Miracle-dz/UniFAR-FTUs huggingface dataset:
Comment on “Why does surface ozone peak in summertime at Waliguan?” by Bin Zhu et al.

[1] Zhu et al. [2004] investigated seasonal variation of boundary layer ozone over East Asia using a regional chemical transport model and concluded that the distinct seasonal variation of surface ozone (summer maximum and winter minimum) at the Waliguan station (WLG), which is located on the northeastern edge of the Tibetan Plateau, is caused by transport of ozone from eastern/central China, Central/South Asia and even Europe. But this paper has shortcomings as follows: (1) Contributions of proposed source regions, eastern/central China in particular, to ozone at WLG was not specifically simulated and (2) it ignores the effect of convection on the vertical redistribution of ozone over the Tibetan Plateau.
Expression quantitative trait loci analysis in rheumatoid arthritis identifies tissue specific variants associated with severity and outcome

Objective Genome-wide association studies have successfully identified more than 100 loci associated with susceptibility to rheumatoid arthritis (RA). However, our understanding of the functional effects of genetic variants in causing RA and their effects on disease severity and response to treatment remains limited. Methods In this study, we conducted expression quantitative trait locus (eQTL) analysis to dissect the link between genetic variants and gene expression comparing the disease tissue against blood using RNA-Sequencing of synovial biopsies (n=85) and blood samples (n=51) from treatment-naïve patients with RA from the Pathobiology of Early Arthritis Cohort. Results This identified 898 eQTL genes in synovium and genes loci in blood, with 232 genes in common to both synovium and blood, although notably many eQTL were tissue specific. Examining the HLA region, we uncovered a specific eQTL at HLA-DPB2 with the critical triad of single-nucleotide polymorphisms (SNPs) rs3128921 driving synovial HLA-DPB2 expression, and both rs3128921 and HLA-DPB2 gene expression correlating with clinical severity and increasing probability of the lympho-myeloid pathotype. Conclusions This analysis highlights the need to explore functional consequences of genetic associations in disease tissue. HLA-DPB2 SNP rs3128921 could potentially be used to stratify patients to more aggressive treatment immediately at diagnosis.
Effects of non-invasive ventilation on sleep in chronic hypercapnic respiratory failure

Chronic respiratory disease can exacerbate the normal physiological changes in ventilation observed in healthy individuals during sleep, leading to sleep-disordered breathing, nocturnal hypoventilation, sleep disruption and chronic respiratory failure. Therefore, patients with obesity, slowly and rapidly progressive neuromuscular disease and chronic obstructive airways disease report poor sleep quality. Non-invasive ventilation (NIV) is a complex intervention used to treat sleep-disordered breathing and nocturnal hypoventilation with overnight physiological studies demonstrating improvement in sleep-disordered breathing and nocturnal hypoventilation, and clinical trials demonstrating improved outcomes for patients. However, the impact on subjective and objective sleep quality is dependent on the tools used to measure sleep quality and the patient population. As home NIV becomes more commonly used, there is a need to conduct studies focused on sleep quality, and the relationship between sleep quality and health-related quality of life, in all patient groups, in order to allow the clinician to provide clear patient-centred information.
Dynamic Changes of Magnetopause Locations Under Different Upstream Conditions

Prediction of the magnetopause location in a broad range of upstream parameters can be considered as a test of our understanding of the solar wind- magnetosphere coupling. Present magnetopause models describe the magnetopause location as a function of the solar wind dynamic pressure and IMF BZ component. However, the crossings observed by spacecraft are often far away from the predicted locations and it suggests that other IMF components and/or other factors can be important under particular circumstances. We have found that magnetopause location strongly dependence on the IMF cone angle.
Crucial role of hsa-mir-503, hsa-mir-1247, and their validation in prostate cancer

