Miracle-dz/UniFAR-FTUs huggingface dataset:
Biocompatibility characterisation of CMOS-based Lab-on-Chip electrochemical sensors for in vitro cancer cell culture applications

Lab-on-Chip electrochemical sensors, such as Ion-Sensitive Field-Effect Transistors (ISFETs), are being developed for use in point-of-care diagnostics, such as pH detection of tumour microenvironments, due to their integration with standard Complementary Metal Oxide Semiconductor technology. With this approach, the passivation of the CMOS process is used as a sensing layer to minimise post-processing, and Silicon Nitride (Si3N4) is the most common material at the microchip surface. ISFETs have the potential to be used for cell-based assays however, there is a poor understanding of the biocompatibility of microchip surfaces. Here, we quantitatively evaluated cell adhesion, morphogenesis, proliferation and mechano-responsiveness of both normal and cancer cells cultured on a Si3N4, sensor surface. We demonstrate that both normal and cancer cell adhesion decreased on Si3N4. Activation of the mechano-responsive transcription regulators, YAP/TAZ, are significantly decreased in cancer cells on Si3N4 in comparison to standard cell culture plastic, whilst proliferation marker, Ki67, expression markedly increased. Non-tumorigenic cells on chip showed less sensitivity to culture on Si3N4 than cancer cells. Treatment with extracellular matrix components increased cell adhesion in normal and cancer cell cultures, surpassing the adhesiveness of plastic alone. Moreover, poly-l-ornithine and laminin treatment restored YAP/TAZ levels in both non-tumorigenic and cancer cells to levels comparable to those observed on plastic. Thus, engineering the electrochemical sensor surface with treatments will provide a more physiologically relevant environment for future cell-based assay development on chip.
Cross-Comparison of Gut Metagenomic Profiling Strategies

A critical issue in microbiome research is the selection of reliable laboratory and bioinformatics pipelines. In the absence of generally accepted technical benchmarks and evaluation standards, comparing data generated by different studies becomes challenging. In this work, we carried out the most comprehensive study to date on this topic. We encompassed every stage of processing, from DNA extraction to computational assessment. We adopted four procedures for DNA purification, six for library construction, three for sequencing, and five for bioinformatics. Additionally, we used datasets published by others to corroborate our results. We introduced a software tool that distinctively delivers consistent results, irrespective of sample or dataset origins. This study underscores the importance of methodological optimization at the outset of research projects to ensure the reliability of results and their comparability with findings from other studies. Additionally, this study provides an optimized robust pipeline for gut microbiome analysis.
Variations of the Northern Hemisphere atmospheric energetics: 1948-2000

Abstract Many studies have shown evidence of a major climate change in the late 1970s and early 1980s. The change comprises a reversal of the sea surface temperature anomaly pattern in the North Pacific Ocean, a lowering of the atmospheric geopotential height in the North Pacific, altered frequency and intensity of cyclones/anticyclones and severe storms in the mid-and high latitudes, along with an “abrupt” increase of the Northern Hemisphere (NH) average surface air temperature. What do these changes mean in terms of the nature of the NH atmospheric circulation in the warmer climate? Do they indicate changes of the atmospheric energy cycle and conversion/ exchange between various energy forms in the atmosphere? In which latitude regions are such changes in the energetics most significant? These are some of the questions examined in this study for the period 1948–2000 using the NCEP–NCAR reanalysis data. Major results of this study show significant increases of both the NH mean and eddy kinetic energy in .
Impalas in an elephant-impacted woodland : browser-driven dynamics of the Chobe riparian zone, northern Botswana

