into how complex acoustic scenes are encoded at early stages of auditory processing.
PVTAD: ALZHEIMER’S DISEASE DIAGNOSIS USING PYRAMID VISION TRANSFORMER APPLIED TO WHITE MATTER OF T1-WEIGHTED STRUCTURAL MRI DATA

Alzheimer’s disease (AD) is a neurodegenerative disorder, and timely diagnosis is crucial for early interventions. AD is known to have disruptive local and global brain neural connections that may be instrumental in understanding and extracting specific biomarkers. Previous machine-learning approaches are mostly based on convolutional neural network (CNN) and standard vision transformer (ViT) models which may not sufficiently capture the multidimensional local and global patterns that may be indicative of AD. Therefore, in this paper, we propose a novel approach called PVTAD to classify AD and cognitively normal (CN) cases using pretrained pyramid vision transformer (PVT) and white matter (WM) of T1-weighted structural MRI (sMRI) data. Our approach combines the advantages of CNN and standard ViT to extract both local and global features indicative of AD from the WM coronal middle slices. We performed experiments on subjects with T1-weighed MPRAGE sMRI scans from the ADNI dataset. Our results demonstrate that the PVTAD achieves an average accuracy of 97.7% and F1-score of 97.6%, outperforming the single and parallel CNN and standard ViT architectures based on sMRI data for AD vs. CN classification.
Combining behavior and mechanics approaches reveals the dynamics of animal impacts in mantis shrimp (Stomatopoda)

Animals deliver and withstand physical impacts in diverse behavioral contexts, from competing rams clashing their antlers together to archerfish impacting prey with jets of water. Though the ability of animals to withstand impact has generally been studied by focusing on morphology, behaviors may also influence impact resistance. Mantis shrimp exchange high-force strikes on each other’s coiled, armored telsons (tailplates) during contests over territory. Prior work has shown that telson morphology has high impact resistance. I hypothesized that the behavior of coiling the telson also contributes to impact energy dissipation. By measuring impact dynamics from high-speed videos of strikes exchanged during contests between freely-moving animals, I found that over 20% more impact energy was dissipated as compared to a prior study that focused solely on morphology. This increase is likely due to behavior: because the telson is lifted off the substrate, the entire body flexes after contact, dissipating more energy than exoskeletal morphology does on its own. While variation in the degree of telson coil did not affect energy dissipation, higher velocity strikes resulted in greater energy dissipation, suggesting striking individuals may vary their behavior to affect impacts. Overall, these findings show that analysis of both behavior and morphology is crucial to understanding impact resistance, and suggest future research on the evolution of structure and function under the selective pressure of biological impacts. Summary statement Freely competing mantis shrimp dissipated over 90% of the energy of high-force strikes by raising their impact-resistant tailplates off the substrate; faster strikes led to greater energy dissipation.
Localized oxygen control in a microfluidic osteochondral interface model recapitulates bone-cartilage crosstalk during osteoarthritis

Osteoarthritis (OA) is characterized by the dysregulation of the osteochondral interface between bone and cartilage. In vitro models that accurately mimic this interface hold great potential for understanding OA pathophysiology and screening therapeutic agents. Presently, research efforts have focused on emulating heterogeneity in structural and mechanical attributes of the extracellular matrix (ECM) at the osteochondral interface. However, the precise simulation of differential oxygen gradients experienced by chondrocytes and osteoblasts in vivo remains a substantial obstacle for modeling osteo-chondral interactions effectively. To overcome this limitation, we show that micropatterned granular hydrogels, which are small microgel particles swelled in liquid culture media to create a shear-yielding jammed-packed solid, can be used to control the delivery of oxygen scavenging agents in a simple and scalable manner. Hypoxic granular hydrogels formulated with Oxyrase™ could maintain <1% oxygen concentration in a conventional cell culture incubator. Primary human chondrocytes maintained in the hypoxic hydrogels expressed a more anabolic phenotype similar to those cultured in a hypoxic incubator. The granular hydrogels can be readily patterned in a microfluidic device to generate a localized hypoxic environment, mimicking the differential oxygen levels at the osteochondral tissue interface (i.e. osteoblast at 20% and chondrocyte at 2% oxygen). Using this microfluidic coculture model, we paired healthy human chondrocytes with osteoblasts isolated from non-sclerotic and sclerotic subchondral bone to investigate how oxygen environment modulates osteoblast-chondrocyte crosstalk during OA. In a differential oxygen environment, the osteoblast-chondrocyte co-culture model showed sclerotic osteoblasts inducing chondrocyte collagen expression changes through increased MMP13 and ADAM15 production, unlike in a uniform normoxic oxygen environment, where the change was driven by altered collagen gene expression favoring Type I over Type II collagen. Furthermore, differential oxygen conditions enabled the identification of extensive transcriptional alterations induced by sclerotic osteoblasts, which involved inflammatory NF-κβ, TGF-β/BMP, and IGF signaling pathways, that was otherwise not detectable in a uniform normoxic co-culture. The microfluidic model with localized oxygen variations effectively mimics physiologically relevant osteoblast-chondrocyte crosstalk, providing valuable insights into OA pathophysiology.
Nonlinear readout of spatial cues underlies robustness of asymmetric cell division

A key challenge in the development of an organism is to maintain robust phenotypic outcomes in the face of perturbation. Yet, how such robust outcomes are encoded by developmental networks remains poorly explored. Here we use the C. elegans zygote as a model to understand sources of developmental robustness during PAR polarity-dependent asymmetric cell division. By quantitatively linking alterations in protein dosage to phenotype in individual embryos, we show that spatial information in the zygote is read out in a highly nonlinear fashion and, as a result, phenotypes are highly canalized against substantial variation in input signals. Specifically, our data point towards an intrinsic robustness of the conserved PAR polarity network that renders polarity axis specification resistant to variations in both the strength of upstream symmetry-breaking cues and PAR protein dosage. At the same time, we find that downstream pathways involved in cell size and fate asymmetry are similarly robust to dosage-dependent changes in the local concentrations of PAR proteins, implying non-trivial complexity in translating PAR signals into pathway outputs. We propose that “quantitative decoupling” of symmetry-breaking, polarity, and asymmetric division modules acts to suppress the accumulation of error as embryos move along this developmental trajectory, thereby ensuring that asymmetric division is robust to perturbation. Such modular organization of developmental networks is likely to be a general mechanism to achieve robust developmental outcomes.