Miracle-dz/UniFAR-FTUs huggingface dataset:
Tianshanbeilu and the Isotopic Millet Road: reviewing the late Neolithic/Bronze Age radiation of human millet consumption from north China to Europe

Abstract The westward expansion of human millet consumption from north China has important implications for understanding early interactions between the East and West. However, few studies have focused on the Xinjiang Uyghur Autonomous Region, the vast geographical area directly linking the ancient cultures of the Eurasian Steppe and the Gansu Corridor of China. In this study, we present the largest isotopic investigation of Bronze Age China (n = 110) on material from the key site of Tianshanbeilu, in eastern Xinjiang. The large range of δ13C values (–17.6‰ to –7.2‰; –15.5 ± 1.2‰) provides direct evidence of unique dietary diversity and consumption of significant C4 resources (millets). The high δ15N results (10.3‰ to 16.7‰; 14.7 ± 0.8‰) likely reflect sheep/goat and wild game consumption and the arid climate of the Taklamakan Desert. Radiocarbon dates from four individuals indicate Tianshanbeilu was in use between 1940 and 1215 cal bc. The Tianshanbeilu results are then analysed with respect to 52 Bronze Age sites from across Eurasia, to investigate the spread and chronology of significant human millet consumption and human migration. This isotopic survey finds novel evidence that the second millennium bc was a dynamic period, with significant dietary interconnectivity occurring between north China, Central Asia and Siberia. Further, we argue that this ‘Isotopic Millet Road’ extended all the way to the Mediterranean and Central Europe, and conclude that these C4 dietary signatures of millet consumption reflect early links (migration and/or resource transfer) between the Bronze Age inhabitants of modern-day China and Europe.
COSEA: Convolutional Code Search with Layer-wise Attention

Semantic code search, which aims to retrieve code snippets relevant to a given natural language query, has attracted many research efforts with the purpose of accelerating software development. The huge amount of online publicly available code repositories has prompted the employment of deep learning techniques to build state-of-the-art code search models. Particularly, they leverage deep neural networks to embed codes and queries into a unified semantic vector space and then use the similarity between code's and query's vectors to approximate the semantic correlation between code and the query. However, most existing studies overlook the code's intrinsic structural logic, which indeed contains a wealth of semantic information, and fails to capture intrinsic features of codes. In this paper, we propose a new deep learning architecture, COSEA, which leverages convolutional neural networks with layer-wise attention to capture the valuable code's intrinsic structural logic. To further increase the learning efficiency of COSEA, we propose a variant of contrastive loss for training the code search model, where the ground-truth code should be distinguished from the most similar negative sample. We have implemented a prototype of COSEA. Extensive experiments over existing public datasets of Python and SQL have demonstrated that COSEA can achieve significant improvements over state-of-the-art methods on code search tasks.
Do Deeper Convolutional Networks Perform Better?

Over-parameterization is a recent topic of much interest in the machine learning community. While over-parameterized neural networks are capable of perfectly fitting (interpolating) training data, these networks often perform well on test data, thereby contradicting classical learning theory. Recent work provided an explanation for this phenomenon by introducing the double descent curve, showing that increasing model capacity past the interpolation threshold can lead to a decrease in test error. In line with this, it was recently shown empirically and theoretically that increasing neural network capacity through width leads to double descent. In this work, we analyze the effect of increasing depth on test performance. In contrast to what is observed for increasing width, we demonstrate through a variety of classification experiments on CIFAR10 and ImageNet32 using ResNets and fully-convolutional networks that test performance worsens beyond a critical depth. We posit an explanation for this phenomenon by drawing intuition from the principle of minimum norm solutions in linear networks.
Characterization of bacterial antagonists of Rhizoctonia solani and Fusarium oxysporum from six European soils and their potential application for biological control

