Miracle-dz/UniFAR-FTUs huggingface dataset:
Codelet Pipe: Realization of Dataflow Software Pipelining for Extended Codelet Model

Dataflow Software Pipelining for Codelet Model is a coarse-grained code-mapping scheme designed to exploit pipelined parallelism across Codelets executing on different cores. The extended operational semantics of the Codelet model exploit pipelined parallelism across loops (coarse-grained) using single owner FIFO buffers across Codelet’s dependencies. The extended Codelet Model with Dataflow Software Pipelining extensions has shown promising performance benefits by leveraging FIFO buffers to communicate between producer and consumer codelets. These performance gains can be further amplified using an efficient implementation of FIFO buffers using hardware-software co-design principles for an architecture that supports explicit access to scratchpad memory closer to compute cores. In this work, we introduce Codelet Pipe which serves as an efficient hardware-software co-designed communication channel between producer-consumer codelets to take advantage of dataflow software pipelining for codelet model. The current implementation of Codelet Pipe exploits Shared Local Memory architectural feature of Intel Iris Pro GPU using OpenCL. Codelet Pipe enables users to construct well-structured Codelet Graphs as well as helps with the challenge of ease of Programmability by relieving user from the responsibility of handling communication between producer-consumer codelet pairs. We demonstrate performance gains using a set of micro-benchmarks for a GPU architecture of strategic importance for exascale supercomputers.
On The Reproducibility of Seasonal Land-surface Climate

The sensitivity of the continental seasonal climate to initial conditions is estimated from an ensemble of decadal simulations of an atmospheric general circulation model with the same specifications of radiative forcings and monthly ocean boundary conditions, but with different initial states of atmosphere and land. As measures of the ''reproducibility'' of continental climate for different initial conditions, spatio-temporal correlations are computed across paired realizations of eleven model land-surface variables in which the seasonal cycle is either included or excluded--the former case being pertinent to climate simulation, and the latter to seasonal anomaly prediction. It is found that the land-surface variables which include the seasonal cycle are impacted only marginally by changes in initial conditions; moreover, their seasonal climatologies exhibit high spatial reproducibility. In contrast, the reproducibility of a seasonal land-surface anomaly is generally low, although it is substantially higher in the Tropics; its spatial reproducibility also markedly fluctuates in tandem with warm and cold phases of the El Nino/Southern Oscillation. However, the overall degree of reproducibility depends strongly on the particular land-surface anomaly considered. It is also shown that the predictability of a land-surface anomaly implied by its reproducibility statistics is consistent with what is inferred from more conventional predictability metrics. Implications of these results for climate model intercomparison projects and for operational forecasts of seasonal continental climate also are elaborated.
Does regional ethnic diversity moderate the negative effect of school ethnic diversity on educational performance

This paper address the question whether ethnic diversity in school classes in the context of major cities and metropolises, where children from an early age grew up with the phenomenon of ethnic diversity, has a positive effect on the educational performance of migrant pupils. We use PISA 2006 data with 8,521 immigrant students from 35 origin countries, living in 15 destination countries, and all 72,329 native students in these countries. Native students and students with an immigrant background have been analyzed separately, using a multilevel analysis. We find that the effect of school ethnic diversity outside cities is quite negative on the educational performance of migrant and native pupils. In cities the effect of school ethnic diversity is smaller but still negative for both migrant and native pupils. But in large cities the effect of school ethnic diversity on educational performance is positive for both migrant and native pupils.
SATE: A Self-Attention Based Text Topic Extraction Mechanism for Customer Service Dialog System

