Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of climatic factors on genetic diversity of midday gerbil (Meriones meridianus Pallas, 1773)

The persistence of a species depends on its vulnerability to environmental changes which is determined by its genetic constitution and physiological tolerance (Lynch & Lande, 1993). The impact of climatic flu c tu ations on the distribution and divergence of spe cies may vary extensively according to different species habitats (Voje et al., 2009). Populations of species are dynamic units and adapt to their environment both physiologically and genetically. Natural selection that favours adaptation to local environmental conditions could lead to genetic differentiation among local populations (Slatkin, 1987). Genetic markers have been used to assess whether environmental changes influence the pattern of genetic diversity in populations of some species. For instance, one study found that the ge netic diversity of chukar partridge (Alectoris chu kar) decreased with a decrease in latitude, altitude, and variation coefficients of temperature and rainfall clines, but increased with an increase in longitude, rain fall, and temperature (Huang et al., 2005). Another study found that the genetic diversity of the lizard Phry nocephalus vlangalii was lower when temperature decreased and the rainfall increased. However, the ge netic diversity of the species increased with a decre a se in the variation coefficients of temperature and rain fall (Jin & Liu, 2008). It seems that there is no u ni versal pattern for understanding the relationship between climatic factors and genetic diversity. The environmental impacts on animals may be different and a certain species has its own ecological strategy for life. Midday gerbils (Meriones meridianus Pallas, 1773) are commonly found in sandy areas in Mongolia and northern China and can be found as far west as the Ca spian Sea (Luo et al., 2000). These rodents have a strong ability to survive and adapt to environments ranging from areas with heavy rainfall to those having arid conditions. In the same way, there are populations occupying an altitudinal interval range from 150 Journal of Biological Research-Thessaloniki 19: 120 – 130, 2013 J. Biol. Res.-Thessalon. is available online at http://www.jbr. gr Indexed in: WoS (Web of Science, ISI Thomson), SCOPUS, CAS (Chemical Abstracts Service) and DOAJ (Directory of Open Access Journals)
Veros v0.1 – a fast and versatile ocean simulator in pure Python

Abstract. A general circulation ocean model is translated from Fortran to Python. Its code structure is optimized to exploit available Python utilities, remove simulation bottlenecks, and comply with modern best practices. Furthermore, support for Bohrium is added, a framework that provides a just-in-time compiler for array operations and that supports parallel execution on both CPU and GPU targets. For applications containing more than a million grid elements, such as a typical 1∘×1∘ horizontal resolution global ocean model, Veros is approximately half as fast as the MPI-parallelized Fortran base code on 24 CPUs and as fast as the Fortran reference when running on a high-end GPU. By replacing the original conjugate gradient stream function solver with a solver from the pyAMG Python package, this particular subroutine outperforms the corresponding Fortran version by up to 1 order of magnitude. The study is concluded with a simple application in which the North Atlantic wave response to a Southern Ocean wind perturbation is investigated. It is found that even in a realistic setting the phase speeds of boundary waves matched the expectations based on theory and idealized models.
Preliminary study on the occurrence of pollinator insect species on oil palm Elaeis guineensis and relationship of the major pollinator Elaeidobious kamerunicus to oil palm yield in Ghana.