Elephants have been transforming the Chobe Riverfront vegetation from a woodland state to a shrubland state. Subsequently along with the transformation certain woody species have shown low regeneration. Elephants have earlier been reported not to be an important factor directly affecting woody species regeneration, suggesting that other factors, such as fire and browsing ungulates may be responsible for the observed low woody species regeneration. There has been no fire along the Chobe Riverfront for the past decades, pointing to browsing ungulates as most plausible factor suppressing of woody species regeneration. Although most ungulates along the Chobe Riverfront have been either decreasing or stabilizing, the impala population has been increasing. This thesis tested the prediction that impalas invade elephantmodified habitats and subsequently retard regeneration of certain woody species. Specifically, the thesis address the following questions: 1) how has the riparian woodland plant community been influenced by elephant impact, 2) how has the Chobe impala population responded to the recovering of elephant population and associated woodland changes, 3) how are patterns of resource use by Chobe impalas associated with patterns of elephant impacts on vegetation, and 4) can impala herbivory restrict woodland regeneration in the Chobe riparian woodland. Generally, densities, species diversities, functional diversity and functional dominance of woody species were higher on sites that were lightly disturbed than sites that were heavily disturbed by elephants. On each site dominant species had functionally minors species. Effect of elephant disturbance on dominant species was influenced by soil type. On Kalahari sandy soils, 75% of species dominating on lightly disturbed sites decreased in abundance on heavily disturbed sites. Of these decreasing dominant species, only 33% had functionally similar species that were mionor on lightly disturbed sites increasing in abundance on heavily disturbed sites. On alluvial soils, 20% of species dominating on lightly disturbed sites decreased in abundance on heavily disturbed sites, and had functionally similar species which were mionor on lightly disturbed sites increasing in abundance on heavily disturbed sites. Along the Chobe Riverfront the impala population increased with increasing openness of the woodland, and used elephant-modified habitat more than relatively unmodified habitats. Within 2 km of Chobe River the capita growth rate of elephants increased at an increasing rate until 1980s when it started to increase at a decreasing rate. Meanwhile, the capita growth of impala increased at a slow rate until 1980s when it started to increase rapidly. During the
Origin of Exceptionally Abundant Phonolites on Ua Pou Island (Marquesas, French Polynesia): Partial Melting of Basanites Followed by Crustal Contamination

On the basis of the first systematic mapping of Ua Pou, long known for its exceptionally abundant phonolites, we estimate that these rocks cover 65% of the surface of the island whereas mafic lavas cover 27% and intermediate ones 8%. The silica-undersaturated suite was erupted in a restricted time span (2 9–2 35Myr), following the emplacement of tholeiites derived from a young HIMU-type source at c. 4Ma. Primitive basanites, derived from a heterogeneous mantle source with a dominant EM II HIMU signature, represent likely parental magmas. The series is characterized by a Daly gap defined by a lack of phonotephrites. We consider that the most likely model for the origin of evolved lavas is partial melting at depth of primitive basanites, leaving an amphibole-rich residuum and producing tephriphonolitic magmas. These tephriphonolitic magmas may have evolved by closed-system fractional crystallization towards Group A phonolites. Three other groups of phonolites could have been derived from tephriphonolitic magmas by open-system fractional crystallization processes, characterized respectively by seawater contamination (Group B), assimilation of nepheline syenite-type materials (Group C) and extreme fractionation coupled with assimilation of the underlying oceanic crust (Group D). The prominence of evolved lavas is a consequence of their origin from partial melting of mafic precursors followed by crustal contamination.
Rock magnetic and magnetic fabric variations across three thin alkaline dykes from Sunnhordland, Western Norway; influence of initial mineralogy and secondary chemical alterations