AI customer service system has been widely used in various industries. It is a research hotspot that accurately understanding customers’ intent and extracting key information from customer service dialogues. However, customer service dialog texts are short, specialized, and sparse. Those features lead to the poor performance of existing topic extraction mechanisms. This paper proposed a self-attention based topic extraction mechanism for customer service dialogue text, which is abbreviated as SATE. It consists of a corpus expansion model that can adaptively expand a suitable number of words based on words’ class and frequency. Then, SATE introduces the self-attention mechanism to train the corpus word vectors and mine their potential semantics. The self-attention mechanism can solve the semantic confusion caused by the polysemy of words in word expansion. Finally, SATE adopts the LDA topic model to extract topics of the expanded corpus. The experimental results show that the proposed SATE method can work better than the state-of-art methods.
Accurate Segmentation of Lumbar Spine Magnetic Resonance Imaging by Deep Neural Network with Size-Adaptive Loss

Accurate segmentation of lumbar spine is crucial for the assessment of spine disease diagnosis. The accuracy and robustness of segmentation is challenged by morphological complexity of lumbar spine anatomy structure, low contrast and background noise. Moreover, unbalanced anatomical structure impede the segmentation of small-size anatomy. To overcome these problems, an automatic segmentation method of lumber spine MRI is needed. In this paper, we propose to segment lumbar spine using a convolutional neural network (CNN) model. Based on the canonical u-shaped model, a multi-scale feature fusion module (MSFF) is proposed as bottleneck to better capture lumbar spine anatomy information of different sizes. To address the problem of anatomy size imbalance, we reshape the standard cross entropy loss as Size-Adaptive loss such that it adaptively weights the loss assigned to anatomy with different sizes. In order to fully verify the accuracy and effectiveness of the proposed method, we construct the first lumbar spine anatomy segmentation dataset which consists of 747 lumbar axial images from 83 patients. The dice similarity coefficient (DSC) is used to evaluate the segmentation methods. Compared with other methods, the proposed method achieves the best results with the highest average DSC of 0.8911. Ablation study shows that the proposed MSFF module and SizeAdaptive loss can promote segmentation accuracy a lot compared with the baseline model. The results indicate that our proposed method can overcome the influence of morphological variety in some degree and is competitive with other current medical image segmentation algorithms. This novel methodology will be expected to use in clinic for image diagnosis in the future.
Baileys: An Efficient Distributed Machine Learning Framework by Dynamic Grouping

Many machine-learning applications rely on distributed machine learning (DML) systems to train models from massive datasets using massive computing resources (e.g., GPUs and TPUs). However, given a DML system in most applications, its parameter synchronization mechanism is fixed and independent from the types and amounts of resources available to the system. In this paper, we argue that given an application, the synchronization mechanism in its DML system should be co-designed with the available resources in the heterogeneous cluster. To this end, we design an efficient parameter synchronization framework called Baileys. First, Baileys retrieves resource information from the heterogeneous clusters and uses such information to partition heterogeneous workers into multiple groups dynamically. Second, Baileys develops efficient group-based parameter synchronization mechanisms to converge model parameters quickly and accurately. We implement a prototype of Baileys and demonstrate its efficiency and efficacy through experiments. Results show that Baileys can reduce block time up to 33.2% and decrease the test error up to 39.7%.
Exploration of the main active metabolites from Tinospora crispa (L.) Hook. f. & Thomson stem as insulin sensitizer in L6.C11 skeletal muscle cell by integrating in vitro, metabolomics, and molecular docking