Background: Prostate cancer (PC) is a common urinary system malignancy, and advanced PC patients had a poor prognosis due to recurrence or distant metastasis. Therefore, it’s imperative to reveal more details in tumorigenesis and prognosis of PC patients. Methods: The miRNA and mRNA expression profile data of 485 PC patients were obtained from The Cancer Genome Atlas database. The univariate Cox regression was applied to screen miRNAs relating to prognosis of PC. Then miRTarBase was used to predict target mRNAs of miRNAs. The hsa-mir-503/hsa-mir-1247 knockdown in 22RV1 cells was established to evaluate the effect of these two miRNAs on tumor cell migration and invasion ability. Flow cytometry was used to detect the effect of hsa-mir-503/hsa-mir-1247 knockdown on 22RV1 apoptosis rate. Results: Univariate Cox regression analysis identified hsa-mir-503 as a poor and hsa-mir-1247 as a favorable prognostic marker. Totally 649 target mRNAs were screened, among which DUSP19, FGF2, and SLC2A5 had a negative correlation with hsa-mir-503, while FGF2 and VSTM4 had a positive correlation with hsa-mir-1247. In 22RV1 cells, hsa-mir-503 was up-regulated, and hsa-mir-1247 was down-regulated. hsa-mir-503 knockdown attenuated the migration and invasion of 22RV1 cells, while hsa-mir-1247 knockdown exhibited the opposite effect. In addition, hsa-mir-503 knockdown promoted 22RV1 cell apoptosis. hsa-mir-1247 overexpression significantly inhibited the tumor growth of PC in vivo. Conclusions: Herein, we demonstrated that hsa-mir-503 and hsa-mir-1247 could serve as new prognostic markers of PC, and hsa-mir-1247 had great potential to inhibit PC progression by suppressing the migration and invasion ability in vitro and in vivo.
Glass eel recruitment and exploitation in a South European estuary (Oria, Bay of Biscay)

The size of the European eel population is below safe biological limits. To assess the status of the stock properly, it is necessary to increase the number of recruitment series based on scientific monitoring. In this study, the spatio-temporal variation in glass eel density in a South European Atlantic estuary, the Oria, has been examined using experimental fisheries and fishery data. Glass eel density was predicted using a mixed generalized additive model. Current and depth were selected as covariates and date as a random variable then extrapolated to the whole sampling point volume to obtain the daily recruitment (mean, 12.76 kg; range, 0–72.8 kg). The average seasonal daily recruitment and fishery data were combined to obtain the seasonal recruitment (mean, 1144 kg; range, 682–1593 kg) and exploitation rate (mean, 31.1%; range, 6.2–48.7%). The information of spatio-temporal dynamics in glass eel density gathered in this study will help to improve the design of a recruitment-monitoring scheme at the European level. The integration of glass eel fishery data and scientific estimates is crucial to obtain a recruitment index in the Bay of Biscay, the area with the largest glass eel recruitment in Europe.
Control of individual electron-spin pairs in an electron-spin bath

The decoherence of a central electron spin due to the dynamics of a coupled electron-spin bath is a core problem in solid-state spin physics. Ensemble experiments have studied the central spin coherence in detail, but such experiments average out the underlying quantum dynamics of the bath. Here, we show the coherent back-action of an individual NV center on an electron-spin bath and use it to detect, prepare and control the dynamics of a pair of bath spins. We image the NV-pair system with sub-nanometer resolution and reveal a long dephasing time ($T_2^* = 44(9)$ ms) for a qubit encoded in the electron-spin pair. Our experiment reveals the microscopic quantum dynamics that underlie the central spin decoherence and provides new opportunities for controlling and sensing interacting spin systems.
Regions are Who Walk Them: a Large Pre-trained Spatiotemporal Model Based on Human Mobility for Ubiquitous Urban Sensing