A cultivation-based approach was used to characterize the culturable antagonistic bacteria in six agricultural soils from different locations in Europe (France, FR; the Netherlands, NL; Sweden, SE; the United Kingdom, UK; and two sites in Germany, Berlin, G-BR and Braunschweig, G-BS). Four of the soils (FR, NL, SE, UK) have history of disease suppression while two (G-BR and G-BS) have no record of diseases suppression. Serial dilutions from the composite soil samples were plated on R2A, AGS and King´s B media. A total of 1,788 isolates (approximately 100 isolates per medium and site) was screened for antagonistic activity towards two phytopathogenic fungi {Rhizoctonia solani AG3 and Fusarium oxysporum f. sp. lini (Foln3)}; and antagonists (327 isolates) were found amongst the dominant culturable bacteria isolated from all six soils. The overall proportion of antagonists was higher in three of the suppressive soils (FR, NL and SE) with NL having the highest proportion. Phenotypic characterization of antagonistic bacteria revealed siderophore and protease activity as the most prominent traits amongst the antagonists. Although, FAME and 16S rRNA gene sequencing identified majority of the antagonists as Pseudomonas spp. (110 of 327) and Streptomyces spp. (113 of 327), phenotypic and genotypic diversity among the antagonists were found. Screening of Pseudomonas spp. for antibiotic encoding genes, 2,4-diacetylphloroglucinol (phlD), pyrrolnitrin (prnD), pyoluteorin (pltC) and phenazine (phzCD), showed that Pseudomonas antagonists carrying phlD gene were found in all sites, with the highest proportion in SE, where about 50% of the Pseudomonas antagonists contained this gene. When ten of the antagonists were assessment in growth experiments for their efficacy in controlling bottom rot disease caused by R. solani AG1-IB on lettuce plants, only one isolate (Pseudomonas jessenii strain RU47) showed effective and consistent suppression of the pathogen.
Influence of Indian Ocean zonal circulation on variability and predictability of Ethiopia highlands vegetation

The vegetation fraction over the Ethiopian highlands exhibits large seasonal and multi-annual variability in the period 1982 to 2014. Northern areas are more sensitive to rainfall due to the brevity of the wet season. Differences in the regional climate during periods of high and low vegetation are analyzed using composite fields of rainfall, wind, humidity, sea temperatures, salinity and currents; and correlations at six-month lead-time. The key climatic driver of vegetation is the zonal overturning atmospheric circulation linking subsidence over the east Indian Ocean and ascent within an expanded African monsoon. Certain aspects of this Walker Cell reflect Pacific ENSO interaction with the Indian Ocean Dipole. During cold phase ENSO an upwelling Rossby wave and cool sea temperatures in the west Indian Ocean promote vegetation growth in the Ethiopian highlands.
Shiga Toxin-Producing Escherichia coli (STEC) Detection Strategies with Formalin-Fixed STEC Cells

Certain pathogenic Escherichia coli known as Shiga toxin (Stx)-producing Escherichia coli (STEC) are commensals in cattle, and typically cause bloody diarrhea in humans once the Stx toxin is secreted in invaded intestinal epithelial cells. Infections with STEC cells can lead to hemolytic uremic syndrome, which is commonly associated with kidney failure. Several STEC serogroups have been declared adulterants in raw, non-intact ground meat, and future regulations could potentially lead to a higher number of STEC serogroup detection strategies for these pathogenic microorganisms. Microbiological research laboratories may benefit from formalinfixed STEC cells for periodic (daily, weekly, monthly, among others) instrument validation/calibration by serving as a working set of known cell concentration samples and internal standard i.e. positive control. These cell concentrations may be used across laboratories in different geographical locations, within an individual laboratory, and across a broad range of detection assays (molecular as well as immuno-based). This thesis consists of three research parts: a comprehensive literature review that covers STEC incidence in foods and molecular detection techniques (chapter 1), a literature review that covers immuno-based detection strategies (chapter 2), and a research manuscript that involves the development of an internal standard and positive control with formalin-fixed STEC cells that can be used for a broad range of molecular as well immuno-based detection assays for instrument calibration and validation purposes (chapter 3).
ISOLATION AND CHARACTERIZATION OF ACTINOMYCETES FROM SOIL OF AD-DAWADMI, SAUDI ARABIA AND SCREENING THEIR ANTIBACTERIAL ACTIVITIES