Ethnopharmacological relevance Tinospora crispa (L.) Hook. f. & Thomson stem (TCS) has long been used as folk medicine for the treatment of diabetes mellitus. Previous study revealed that TCS possesses multi-ingredients and multi-targets characteristic potential as insulin sensitizer activity. However, its mechanisms of action and molecular targets are still obscure. Aim of the study In the present study, we investigated the effects of TCS against insulin resistance in muscle cells through integrating in vitro experiment and identifying its active biomarker using metabolomics and in molecular docking validation. Materials and methods We used centrifugal partition chromatography (CPC) to isolate 33 fractions from methanolic extract of TCS, and then used UHPLC-Orbitrap-HRMS to identify the detectable metabolites in each fraction. We assessed the insulin sensitization activity of each fraction using enzyme-linked immunosorbent assay (ELISA), and then used confocal immunocytochemistry microscopy to measure the translocation of glucose transporter 4 (GLUT4) to the cell membrane. The identified active metabolites were further simulated for its molecular docking interaction using Autodock Tools. Results The polar fractions of TCS significantly increased insulin sensitivity, as measured by the inhibition of phosphorylated insulin receptor substrate-1 (pIRS1) at serine-312 residue (ser312) also the increasing number of translocated GLUT4 and glycogen content. We identified 58 metabolites of TCS, including glycosides, flavonoids, alkaloids, coumarins, and nucleotides groups. The metabolomics and molecular docking simulations showed the presence of minor metabolites consisting of tinoscorside D, higenamine, and tinoscorside A as the active compounds. Conclusions Our findings suggest that TCS is a promising new treatment for insulin resistance and the identification of the active metabolites in TCS could lead to the development of new drugs therapies for diabetes that target these pathways. Highlights TCS attenuated insulin resistance in skeletal muscle through lowering IRS ser312 phosphorylation, increasing translocation of GLUT4 and glycogen content. Tinoscorside D, tinoscorside A, and higenamine were identified as the primary bioactive compounds of TCS.
The simplicity of protein sequence-function relationships

How complicated is the genetic architecture of proteins – the set of causal effects by which sequence determines function? High-order epistatic interactions among residues are thought to be pervasive, making a protein’s function difficult to predict or understand from its sequence. Most studies, however, used methods that overestimate epistasis, because they analyze genetic architecture relative to a designated reference sequence – causing measurement noise and small local idiosyncrasies to propagate into pervasive high-order interactions – or have not effectively accounted for global nonlinearity in the sequence-function relationship. Here we present a new reference-free method that jointly estimates global nonlinearity and specific epistatic interactions across a protein’s entire genotype-phenotype map. This method yields a maximally efficient explanation of a protein’s genetic architecture and is more robust than existing methods to measurement noise, partial sampling, and model misspecification. We reanalyze 20 combinatorial mutagenesis experiments from a diverse set of proteins and find that additive and pairwise effects, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of total variance in measured phenotypes (and >92% in every case). Only a tiny fraction of genotypes are strongly affected by third- or higher-order epistasis. Genetic architecture is also sparse: the number of terms required to explain the vast majority of variance is smaller than the number of genotypes by many orders of magnitude. The sequence-function relationship in most proteins is therefore far simpler than previously thought, opening the way for new and tractable approaches to characterize it.
Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning

Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually-tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from MERFSIH, seqFISH, or ISS experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell. org.
3D MULTIPLE CRACK SYSTEM: EXTRACTION AND ANALYSIS USING MOTION-BASED IMAGE PROCESSING TECHNIQUE

We present a motion-based method to extract the multiple 3D crack surfaces from a sequence of 3D X-ray microtomographic mortar images. We verify the correctness and accuracy of results by ground truth obtained with simulated cracks in real specimens. We provide several three-dimensional measures of crack surface, including the number of cracks, surface area, volume, tortuosity, and permeability.
Approche quantitative de l'erosion hydrique a differentes echelles spatiales: bassin versant de l'Oued Mina / Quantitative approach to water erosion at different spatial scales: basin of Oued Mina

Resume Nous examinons ici le phenomene tres complexe de l'erosion hydrique de bassins versants. L'approche adoptee consiste d'une part a quantifier l'erosion a plusieurs echelles spatiales, au sens de la methode Wischmeier, de la simulation de pluies sur 1 et 87 m2, de micro-bassins experimentaux, de retenues collinaires et d'un barrage en exploitation, et d'autre part a rechercher un modele regressif pouvant expliquer la relation debit solide-debit liquide pour les differentes formes d'erosion. Une zone de marnes du bassin de la Mina en Algerie, productive en sediments pour un barrage en aval et representant le cinquieme de la surface, a constitue la zone experimentale. Les resultats montrent que, vu la complexite des facteurs mis enjeu, il est quasiment impossible d'extrapoler a differentes echelles. Le modele puissance caracterise la relation debit solide-debit liquide, quelle que soit l'echelle spatiale consideree.
Illuminating the Terminal Nerve: Uncovering the Link between GnRH-1 and Olfactory Development