SUMMARY Profiles of natural remanent magnetization (NRM), rock magnetic properties and anisotropy of magnetic susceptibility (AMS) across three thin (26 to 109 cm) alkaline dykes from Sunnhordland, Western Norway are presented. The magnetic remanence is carried by pseudo-single-domain (PSD) lowtemperature oxidized titanomagnetite, with circa 6 per cent of cations substituted by aluminium. Systematic variations in magnetic properties across the dykes are caused by differences in titanomagnetite concentration as well as grain size. Lowtemperature alteration and laboratory oxidation by heating in air to a maximum temperature of 600 "C, do not homogenize the magnetic properties across the dykes. Fine-grained regions near dyke margins carry the most stable remanent magnetization, but are also most amenable to secondary chemical remanent magnetization (CRM) resetting. Between dyke variations in magnetic properties are larger than within dyke variations. A stable primary remanence is found in two of the dykes, while the third is totally reset by a soft viscous remanent magnetization (VRM), despite similar optical grain sizes, magnetic mineralogy and oxidation state. The difference is attributed to a lower titanium content in the latter dyke, preventing effective partitioning of magnetic grains during low-temperature alteration. Laboratory heating affects anisotropy variations across the dykes, resulting in more consistent axis orientations within each dyke, and symmetric edge-to-edge variations in the degree of anisotropy. AMS directions are different from those in the country rock, and vary systematically across the thinnest dyke before heating.
Sorghum Productivity in Mali: Past, Present, and Future

Mali has a long history of focusing agricultural research and policies on the cereal sector, as cereals are the major staples providing food security. Despite the overall success of Malian cereal research and market reforms, recent production and productivity trends for traditional coarse grains (millet and sorghum) have grown at a much slower pace than rice and maize. This literature review describes how Mali is currently performing in terms of sorghum productivity, how the sector got to where it is today, and issues that need to be addressed to further its development. Sorghum is the focus of the review because of its adaptability to a variety of climates and the role it plays in providing food security to semi-subsistent rural households.
Weakly-Supervised 3D Reconstruction of Clothed Humans via Normal Maps

We present a novel deep learning-based approach to the 3D reconstruction of clothed humans using weak supervision via 2D normal maps. Given a single RGB image or multiview images, our network infers a signed distance function (SDF) discretized on a tetrahedral mesh surrounding the body in a rest pose. Subsequently, inferred pose and camera parameters are used to generate a normal map from the SDF. A key aspect of our approach is the use of Marching Tetrahedra to (uniquely) compute a triangulated surface from the SDF on the tetrahedral mesh, facilitating straightforward differentiation (and thus backpropagation). Thus, given only ground truth normal maps (with no volumetric information ground truth information), we can train the network to produce SDF values from corresponding RGB images. Optionally, an additional multiview loss leads to improved results. We demonstrate the efficacy of our approach for both network inference and 3D reconstruction.
GLIME: General, Stable and Local LIME Explanation

As black-box machine learning models grow in complexity and find applications in high-stakes scenarios, it is imperative to provide explanations for their predictions. Although Local Interpretable Model-agnostic Explanations (LIME) [22] is a widely adpoted method for understanding model behaviors, it is unstable with respect to random seeds [35,24,3] and exhibits low local fidelity (i.e., how well the explanation approximates the model's local behaviors) [21,16]. Our study shows that this instability problem stems from small sample weights, leading to the dominance of regularization and slow convergence. Additionally, LIME's sampling neighborhood is non-local and biased towards the reference, resulting in poor local fidelity and sensitivity to reference choice. To tackle these challenges, we introduce GLIME, an enhanced framework extending LIME and unifying several prior methods. Within the GLIME framework, we derive an equivalent formulation of LIME that achieves significantly faster convergence and improved stability. By employing a local and unbiased sampling distribution, GLIME generates explanations with higher local fidelity compared to LIME. GLIME explanations are independent of reference choice. Moreover, GLIME offers users the flexibility to choose a sampling distribution based on their specific scenarios.
Surface mapping class group actions on 3-manifolds

For each circle bundle $S^1\to X\to\Sigma_g$ over a surface with genus $g\ge2$, there is a natural surjection $\pi:Homeo^+(X)\to Mod(\Sigma_g)$. When $X$ is the unit tangent bundle $U\Sigma_g$, it is well-known that $\pi$ splits. On the other hand $\pi$ does not split when the Euler number $e(X)$ is not divisible by the Euler characteristic $\chi(\Sigma_g)$ by work of the second two authors. In this paper we show that this homomorphism does not split in many cases where $\chi(\Sigma_g)$ divides $e(X)$.
Quasi-optimal Discontinuous Galerkin discretisations of the p-Dirichlet problem