Objective: To isolate and characterize novel actinomycetes and to evaluate their antibacterial activity against drug-resistant pathogenic bacteria Methods: In the present study, 19 soil samples were collected from different localities of Ad-Dawadmi, Saudi Arabia. Actinomycetes were isolated from these samples using serial dilution and plating method on Actinomycetes isolation agar supplemented with nalidixic acid and actidione to inhibit bacteria and fungi. Crude extracts of potential actinomycetes were produced by submerged fermentation. The antimicrobial activity of crude extracts of actinomycetes was tested against different bacteria using the agar well diffusion method. Characterization of the isolates was done by morphological, physiological and biochemical methods. Results: A total of 9 (47%) isolates of actinomycetes were isolated from 19 different soil samples tested. Among them, 4 (44%) isolates confirmed as Streptomyces sp. showed potential antimicrobial activity against one or more test organisms. Crude extracts were made from these 4 actinomycetes isolates(DOM1, DOM3, DP3, DP4)and tested for their antibacterial activities against 4 different clinical bacterial strains ( Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Enterococcu s) . Crude extract from DP3 isolate showed highest antibacterial activity against all the four test organisms (28 mm, 21 mm, 20 mm and 18 mm) respectively and DP4 showed lowest antibacterial activity against all the four test organisms (14 mm, 12 mm, 0 mm, 6 mm) respectively. The highest zone of inhibition was shown by DP3 against Staphylococcus aureus (28 mm) and Escherichia coli was resistant for DP4. Most of the Inhibition zones produced by crude extracts showed significant differences when compared with control, tested against test organisms (P<0.05). Inhibition zones produced by DP3 and DOM1 against Staphylococcus aureus were 28 mm and 23 mm, respectively which were strong active when compared with control Ciprofloxacin (18 mm). Conclusion: Further studies for purification of bioactive metabolites and molecular characterization analysis of isolated Streptomyces sp. are in progress which would be helpful in discovering novel compounds of commercial value.
Unraveling the response of the actin cortex to electric pulses in vesicles

The plasma membrane is essential for maintaining the integrity of living cells. Its shape and mechanical properties are regulated by a subjacently attached interconnected network of actin filaments, known as the “actin cortex”. The actin cortex is a major factor influencing the mechanical response of the cell to external physical cues. In this study, using giant unil-amellar vesicles (GUVs) as a model system, we investigate the role of the actin cortex during the application of electric pulses that induce electroporation or electropermeabilization. We demonstrate that the presence of an actin cortex inhibits the formation of macropores in the electroporated GUVs and slows down the resealing process of the permeabilized membrane. We further analyze the stability of the actin cortex inside the GUVs exposed to high electric pulses. We find disruption of the actin cortex that is likely due to the electrophoretic forces acting on the actin filaments during the permeabilization of the GUVs. Our findings also reveal that the current simplified experimental models of cells need to be endowed with an actin cortex in order to more closely mimic the complex nature of cellular membranes.
Comparison of the Characteristics of Two Kinds of Tea Seed Oils: Oil-tea Seed Oil and Green-Tea Seed Oil