A preliminary study was carried out on the “occurrence of the oil palm pollinator insect species on oil palm and the relationship of the major pollinator insect species Elaeidobious kamerunicus to oil palm yield in Ghana”. The result showed that pollinator insect species that occurred on oil palm inflorescences were mainly coleopterans. Not all the insect species that were commonly found occurred on both the male and female inflorescences. The common insect species encountered were: Elaeidobious plagiatus, E. singularis, E. kamerunicus, E. bilineatus, E. subvittatus, Microporum congolenses, M. dispar, Atheta sp, Prosoestus sculptilis and P. minor. Elaeidobious subvittatus occurrence was the most common insect species monthly, on the male inflorescence throughout the years 2007, 2008 and 2009. E. kamerunicus occurrence was also the most frequent among the pollinator insect species found on the female inflorescence. E. plagiatus, E. singularis, E. bilineatus, E. subvittatus, M. congolenses, M. dispar, Atheta sp. , P. sculptilis and P. minor occurrence on female oil palm inflorescence was low. Their individual contribution Preliminary study on the occurrence of pollinator insect species on oil palm Elaeis guineensis and relationship of the major pollinator Elaeidobious kamerunicus to oil palm yield in Ghana to successful female oil palm pollination was therefore very low. P. sculptilis and P. minor did not occur on the male inflorescence. Likewise E. bilineatus, M.congolenses, Atheta species and E. plagiatus rarely occurred on the female inflorescence. There was a positive correlation between occurrence of E. kamerunicus and mean oil palm fresh fruit bunch weight in 2008 and 2009. The highest fresh fruit bunch weight was obtained in December, 2009 and lowest in January, 2006.
Don’t Let Me Be Misunderstood:Comparing Intentions and Perceptions in Online Discussions

Discourse involves two perspectives: a person’s intention in making an utterance and others’ perception of that utterance. The misalignment between these perspectives can lead to undesirable outcomes, such as misunderstandings, low productivity and even overt strife. In this work, we present a computational framework for exploring and comparing both perspectives in online public discussions. We combine logged data about public comments on Facebook with a survey of over 16,000 people about their intentions in writing these comments or about their perceptions of comments that others had written. Unlike previous studies of online discussions that have largely relied on third-party labels to quantify properties such as sentiment and subjectivity, our approach also directly captures what the speakers actually intended when writing their comments. In particular, our analysis focuses on judgments of whether a comment is stating a fact or an opinion, since these concepts were shown to be often confused. We show that intentions and perceptions diverge in consequential ways. People are more likely to perceive opinions than to intend them, and linguistic cues that signal how an utterance is intended can differ from those that signal how it will be perceived. Further, this misalignment between intentions and perceptions can be linked to the future health of a conversation: when a comment whose author intended to share a fact is misperceived as sharing an opinion, the subsequent conversation is more likely to derail into uncivil behavior than when the comment is perceived as intended. Altogether, these findings may inform the design of discussion platforms that better promote positive interactions.
SGX-SSD: A Policy-based Versioning SSD with Intel SGX

This paper demonstrates that SSDs, which perform device-level versioning, can be exposed to data tampering attacks when the retention time of data is less than the malware's dwell time. To deal with that threat, we propose SGX-SSD, a SGX-based versioning SSD which selectively preserves file history based on the given policy. The proposed system adopts Intel SGX to implement the version policy management system that is safe from high-privileged malware. Based on the policy, only the necessary data is selectively preserved in SSD that prevents files with less priority from wasting space and also ensures the integrity of important files.
Transferable Active Grasping and Real Embodied Dataset

Grasping in cluttered scenes is challenging for robot vision systems, as detection accuracy can be hindered by partial occlusion of objects. We adopt a reinforcement learning (RL) framework and 3D vision architectures to search for feasible viewpoints for grasping by the use of hand-mounted RGB-D cameras. To overcome the disadvantages of photo-realistic environment simulation, we propose a large-scale dataset called Real Embodied Dataset (RED), which includes full-viewpoint real samples on the upper hemisphere with amodal annotation and enables a simulator that has real visual feedback. Based on this dataset, a practical 3-stage transferable active grasping pipeline is developed, that is adaptive to unseen clutter scenes. In our pipeline, we propose a novel mask-guided reward to overcome the sparse reward issue in grasping and ensure category-irrelevant behavior. The grasping pipeline and its possible variants are evaluated with extensive experiments both in simulation and on a real-world UR-5 robotic arm.
Learning Interpretable and Discrete Representations with Adversarial Training for Unsupervised Text Classification