The classical arguments employed when obtaining error estimates of Finite Element (FE) discretisations of elliptic problems lead to more restrictive assumptions on the regularity of the exact solution when applied to non-conforming methods. The so-called minimal regularity estimates available in the literature relax some of these assumptions, but are not truly of -minimal regularity-, since a data oscillation term appears in the error estimate. Employing an approach based on a smoothing operator, we derive for the first time error estimates for Discontinuous Galerkin (DG) type discretisations of non-linear problems with $(p,\delta)$-structure that only assume the natural $W^{1,p}$-regularity of the exact solution, and which do not contain any oscillation terms.
GAIA: Zero-shot Talking Avatar Generation

Zero-shot talking avatar generation aims at synthesizing natural talking videos from speech and a single portrait image. Previous methods have relied on domain-specific heuristics such as warping-based motion representation and 3D Morphable Models, which limit the naturalness and diversity of the generated avatars. In this work, we introduce GAIA (Generative AI for Avatar), which eliminates the domain priors in talking avatar generation. In light of the observation that the speech only drives the motion of the avatar while the appearance of the avatar and the background typically remain the same throughout the entire video, we divide our approach into two stages: 1) disentangling each frame into motion and appearance representations; 2) generating motion sequences conditioned on the speech and reference portrait image. We collect a large-scale high-quality talking avatar dataset and train the model on it with different scales (up to 2B parameters). Experimental results verify the superiority, scalability, and flexibility of GAIA as 1) the resulting model beats previous baseline models in terms of naturalness, diversity, lip-sync quality, and visual quality; 2) the framework is scalable since larger models yield better results; 3) it is general and enables different applications like controllable talking avatar generation and text-instructed avatar generation.
Sparse Pedestrian Character Learning for Trajectory Prediction

Pedestrian trajectory prediction in a first-person view has recently attracted much attention due to its importance in autonomous driving. Recent work utilizes pedestrian character information, \textit{i.e.}, action and appearance, to improve the learned trajectory embedding and achieves state-of-the-art performance. However, it neglects the invalid and negative pedestrian character information, which is harmful to trajectory representation and thus leads to performance degradation. To address this issue, we present a two-stream sparse-character-based network~(TSNet) for pedestrian trajectory prediction. Specifically, TSNet learns the negative-removed characters in the sparse character representation stream to improve the trajectory embedding obtained in the trajectory representation stream. Moreover, to model the negative-removed characters, we propose a novel sparse character graph, including the sparse category and sparse temporal character graphs, to learn the different effects of various characters in category and temporal dimensions, respectively. Extensive experiments on two first-person view datasets, PIE and JAAD, show that our method outperforms existing state-of-the-art methods. In addition, ablation studies demonstrate different effects of various characters and prove that TSNet outperforms approaches without eliminating negative characters.
The use and value of citizen science data in New Zealand

There has been significant growth worldwide of citizen science projects involving community members collecting environmental data. The following study is based on questionnaire responses from 296 community environmental groups and interviews with 34 project partners (e.g. resource managers and scientists) and examines the use and value of citizen science data in New Zealand. Frequency counts and inductive thematic analysis were used to examine the quantitative and qualitative data, respectively. Groups reported using their data to support funding applications (63%; n = 151), inform restoration management decision-making (60%; n = 151) and for educational purposes (48%; n = 157). Outcomes such as relationship-building with project partners and increasing environmental knowledge highlighted the transformational nature of groups’ citizen science activities. Although groups reported providing data to project partners (60%; n = 151), concerns were expressed over data quality and a lack of institutional systems for using the data. Project partners, however, proposed novel solutions for enhancing collaboration with groups to produce useful data, underscoring the value they attributed to groups’ citizen science efforts.
High-fidelity spin qubit shuttling via large spin-orbit interaction

