Miracle-dz/UniFAR-FTUs huggingface dataset:
SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research

Recently, there has been growing interest in using Large Language Models (LLMs) for scientific research. Numerous benchmarks have been proposed to evaluate the ability of LLMs for scientific research. However, current benchmarks are mostly based on pre-collected objective questions. This design suffers from data leakage problem and lacks the evaluation of subjective Q/A ability. In this paper, we propose SciEval, a comprehensive and multi-disciplinary evaluation benchmark to address these issues. Based on Bloom's taxonomy, SciEval covers four dimensions to systematically evaluate scientific research ability. In particular, we design a "dynamic" subset based on scientific principles to prevent evaluation from potential data leakage. Both objective and subjective questions are included in SciEval. These characteristics make SciEval a more effective benchmark for scientific research ability evaluation of LLMs. Comprehensive experiments on most advanced LLMs show that, although GPT-4 achieves SOTA performance compared to other LLMs, there is still substantial room for improvement, especially for dynamic questions. The codes and data are publicly available on https://github.com/OpenDFM/SciEval.
Stabilization of Hubbard-Thouless pumps through nonlocal fermionic repulsion

Thouless pumping represents a powerful concept to probe quantized topological invariants in quantum systems. We explore this mechanism in a generalized Rice-Mele Fermi-Hubbard model characterized by the presence of competing onsite and intersite interactions. Contrary to recent experimental and theoretical results, showing a breakdown of quantized pumping induced by the onsite repulsion, we prove that sufficiently large intersite interactions allow for an interaction-induced recovery of Thouless pumps. Our analysis further reveals that the occurrence of stable topological transport at large interactions is connected to the presence of a spontaneous bond-order-wave in the ground-state phase diagram of the model. Finally, we discuss a concrete experimental setup based on ultracold magnetic atoms in an optical lattice to realize the newly introduced Thouless pump. Our results provide a new mechanism to stabilize Thouless pumps in interacting quantum systems.
Self-supervised learning for hotspot detection and isolation from thermal images

Hotspot detection using thermal imaging has recently become essential in several industrial applications, such as security applications, health applications, and equipment monitoring applications. Hotspot detection is of utmost importance in industrial safety where equipment can develop anomalies. Hotspots are early indicators of such anomalies. We address the problem of hotspot detection in thermal images by proposing a self-supervised learning approach. Self-supervised learning has shown potential as a competitive alternative to their supervised learning counterparts but their application to thermography has been limited. This has been due to lack of diverse data availability, domain specific pre-trained models, standardized benchmarks, etc. We propose a self-supervised representation learning approach followed by fine-tuning that improves detection of hotspots by classification. The SimSiam network based ensemble classifier decides whether an image contains hotspots or not. Detection of hotspots is followed by precise hotspot isolation. By doing so, we are able to provide a highly accurate and precise hotspot identification, applicable to a wide range of applications. We created a novel large thermal image dataset to address the issue of paucity of easily accessible thermal images. Our experiments with the dataset created by us and a publicly available segmentation dataset show the potential of our approach for hotspot detection and its ability to isolate hotspots with high accuracy. We achieve a Dice Coefficient of 0.736, the highest when compared with existing hotspot identification techniques. Our experiments also show self-supervised learning as a strong contender of supervised learning, providing competitive metrics for hotspot detection, with the highest accuracy of our approach being 97%.
Extracting Koopman Operators for Prediction and Control of Non-linear Dynamics Using Two-stage Learning and Oblique Projections

The Koopman operator framework provides a perspective that non-linear dynamics can be described through the lens of linear operators acting on function spaces. As the framework naturally yields linear embedding models, there have been extensive efforts to utilize it for control, where linear controller designs in the embedded space can be applied to control possibly nonlinear dynamics. However, it is challenging to successfully deploy this modeling procedure in a wide range of applications. In this work, some of the fundamental limitations of linear embedding models are addressed. We show a necessary condition for a linear embedding model to achieve zero modeling error, highlighting a trade-off relation between the model expressivity and a restriction on the model structure to allow the use of linear systems theories for nonlinear dynamics. To address these limitations, neural network-based modeling is proposed based on linear embedding with oblique projection, which is derived from a weak formulation of projection-based linear operator learning. We train the proposed model using a two-stage learning procedure, wherein the features and operators are initialized with orthogonal projection, followed by the main training process in which test functions characterizing the oblique projection are learned from data. The first stage achieves an optimality ensured by the orthogonal projection and the second stage improves the generalizability to various tasks by optimizing the model with the oblique projection. We demonstrate the effectiveness of the proposed method over other data-driven modeling methods by providing comprehensive numerical evaluations where four tasks are considered targeting three different systems.
Fixed points for $F$-expanding mappings in the sense of measures of noncompactness