Physicochemical properties, fatty acid composition, antioxidant compounds and oxidative stability of oil-tea seed oil ( Camellia oleifera Abel.) and green-tea seed oil ( Camellia sinensis O. Ktze.) were investigated. The refractive index, saponification value, iodine value, acid value, peroxide value, unsaponifiables were determined to assess the quality of the oils. The major fatty acids of green-tea seed oil and oil-tea seed oil were oleic acid, linoleic acid and palmitic acid. Green-tea seed oil was typical oleic-linoleic-oil with 52.13% oleic acid and 24.32% linoleic acid level, whereas oil-tea seed oil was typical oleic-oil with very high oleic acid level (73.67%). The amount of total phenols, α-tocopherol and β-carotene of green-tea seed oil were 8.68 mg/kg, 160.33 mg/kg, 3.20 mg/kg, respectively, whereas they were 17.90 mg/kg, 85.66 mg/kg, 1.18 mg/kg in oil-tea seed oil, respectively. Green-tea seed oil contained high amounts of α-tocopherol which was nearly twice that of oil-tea seed oil. The initial induction period (IP) values of green-tea seed oil and oil-tea seed oil were 6.55h and 6.08h at 110 o C by OSI method, respectively, which shows the oxidative stability of two kinds of tea seed oils were preferable. Therefore, oil-tea seed oil could be a good dietary supplement with high level of monounsaturated fatty acids and similar fatty acid composition of olive oil. Green-tea seed oil was a new oil resource which is rich in α-tocopherol in China.
Influence of environmental factors on content and composition of essential oil from common juniper ripe berry cones (Juniperus communis L.)

Abstract The present study evaluated the influence of some environmental factors on the quantity and composition of essential oil (EO) in ripe berry cones of Juniperus communis L. The berry cones were collected from juniper shrubs growing wild at five localities of north-east Slovakia during the years 2012–2014. The EO yield ranged from 0.4 to 1.9%, depending on the locality and year. In the EO, eight monoterpenes (α-pinene, β-pinene, β-myrcene, sabinene, limonene, terpinene-4-ol, borneol, bornylacetate) and one sesquiterpene (β-caryophyllene) were identified. The dominant component was the monoterpene α-pinene, ranging from 31.0 to 49.0%. The amount and composition of the EO was affected by soil composition (content of humus and pH) and topographic environmental factors, including air temperature and precipitation. According to the composition of the EO, the studied juniper shrubs belong to the α-pinene chemotype.
Tree cricket baffles are manufactured tools

Tree crickets use sound to attract mates and make acoustic baffles to increase their sound production efficiency. It has been recently discovered that these insects use a flexible yet inherited behavioural programme to make acoustically optimal baffles. Whether these baffles qualify as tools, however, remains controversial. Here, baffle‐using and baffle‐making behaviours are analysed using the most current and authoritative definition of tool use. The current definition of tool use does not require the tool to be detached from the substrate and includes attached but manipulable external objects. Given this schema, tree cricket baffles, which are attached but manipulated prior to use, must be considered tools. The mode of manufacture for a baffle is “Subtract,” and the mode of use is “Drape”.
Approximate information state for approximate planning and reinforcement learning in partially observed systems

We propose a theoretical framework for approximate planning and learning in partially observed systems. Our framework is based on the fundamental notion of information state. We provide two equivalent definitions of information state---i) a function of history which is sufficient to compute the expected reward and predict its next value; ii) equivalently, a function of the history which can be recursively updated and is sufficient to compute the expected reward and predict the next observation. An information state always leads to a dynamic programming decomposition. Our key result is to show that if a function of the history (called approximate information state (AIS)) approximately satisfies the properties of the information state, then there is a corresponding approximate dynamic program. We show that the policy computed using this is approximately optimal with bounded loss of optimality. We show that several approximations in state, observation and action spaces in literature can be viewed as instances of AIS. In some of these cases, we obtain tighter bounds. A salient feature of AIS is that it can be learnt from data. We present AIS based multi-time scale policy gradient algorithms. and detailed numerical experiments with low, moderate and high dimensional environments.
Self-Supervised Visual Attention Learning for Vehicle Re-Identification