Shuttling spins with high fidelity is a key requirement to scale up semiconducting quantum computers, enabling qubit entanglement over large distances and favoring the integration of control electronics on-chip. To decouple the spin from the unavoidable charge noise, state-of-the-art spin shuttlers try to minimize the inhomogeneity of the Zeeman field. However, this decoupling is challenging in otherwise promising quantum computing platforms such as hole spin qubits in silicon and germanium, characterized by a large spin-orbit interaction and electrically-tunable qubit frequency. In this work, we show that, surprisingly, the large inhomogeneity of the Zeeman field stabilizes the coherence of a moving spin state, thus enabling high-fidelity shuttling also in these systems. We relate this enhancement in fidelity to the deterministic dynamics of the spin which filters out the dominant low-frequency contributions of the charge noise. By simulating several different scenarios and noise sources, we show that this is a robust phenomenon generally occurring at large field inhomogeneity. By appropriately adjusting the motion of the quantum dot, we also design realistic protocols enabling faster and more coherent spin shuttling. Our findings are generally applicable to a wide range of setups and could pave the way toward large-scale quantum processors.
GPT4Vis: What Can GPT-4 Do for Zero-shot Visual Recognition?

This paper does not present a novel method. Instead, it delves into an essential, yet must-know baseline in light of the latest advancements in Generative Artificial Intelligence (GenAI): the utilization of GPT-4 for visual understanding. Our study centers on the evaluation of GPT-4's linguistic and visual capabilities in zero-shot visual recognition tasks: Firstly, we explore the potential of its generated rich textual descriptions across various categories to enhance recognition performance without any training. Secondly, we evaluate GPT-4's visual proficiency in directly recognizing diverse visual content. We conducted extensive experiments to systematically evaluate GPT-4's performance across images, videos, and point clouds, using 16 benchmark datasets to measure top-1 and top-5 accuracy. Our findings show that GPT-4, enhanced with rich linguistic descriptions, significantly improves zero-shot recognition, offering an average top-1 accuracy increase of 7% across all datasets. GPT-4 excels in visual recognition, outshining OpenAI-CLIP's ViT-L and rivaling EVA-CLIP's ViT-E, particularly in video datasets HMDB-51 and UCF-101, where it leads by 22% and 9%, respectively. We hope this research contributes valuable data points and experience for future studies. We release our code at https://github.com/whwu95/GPT4Vis.
Quantum Carnot thermal machines re-examined: Definition of efficiency and the effects of strong coupling

Whether the strong coupling to thermal baths can improve the performance of quantum thermal machines remains an open issue under active debate. Here, we revisit quantum thermal machines operating with the quasi-static Carnot cycle and aim to unveil the role of strong coupling in maximum efficiency. Our analysis builds upon definitions of excess work and heat derived from an exact formulation of the first law of thermodynamics for the working substance, which captures the non-Gibbsian thermal equilibrium state that emerges at strong couplings during quasi-static isothermal processes. These excess definitions differ from conventional ones by an energetic cost for maintaining the non-Gibbsian characteristics. With this distinction, we point out that one can introduce two different yet thermodynamically allowed definitions for efficiency of both the heat engine and refrigerator modes. We dub them excess and hybrid definitions which differ in the way of defining the gain for the thermal machines at strong couplings by either just analyzing the energetics of the working substance or instead evaluating the performance from an external system upon which the thermal machine acts, respectively. We analytically demonstrate that the excess definition predicts that the Carnot limit remains the upper bound for both operation modes at strong couplings, whereas the hybrid one reveals that strong coupling can suppress the maximum efficiency rendering the Carnot limit unattainable. These seemingly incompatible predictions thus indicate that it is imperative to first gauge the definition for efficiency before elucidating the exact role of strong coupling, thereby shedding light on the on-going investigation on strong-coupling quantum thermal machines.