In this article, we model with measures of noncompactness the well-known concept of F-expanding mappings given by Gornicki (Fixed Point Theory Appl 2017, 9 (2016)). Our results are proved by weakening some assumptions on F and without using the surjectivity. Moreover, the paper contains some examples and an application to the Volterra-integral equation.
CS-Mixer: A Cross-Scale Vision MLP Model with Spatial-Channel Mixing

Despite their simpler information fusion designs compared with Vision Transformers and Convolutional Neural Networks, Vision MLP architectures have demonstrated strong performance and high data efficiency in recent research. However, existing works such as CycleMLP and Vision Permutator typically model spatial information in equal-size spatial regions and do not consider cross-scale spatial interactions. Further, their token mixers only model 1- or 2-axis correlations, avoiding 3-axis spatial-channel mixing due to its computational demands. We therefore propose CS-Mixer, a hierarchical Vision MLP that learns dynamic low-rank transformations for spatial-channel mixing through cross-scale local and global aggregation. The proposed methodology achieves competitive results on popular image recognition benchmarks without incurring substantially more compute. Our largest model, CS-Mixer-L, reaches 83.2% top-1 accuracy on ImageNet-1k with 13.7 GFLOPs and 94 M parameters.
Unsupervised Domain Adaptation for Anatomical Landmark Detection

Recently, anatomical landmark detection has achieved great progresses on single-domain data, which usually assumes training and test sets are from the same domain. However, such an assumption is not always true in practice, which can cause significant performance drop due to domain shift. To tackle this problem, we propose a novel framework for anatomical landmark detection under the setting of unsupervised domain adaptation (UDA), which aims to transfer the knowledge from labeled source domain to unlabeled target domain. The framework leverages self-training and domain adversarial learning to address the domain gap during adaptation. Specifically, a self-training strategy is proposed to select reliable landmark-level pseudo-labels of target domain data with dynamic thresholds, which makes the adaptation more effective. Furthermore, a domain adversarial learning module is designed to handle the unaligned data distributions of two domains by learning domain-invariant features via adversarial training. Our experiments on cephalometric and lung landmark detection show the effectiveness of the method, which reduces the domain gap by a large margin and outperforms other UDA methods consistently. The code is available at https://github.com/jhb86253817/UDA_Med_Landmark.
Gravitational waves from metastable cosmic strings in the Pati-Salam model in light of new pulsar timing array data

A series of pulsar timing arrays (PTAs) recently observed gravitational waves at the nanohertz frequencies. Motivated by this remarkable result, we present a novel class of Pati-Salam models that give rise to a network of metastable cosmic strings, offering a plausible explanation for the observed PTA data. Besides, we introduce a hybrid inflationary scenario to eliminate magnetic monopoles that arise during the spontaneous symmetry breaking of the Pati-Salam gauge group to the Standard Model. The resulting scalar spectral index is compatible with Planck data, and the tensor-to-scalar ratio is anticipated to be extremely small. Moreover, we incorporate a non-thermal leptogenesis to generate the required baryon asymmetry in our framework. Finally, the gravitational wave spectra generated by the metastable cosmic strings not only correspond to signals observed in recent PTAs, including NANOGrav, but are also within the exploration capacity of both present and future ground-based and space-based experiments.
The structure of molten calcium ferrite under various redox conditions

Laser-heated melts based on the 43CaO–57Fe2O3−x eutectic, close to the calcium ferrite (CF) composition, were measured with high-energy X-ray diffraction using aerodynamic levitation over a range of redox states controlled by CO/CO2 gas atmospheres. The iron–oxygen coordination number was found to rise from 4.4 ± 0.3 at 15% Fe3+ to 5.3 ± 0.3 at 87% Fe3+. Empirical potential structure refinement modelling was used to obtain the ferric and ferrous partial pair distribution functions. It was found that the Fe2+ iron–oxygen coordination number is consistently approximately 10% higher in CF than in pure iron oxide, while Fe3+ is essentially identical in all but the most oxygen-rich environments (where it is higher in CF compared with FeOx). The model also shows calcium octahedra to be the dominant species across all redox environments, although the population of CaO7 increases with the availability of oxygen at the expense of CaO4 and CaO5. This article is part of the theme issue ‘Exploring the length scales, timescales and chemistry of challenging materials (Part 1)’.
In search of a unifying theory of white matter aging: improving the understanding of tract-wise degeneration using multi-parametric signatures of morphometry and microstructure