User profiling and region analysis are two tasks of significant commercial value. However, in practical applications, modeling different features typically involves four main steps: data preparation, data processing, model establishment, evaluation, and optimization. This process is time-consuming and labor-intensive. Repeating this workflow for each feature results in abundant development time for tasks and a reduced overall volume of task development. Indeed, human mobility data contains a wealth of information. Several successful cases suggest that conducting in-depth analysis of population movement data could potentially yield meaningful profiles about users and areas. Nonetheless, most related works have not thoroughly utilized the semantic information within human mobility data and trained on a fixed number of the regions. To tap into the rich information within population movement, based on the perspective that Regions Are Who walk them, we propose a large spatiotemporal model based on trajectories (RAW). It possesses the following characteristics: 1) Tailored for trajectory data, introducing a GPT-like structure with a parameter count of up to 1B; 2) Introducing a spatiotemporal fine-tuning module, interpreting trajectories as collection of users to derive arbitrary region embedding. This framework allows rapid task development based on the large spatiotemporal model. We conducted extensive experiments to validate the effectiveness of our proposed large spatiotemporal model. It's evident that our proposed method, relying solely on human mobility data without additional features, exhibits a certain level of relevance in user profiling and region analysis. Moreover, our model showcases promising predictive capabilities in trajectory generation tasks based on the current state, offering the potential for further innovative work utilizing this large spatiotemporal model.
I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs Quantization

Albeit the scalable performance of vision transformers (ViTs), the dense computational costs (training&inference) undermine their position in industrial applications. Post-training quantization (PTQ), tuning ViTs with a tiny dataset and running in a low-bit format, well addresses the cost issue but unluckily bears more performance drops in lower-bit cases. In this paper, we introduce I&S-ViT, a novel method that regulates the PTQ of ViTs in an inclusive and stable fashion. I&S-ViT first identifies two issues in the PTQ of ViTs: (1) Quantization inefficiency in the prevalent log2 quantizer for post-Softmax activations; (2) Rugged and magnified loss landscape in coarse-grained quantization granularity for post-LayerNorm activations. Then, I&S-ViT addresses these issues by introducing: (1) A novel shift-uniform-log2 quantizer (SULQ) that incorporates a shift mechanism followed by uniform quantization to achieve both an inclusive domain representation and accurate distribution approximation; (2) A three-stage smooth optimization strategy (SOS) that amalgamates the strengths of channel-wise and layer-wise quantization to enable stable learning. Comprehensive evaluations across diverse vision tasks validate I&S-ViT' superiority over existing PTQ of ViTs methods, particularly in low-bit scenarios. For instance, I&S-ViT elevates the performance of 3-bit ViT-B by an impressive 50.68%.
Book review: Unplanned development: tracking change in Asia

"Unplanned Development: Tracking Change in Asia. " Jonathan Rigg. Zed Books. November 2012. --- Unplanned Development offers a fascinating and fresh view into the realities of development planning. To an observer, many development projects present themselves as thoroughly planned endeavours informed by structure, direction and intent, but here Jonathan Rigg aims to expose the truth of development experience: how chance, serendipity, turbulence and the unexpected define development around the world. Reviewed by Hansley A. Juliano.
FRCSyn Challenge at WACV 2024: Face Recognition Challenge in the Era of Synthetic Data

Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology. Specifically, the FRCSyn Challenge targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. The results achieved in the FRCSyn Challenge, together with the proposed benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.
Solar-stellar atmospheric tomography with mm-radio snapshot spectroscopic imaging

Millimter (mm) frequencies are primarily sensitive to thermal emission from layers across the stellar chromosphere up to the transition region, while metrewave (radio) frequencies probe the coronal heights. Together the mm and radio band spectroscopic snapshot imaging enables the tomographic exploration of the active atmospheric layers of the cool main-sequence stars (spectral type: FGKM), including our Sun. Sensitive modern mm and radio interferometers let us explore solar/stellar activity covering a range of energy scales at sub-second and sub-MHz resolution over wide operational bandwidths. The superior uv-coverage of these instruments facilitate high dynamic range imaging, letting us explore the morphological evolution of even energetically weak events on the Sun at fine spectro-temporal cadence. This article will introduce the current advancements, the data analysis challenges and available tools. The impact of these tools and novel data in field of solar/stellar research will be summarised with future prospects.
A large and natural Class of Σp2- and Σp3-complete Problems in Bilevel and Robust Optimization