During embryonic development, the olfactory placode (OP) generates migratory neurons, including olfactory pioneer neurons, cells of the terminal nerve (TN), Gonadotropin-releasing hormone-1 (GnRH-1) neurons, and other uncharacterized neurons. Pioneer neurons from the olfactory placode induce olfactory bulb morphogenesis. In mice, GnRH-1 neurons appear in the olfactory system around mid-gestation and migrate via the terminal nerve axons to different brain regions. The GnRH-1 neurons are crucial in controlling the hypothalamic-pituitary-gonadal (HPG) axis. Kallmann syndrome is characterized by impaired olfactory system development, defective olfactory bulbs, defective secretion of GnRH-1, and infertility. The precise mechanistic link between the olfactory system and GnRH-1 development remains unclear. Studies in humans and mice highlight the importance of the Prokineticin-2/Prokineticin-Receptor-2 (Prokr2) signaling pathway in olfactory bulb morphogenesis and GnRH-1 neuronal migration. Prokr2 loss-of-function mutations can cause Kallmann syndrome, and hence the Prokr2 signaling pathway represents a unique model to decipher the olfactory/GnRH-1 connection. We discovered that Prokr2 is expressed in the TN neurons during the critical period of GnRH-1 neuron formation, migration, and induction of olfactory bulb morphogenesis. Single-cell RNA sequencing identified that the TN is formed by neurons that are distinct from the olfactory neurons. The TN neurons express multiple genes associated with KS. Our study suggests that the aberrant development of pioneer/TN neurons might cause the KS spectrum. Key Points 1) Pioneer or terminal nerve neurons play a crucial role in initiating the development of the olfactory bulbs. We found that the Prokineticin Receptor-2 gene, associated with Kallmann syndrome, is expressed by the olfactory pioneer/terminal nerve neurons. 2) We genetically traced, isolated, and conducted Single-cell RNA sequencing on terminal nerve neurons of rodents. This analysis revealed a significant enrichment of gene expression related to Kallmann syndrome. 3) Our study indicates that the investigation of Pioneer/terminal nerve neurons should be a pivotal focal point for comprehending developmental defects affecting olfactory and GnRH-1 systems.
The SLC1A1/EAAT3 Dicarboxylic Amino Acid Transporter is an Epigenetically Dysregulated Nutrient Carrier that Sustains Oncogenic Metabolic Programs

Inactivation of pVHL tumor suppressor in clear cell Renal Cell Carcinoma (ccRCC) increases the abundance of Histone H3 lysine 27 acetylation (H3K27ac). We hypothesized that H3K27ac, a marker of transcriptional activation, drives the expression of critical oncogenes in ccRCC. Using H3K27ac ChIP-Seq; RNA-Seq; an in vivo positive selection screen; cell-based functional studies; and clinical validations; here, we report the identification of the SLC1A1/EAAT3 aspartate (Asp) and glutamate (Glu) transporter as a ccRCC oncogene. pVHL loss promotes SLC1A1 expression in a HIF-independent manner. Importantly, SLC1A1 inactivation depletes Asp/Glu-derived metabolites, impedes ccRCC growth both in vitro and in vivo, and sensitizes ccRCCs to metabolic therapeutics (e.g., glutaminase blockers). Finally, in human ccRCC biospecimens, higher SLC1A1 expression is associated with metastatic disease and clusters with elevated expression of other solute carriers, but not HIF/Hypoxia pathways. Altogether, our studies identify a HIF-independent metabolic hub in ccRCC and credential SLC1A1 as an actionable ccRCC oncogene. STATEMENT OF SIGNIFICANCE Targeting chronic HIF activation underlies many therapeutic strategies in ccRCC; but, unfortunately, is not curative. SLC1A1, instead, represents a HIF-independent ccRCC dependency, which is targetable alone and together with other antimetabolites, such as glutaminase inhibitors. These observations identify an actionable metabolic program that functions independent of HIF in ccRCC.
Heterogeneity and genomic evolution of metastatic prostate cancer