While tract-wise differences in volume and microstructure are common targets of investigation in age-related changes in the white matter (WM), there has been relatively little exploration into other attributes of tract morphometry or its relation to microstructure in vivo, and limited understanding on how they jointly inform the interpretation of the WM aging trajectory. This study examines ten WM tracts for tract-wise differences in morphometry (i.e. volume, length, and volume-to-length ratio) and microstructural integrity (i.e. fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) using diffusion MRI data from the Human Connectome Project in Aging (HCP-A) with the goal of laying the foundation for a unified model of age-related WM microstructure-morphometry trajectories with a special focus on sex differences. Results indicated widely heterogeneous patterns of decline and morphometry-microstructural associations across tracts. Multi-parametric signatures of decline suggest stages or mechanisms of degeneration that differ between sexes. This work highlights the value of integrating microstructural and morphometric measures of WM health instead of observing them separately, suggesting multiple modes of WM degeneration.
Pan-cancer analysis of the PDE4DIP gene with potential prognostic and immunotherapeutic values in multiple cancers including acute myeloid leukemia

Abstract Phosphodiesterase 4D interacting protein (PDE4DIP) interacts with cAMP-specific phosphodiesterase 4D and its abnormal expression promotes the development of hematological malignancies, breast cancer, and pineal cell carcinoma. However, there is currently no systematic pan-cancer analysis of the association between PDE4DIP and various cancers. Thus, this study aimed to elucidate the potential functions of PDE4DIP in various cancers. Based on the multiple public databases and online websites, we conducted comprehensive analyses for PDE4DIP in various cancers, including differential expression, prognosis, genetic variation, DNA methylation, and immunity. We thoroughly analyzed the specific role of PDE4DIP in acute myeloid leukemia (LAML). The results indicated that there were differences in PDE4DIP expression in cancers, and in kidney chromophobe, LAML, pheochromocytoma and paraganglioma, thymoma, and uveal melanoma, PDE4DIP had potential prognostic value. PDE4DIP expression was also correlated with genetic variation, DNA methylation, immune cell infiltration, and immune-related genes in cancers. Functional enrichment analysis showed that PDE4DIP was mainly related to immune-related pathways in cancers, and in LAML, PDE4DIP was mainly related to immunoglobulin complexes, T-cell receptor complexes, and immune response regulatory signaling pathways. Our study systematically revealed for the first time the potential prognostic and immunotherapeutic value of PDE4DIP in various cancers, including LAML.
Spheroid architecture strongly induces miR-221/222 expression and promotes oxidative phosphorylation and survival of mobile tumor cells through a mechanism that includes restriction of miR-9 expression

Tumor cell spheroids are three dimensional multicellular structures that form during the expansive growth of carcinoma cells. Spheroids support tumor metastasis as vehicles of dissemination, promoting growth and survival of bulk tumor and cancer stem cells within the mobile tumor cell population. Deciphering how spheroid architecture affects tumor cell phenotype will be essential for the development of therapeutics to inhibit transperitoneal metastasis and the development of peritoneal carcinomatosis by ovarian cancers. We investigated how spheroid formation directly affects OXPHOS activity and microRNA expression in a cultured ovarian carcinoma cell system. The rate of oxidative phosphorylation/respiration per cell in spheroids was nearly double that of the same cell type growing in suspension as single cells. Cells growing as spheroids showed greatly enhanced expression of miR-221/222, an oncomiR that targets multiple tumor suppressor genes, promotes invasion, as well as reduced expression of miR-9, which targets mitochondrial tRNA-modification enzymes and inhibits OXPHOS. Consistent with the greater efficiency of ATP generation afforded by OXPHOS phosphorylation, tumor cells growing as spheroids injected into the nutrient-poor environment of the murine peritoneum survived longer than the cells growing in suspension as loosely associated aggregates. The data suggest that in addition to the reported effects of spheroid formation on cancer cell growth and phenotype, including promotion of stem cell generation, spheroid architecture increases the OXPHOS activity of constituent tumor cells. During the mobile phase of metastasis, when ovarian tumor cells disperse through nutrient-poor environments such as the peritoneum, enhanced OXPHOS activity afforded by spheroid architecture would enhance survival and thereby contribute to metastatic potential.
A Systematic Review of Experimental Studies Evaluating Antiracist Program Techniques for Children and Adolescents

Abstract:This systematic review synthesizes studies that used experimental designs to evaluate techniques theorized to foster the development of antiracism among youth in school settings (19 published reports; 23 independent studies; participant ages 3–19 years old, primarily White). Our goal herein was to identify unique program components, design elements, and outcome measures; and to critically evaluate the existing studies in terms of potential public health impact. A number of specific programming elements were distilled that may be included in future interventions. Overall, interventions that leveraged cognitive and educational components to help increase positive outgroup contact seemed most promising. However, most of the studies testing such programs lacked methodological robustness (e.g., probable gaps in internal validity from the absence of intervention manuals or equivalent, fidelity checks, reliance on outcome measures with unknown psychometric properties, and follow-up designs). Future research would benefit from establishing adherence to implementation (fidelity to protocol), including pre, post, and follow-up assessments, as well as using outcome measures appropriate for determining both short-term and long-term change. There is a clear need for the funding of technique development, manualized programming for delivery, rigorous evaluation of these with standardized outcome measures, and adequately powered studies testing outcomes across development.
Evaluation of Microbiological Quality of Raw Milk from Farmers and Dairy Producers in Six Districts of Djibouti