Because $\Sigma^p_2$- and $\Sigma^p_3$-hardness proofs are usually tedious and difficult, not so many complete problems for these classes are known. This is especially true in the areas of min-max regret robust optimization, network interdiction, most vital vertex problems, blocker problems, and two-stage adjustable robust optimization problems. Even though these areas are well-researched for over two decades and one would naturally expect many (if not most) of the problems occurring in these areas to be complete for the above classes, almost no completeness results exist in the literature. We address this lack of knowledge by introducing over 70 new $\Sigma^p_2$-complete and $\Sigma^p_3$-complete problems. We achieve this result by proving a new meta-theorem, which shows $\Sigma^p_2$- and $\Sigma^p_3$-completeness simultaneously for a huge class of problems. The majority of all earlier publications on $\Sigma^p_2$- and $\Sigma^p_3$-completeness in said areas are special cases of our meta-theorem. Our precise result is the following: We introduce a large list of problems for which the meta-theorem is applicable (including clique, vertex cover, knapsack, TSP, facility location and many more). For every problem on this list, we show: The interdiction/minimum cost blocker/most vital nodes problem (with element costs) is $\Sigma^p_2$-complete. The min-max-regret problem with interval uncertainty is $\Sigma^p_2$-complete. The two-stage adjustable robust optimization problem with discrete budgeted uncertainty is $\Sigma^p_3$-complete. In summary, our work reveals the interesting insight that a large amount of NP-complete problems have the property that their min-max versions are 'automatically' $\Sigma^p_2$-complete.
Critical well-posedness for the 2D Peskin problem with general tension

In this paper, we study the two dimensional Peskin problem with general elasticity law. Specifically, we prove global regularity for small perturbations, in suitable critical spaces, of the circle solution, possibly containing corners. For such initial data we prove asymptotic stability in the sense that as $t\to\infty$, the solution converges to a translated and rotated disk.
3D-TexSeg: Unsupervised Segmentation of 3D Texture using Mutual Transformer Learning

Analysis of the 3D Texture is indispensable for various tasks, such as retrieval, segmentation, classification, and inspection of sculptures, knitted fabrics, and biological tissues. A 3D texture is a locally repeated surface variation independent of the surface's overall shape and can be determined using the local neighborhood and its characteristics. Existing techniques typically employ computer vision techniques that analyze a 3D mesh globally, derive features, and then utilize the obtained features for retrieval or classification. Several traditional and learning-based methods exist in the literature, however, only a few are on 3D texture, and nothing yet, to the best of our knowledge, on the unsupervised schemes. This paper presents an original framework for the unsupervised segmentation of the 3D texture on the mesh manifold. We approach this problem as binary surface segmentation, partitioning the mesh surface into textured and non-textured regions without prior annotation. We devise a mutual transformer-based system comprising a label generator and a cleaner. The two models take geometric image representations of the surface mesh facets and label them as texture or non-texture across an iterative mutual learning scheme. Extensive experiments on three publicly available datasets with diverse texture patterns demonstrate that the proposed framework outperforms standard and SOTA unsupervised techniques and competes reasonably with supervised methods.
Energy and Carbon Considerations of Fine-Tuning BERT

Despite the popularity of the `pre-train then fine-tune' paradigm in the NLP community, existing work quantifying energy costs and associated carbon emissions has largely focused on language model pre-training. Although a single pre-training run draws substantially more energy than fine-tuning, fine-tuning is performed more frequently by many more individual actors, and thus must be accounted for when considering the energy and carbon footprint of NLP. In order to better characterize the role of fine-tuning in the landscape of energy and carbon emissions in NLP, we perform a careful empirical study of the computational costs of fine-tuning across tasks, datasets, hardware infrastructure and measurement modalities. Our experimental results allow us to place fine-tuning energy and carbon costs into perspective with respect to pre-training and inference, and outline recommendations to NLP researchers and practitioners who wish to improve their fine-tuning energy efficiency.
Fate of the superconducting state in floating islands of hybrid nanowire devices