Background Metastasis is the primary cause of prostate cancer-related deaths. However, the underlying molecular mechanisms and evolutionary patterns remain largely uncharacterized. Methods We evaluate the heterogeneity and genomic evolution of prostate cancer with multi-organ metastases. The samples include 32 primary samples, 23 lymph node metastases, 22 bone metastases, 16 liver metastases, and four pelvic mess metastases. They are analyzed to identify the mutated genes enriched in metastatic samples, selected by metastases, and leading to different long-distance migrations. These metastasis-related alterations constitute a Mscore for evaluating the metastatic risk of primary prostate tumors. Results Our analysis discovers 21 metastasis-related mutated genes in total. Of them, 14 genes are finally selected for metastatic risk prognosis, including the mutations of AR and KMT2C with high prediction ability. A Mscore established with these 14 characteristics by the xgboost model displays its ability to classify primary tumors and metastases. This score can further divide primary prostate tumors from the TCGA cohort into two groups. The two subsets present significantly differential survival risks. This score can also identify metastasis-featured primary tumors for breast cancer, bladder cancer, liver cancer, and uterine corpus endometrial carcinoma. Conclusion Our research proposes 14 molecular features potentially driving prostate cancer metastasis. The Mscore established on them can estimate the metastatic risk of primary tumors.
Investigating the genetic control of plant development under speed breeding conditions

Speed breeding is a powerful tool to accelerate breeding and research programmes by shortening generation time and has been widely adopted for a range of crop species. Despite its success and growing popularity with breeders the genetic basis of plant development under speed breeding remains unknown. In this study, we explored how genotypes respond in terms of developmental advancement under different photoperiod regimes in the context of speed breeding. A subset of the barley HEB-25 Nested Association Mapping population was evaluated for days to heading and maturity under two contrasting photoperiod conditions: 1) Speed Breeding (SB) consisting of 22 hours of light and 2 hours of darkness), and 2) Normal Breeding (NB) consisting of 16 hours of light and 8 hours of darkness. GWAS revealed that developmental responses under both conditions were largely controlled by two loci: PPDH-1 and ELF3. Allelic variants at these genes determine whether plants display early flowering and maturity under both NB and SB. At key QTL regions, domesticated alleles were associated with late flowering and maturity in NB and early flowering and maturity in SB, whereas wild alleles were associated with early flowering under both conditions. We hypothesise that this may be related to the dark dependent repression of PPD-H1 by ELF3 which might be more prominent in NB conditions. Furthermore, by comparing development under two contrasting photoperiod regimes, we were able to derive an estimate of plasticity for the two traits. Interestingly, plasticity in development was largely attributed to allelic variation at ELF3. Our results have important implications for our understanding and optimisation of speed breeding protocols particularly when incorporating genetics from wild relatives into breeding programmes and the design of breeding programmes to support the delivery of climate resilient crops.
The Ethics of AI-Generated Maps: DALL·E 2 and AI's Implications for Cartography (Short Paper)

The rapid advancement of artificial intelligence (AI) such as the emergence of large language models ChatGPT and DALL·E 2 has brought both opportunities for improving productivity and raised ethical concerns. This paper investigates the ethics of using artificial intelligence (AI) in cartography, with a particular focus on the generation of maps using DALL·E 2. To accomplish this, we first created an open-sourced dataset that includes synthetic (AI-generated) and real-world (human-designed) maps at multiple scales with a variety of settings. We subsequently examined four potential ethical concerns that may arise from the characteristics of DALL·E 2 generated maps, namely inaccuracies, misleading information, unanticipated features