Objectives: The present study was carried out to evaluate the microbial quality of raw milk taken at different sampling points from farmers in Ali Sabieh, Arta, Dikhil, Djibouti, Obock and Tadjourah districts of Djibouti. Methods: Two hundred samples of milk extracted from goats, cows and camels were collected and analyzed for the presence and enumeration of microorganisms using standard methods. Total bacterial counts exceeded the national standard in 56% of collected samples and Tadjourah district had higher milk samples (82%) with unacceptable total bacterial count. Results: Key data demonstrated the presence of food-borne microorganisms. The mean value of aerobic mesophilic bacteria (6.78 log cfu mL-1), coliform counts (3.91 cfu mL-1) and E. coli count (2.58 cfu mL-1) was higher than maximum recommended value. The count of yeast and moulds (5.54 log cfu mL-1) was also very high. Salmonella spp. (0.10 log cfu mL-1) was only detected in the samples from Ali Sabieh district whereas Staphylococcus aureus and spore of Clostridium spp. were below the detection level in all samples. Count of Brucella spp. and Mycobacteria were 0.56 log cfu mL-1 and 0.86 log cfu mL-1 respectively suggesting the existence of mastitic animals. Pearson correlation matrix of microorganism’s distribution in raw milk showed highest positive correlation between Yeast and moulds and Streptococci at r=0.929. Analysis in principal components exhibited the variability of microbial groups with 80.797% cumulative values of the variance and Eigen values ranging between 1.553 and 3.622. Conclusion: The data suggested that raw milk samples collected from the six districts were non suitable quality and most of bacteria identified in the milk sampled could be potential food-borne pathogens.
Snow mechanical properties variability at the slope scale, implication for snow mechanical modeling

Snow avalanches represent a natural hazard for infrastructures and backcountry recreationists. Risk assessment of avalanche hazard is difficult due to the sparse nature of available observations informing on snowpack mechanical and geo-physical properties and overall stability. The spatial variability of these properties also adds complexity to the decision-making and route finding in avalanche terrain for mountain users. Snow cover models simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5-50 5 m) remains critical, since slope stability and the possible size of an avalanche are governed by such a scale. To better understand and estimate the spatial variability at the slope scale, this work explores existing links between snow mechanical properties and microtopographic indicators. First, we compared the covariance models and the scaling properties. Then, we estimated snow mechanical properties, including point snow stability, using GAM spatial models (Generalized Additives Models) with microtopographic indicators as covariates. Snow mechanical properties such as snow density, elastic modulus, shear modulus 10 and snow microstructural strength were measured at multiple locations over several studied slopes using a high-resolution snow penetrometer (SMP), in Rogers Pass, British-Columbia, and Mt Albert, Québec. Point snow stability such as the skier crack length, critical propagation crack length and a skier stability index were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators such as the topographic position index (TPI), vegetation height and proximity, Up-wind slope index (wind exposed/sheltered area) and potential radiation index were derived from Unmanned Aerial 15 Vehicles (UAV) surveys with sub-meter resolution. We computed the variogram and log-log variogram of snow mechanical properties. The comparison showed some similarities in the correlation distances and fractal dimensions between the slab thickness and the slab snow density and also between the weak layer microstructural strength and the stability metrics. GAM models suggested several significant covariates such as TPI, VRM, Winstral index, vegetation height and distance to vegetation. The snow stability maps that were generated represent good teaching material in avalanche skill training and awareness courses. 20 The difference in spatial pattern between the slab and the weak layer should be considered in snow mechanical modeling.
Some results on second transpose of a dual valued derivation

Let $A$ be a Banach algebra and $X$ be an arbitrary Banach $A$-module. In this paper, we study the second transpose of derivations with value in dual Banach $A$-module $X^{*}. $ Indeed, for a continuous derivation $D:Alongrightarrow X^{*}$ we obtain a necessary and sufficient condition such that the bounded linear map $Lambdacirc D^{primeprime}:A^{**}longrightarrow X^{***}$ to be a derivation, where $Lambda$ is composition of restriction and canonical injection maps. This characterization generalizes some well known results in cite{aef}.
Sulla varietà delle curve algebriche piane spezzate in un dato modo

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