Visual attention learning (VAL) aims to produce a confidence map as weights to detect discriminative features in each image for certain task such as vehicle re-identification (ReID) where the same vehicle instance needs to be identified across different cameras. In contrast to the literature, in this paper we propose utilizing self-supervised learning to regularize VAL to improving the performance for vehicle ReID. Mathematically using lifting we can factorize the two functions of VAL and self-supervised regularization through another shared function. We implement such factorization using a deep learning framework consisting of three branches: (1) a global branch as backbone for image feature extraction, (2) an attentional branch for producing attention masks, and (3) a self-supervised branch for regularizing the attention learning. Our network design naturally leads to an end-to-end multi-task joint optimization. We conduct comprehensive experiments on three benchmark datasets for vehicle ReID, i.e., VeRi-776, CityFlow-ReID, and VehicleID. We demonstrate the state-of-the-art (SOTA) performance of our approach with the capability of capturing informative vehicle parts with no corresponding manual labels. We also demonstrate the good generalization of our approach in other ReID tasks such as person ReID and multi-target multi-camera tracking.
Two-particle correlations at high-energy nuclear collisions, peripheral-tube model revisited

In this paper, we give an account of the peripheral-tube model, which has been developed to give an intuitive and dynamical description of the so-called ridge effect in two-particle correlations in high-energy nuclear collisions. Starting from a realistic event-by-event fluctuating hydrodynamical model calculation, we first show the emergence of ridge + shoulders in the so-called two-particle long-range correlations, reproducing the data. In contrast to the commonly used geometric picture of the origin of the anisotropic flow, we can explain such a structure dynamically in terms of the presence of high energy-density peripheral tubes in the initial conditions. These tubes violently explode and deflect the near radial flow coming from the interior of the hot matter, which in turn produces a two-ridge structure in single-particle distribution, with approximately two units opening in azimuth. When computing the two-particle correlation, this will result in characteristic three-ridge structure, with a high near-side ridge and two symmetric lower away-side ridges or shoulders. Several anisotropic flows, necessary to producing ridge + shoulder structure, appear naturally in this dynamical description. Using this simple idea, we can understand several related phenomena, such as centrality dependence and trigger-angle dependence.
SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks

Recent learning-based LiDAR odometry methods have demonstrated their competitiveness. However, most methods still face two substantial challenges: 1) the 2D projection representation of LiDAR data cannot effectively encode 3D structures from the point clouds; 2) the needs for a large amount of labeled data for training limit the application scope of these methods. In this paper, we propose a self-supervised LiDAR odometry method, dubbed SelfVoxeLO, to tackle these two difficulties. Specifically, we propose a 3D convolution network to process the raw LiDAR data directly, which extracts features that better encode the 3D geometric patterns. To suit our network to self-supervised learning, we design several novel loss functions that utilize the inherent properties of LiDAR point clouds. Moreover, an uncertainty-aware mechanism is incorporated in the loss functions to alleviate the interference of moving objects/noises. We evaluate our method's performances on two large-scale datasets, i.e., KITTI and Apollo-SouthBay. Our method outperforms state-of-the-art unsupervised methods by 27%/32% in terms of translational/rotational errors on the KITTI dataset and also performs well on the Apollo-SouthBay dataset. By including more unlabelled training data, our method can further improve performance comparable to the supervised methods.
Brightest cluster galaxies: the centre can(not?) hold

We explore the persistence of the alignment of brightest cluster galaxies (BCGs) with their local environment. We find that a significant fraction of BCGs do not coincide with the centroid of the X-ray gas distribution and/or show peculiar velocities (they are not at rest with respect to the cluster mean). Despite this, we find that BCGs are generally aligned with the cluster mass distribution even when they have significant offsets from the X-ray centre and significant peculiar velocities. The large offsets are not consistent with simple theoretical models. To account for these observations BCGs must undergo mergers preferentially along their major axis, the main infall direction. Such BCGs may be oscillating within the cluster potential after having been displaced by mergers or collisions, or the dark matter halo itself may not yet be relaxed.