We investigate the impact of transport current on the superconducting order parameter in superconducting islands in full-shell epitaxial Al-InAs nanowires. Depending on a device layout, the suppression of superconductivity occurs in three fundamentally different ways -- by a critical current in the case of superconducting reservoirs and by a critical voltage or by a critical Joule power in the case of normal reservoirs. In the latter case, the collapse of the superconducting state depends on the ratio of the dwell time and the electron-phonon relaxation time of quasiparticles in the island. For low resistive and high resistive coupling to the reservoirs, respectively, the relaxation-free regime and the strong electron-phonon relaxation regime are realized. Our results shed light on potential shortcomings of finite-bias transport spectroscopy in floating islands.
A2XP: Towards Private Domain Generalization

Deep Neural Networks (DNNs) have become pivotal in various fields, especially in computer vision, outperforming previous methodologies. A critical challenge in their deployment is the bias inherent in data across different domains, such as image style, and environmental conditions, leading to domain gaps. This necessitates techniques for learning general representations from biased training data, known as domain generalization. This paper presents Attend to eXpert Prompts (A2XP), a novel approach for domain generalization that preserves the privacy and integrity of the network architecture. A2XP consists of two phases: Expert Adaptation and Domain Generalization. In the first phase, prompts for each source domain are optimized to guide the model towards the optimal direction. In the second phase, two embedder networks are trained to effectively amalgamate these expert prompts, aiming for an optimal output. Our extensive experiments demonstrate that A2XP achieves state-of-the-art results over existing non-private domain generalization methods. The experimental results validate that the proposed approach not only tackles the domain generalization challenge in DNNs but also offers a privacy-preserving, efficient solution to the broader field of computer vision.
A Language and Its Dimensions: Intrinsic Dimensions of Language Fractal Structures

The present paper introduces a novel object of study - a language fractal structure. We hypothesize that a set of embeddings of all $n$-grams of a natural language constitutes a representative sample of this fractal set. (We use the term Hailonakea to refer to the sum total of all language fractal structures, over all $n$). The paper estimates intrinsic (genuine) dimensions of language fractal structures for the Russian and English languages. To this end, we employ methods based on (1) topological data analysis and (2) a minimum spanning tree of a data graph for a cloud of points considered (Steele theorem). For both languages, for all $n$, the intrinsic dimensions appear to be non-integer values (typical for fractal sets), close to 9 for both of the Russian and English language.
Polymorphic Type Inference for Dynamic Languages

We present a type system that combines, in a controlled way, first-order polymorphism with intersection types, union types, and subtyping, and prove its safety. We then define a type reconstruction algorithm that is sound and terminating. This yields a system in which unannotated functions are given polymorphic types (thanks to Hindley-Milner) that can express the overloaded behavior of the functions they type (thanks to the intersection introduction rule) and that are deduced by applying advanced techniques of type narrowing (thanks to the union elimination rule). This makes the system a prime candidate to type dynamic languages.
Mind the map! Accounting for existing map information when estimating online HDMaps from sensor data

While HDMaps are a crucial component of autonomous driving, they are expensive to acquire and maintain. Estimating these maps from sensors therefore promises to significantly lighten costs. These estimations however overlook existing HDMaps, with current methods at most geolocalizing low quality maps or considering a general database of known maps. In this paper, we propose to account for existing maps of the precise situation studied when estimating HDMaps. We identify 3 reasonable types of useful existing maps (minimalist, noisy, and outdated). We also introduce MapEX, a novel online HDMap estimation framework that accounts for existing maps. MapEX achieves this by encoding map elements into query tokens and by refining the matching algorithm used to train classic query based map estimation models. We demonstrate that MapEX brings significant improvements on the nuScenes dataset. For instance, MapEX - given noisy maps - improves by 38% over the MapTRv2 detector it is based on and by 8% over the current SOTA.