Learning continuous representations from unlabeled textual data has been increasingly studied for benefiting semi-supervised learning. Although it is relatively easier to interpret discrete representations, due to the difficulty of training, learning discrete representations for unlabeled textual data has not been widely explored. This work proposes TIGAN that learns to encode texts into two disentangled representations, including a discrete code and a continuous noise, where the discrete code represents interpretable topics, and the noise controls the variance within the topics. The discrete code learned by TIGAN can be used for unsupervised text classification. Compared to other unsupervised baselines, the proposed TIGAN achieves superior performance on six different corpora. Also, the performance is on par with a recently proposed weakly-supervised text classification method. The extracted topical words for representing latent topics show that TIGAN learns coherent and highly interpretable topics.
Leveraging Photometric Consistency Over Time for Sparsely Supervised Hand-Object Reconstruction

Modeling hand-object manipulations is essential for understanding how humans interact with their environment. While of practical importance, estimating the pose of hands and objects during interactions is challenging due to the large mutual occlusions that occur during manipulation. Recent efforts have been directed towards fully-supervised methods that require large amounts of labeled training samples. Collecting 3D ground-truth data for hand-object interactions, however, is costly, tedious, and error-prone. To overcome this challenge we present a method to leverage photometric consistency across time when annotations are only available for a sparse subset of frames in a video. Our model is trained end-to-end on color images to jointly reconstruct hands and objects in 3D by inferring their poses. Given our estimated reconstructions, we differentiably render the optical flow between pairs of adjacent images and use it within the network to warp one frame to another. We then apply a self-supervised photometric loss that relies on the visual consistency between nearby images. We achieve state-of-the-art results on 3D hand-object reconstruction benchmarks and demonstrate that our approach allows us to improve the pose estimation accuracy by leveraging information from neighboring frames in low-data regimes.
Meta-Learning for Few-Shot Land Cover Classification

The representations of the Earth’s surface vary from one geographic region to another. For instance, the appearance of urban areas differs between continents, and seasonality influences the appearance of vegetation. To capture the diversity within a single category, such as urban or vegetation, requires a large model capacity and, consequently, large datasets. In this work, we propose a different perspective and view this diversity as an inductive transfer learning problem where few data samples from one region allow a model to adapt to an unseen region. We evaluate the modelagnostic meta-learning (MAML) algorithm on classification and segmentation tasks using globally and regionally distributed datasets. We find that few-shot model adaptation outperforms pre-training with regular gradient descent and fine-tuning on the (1) Sen12MS dataset and (2) DeepGlobe dataset when the source domain and target domain differ. This indicates that model optimization with meta-learning may benefit tasks in the Earth sciences whose data show a high degree of diversity from region to region, while traditional gradient-based supervised learning remains suitable in the absence of a feature or label shift.
NetML: A Challenge for Network Traffic Analytics

Classifying network traffic is the basis for important network applications. Prior research in this area has faced challenges on the availability of representative datasets, and many of the results cannot be readily reproduced. Such a problem is exacerbated by emerging data-driven machine learning based approaches. To address this issue, we provide three open datasets containing almost 1.3M labeled flows in total, with flow features and anonymized raw packets, for the research community. We focus on broad aspects in network traffic analysis, including both malware detection and application classification. We release the datasets in the form of an open challenge called NetML and implement several machine learning methods including random-forest, SVM and MLP. As we continue to grow NetML, we expect the datasets to serve as a common platform for AI driven, reproducible research on network flow analytics.
First-Principles Method to Study Near-Field Radiative Heat Transfer

We present a general and convenient first principle method to study near-field radiative heat transfer. We show that the Landauer-like expression of heat flux can be expressed in terms of a frequency and wave-vector dependent macroscopic dielectric function which can be obtained from the linear response density functional theory. A random phase approximation is used to calculate the response function. We computed the heat transfer in three systems -- graphene, molybdenum disulfide (MoS$_2$), and hexagonal boron nitride (h-BN). Our results show that the near-field heat flux exceeds the blackbody limit up to four orders of magnitude. With the increase of the distances between two parallel sheets, a $1/d^2$ dependence of heat flux is shown, consistent with Coulomb's law. The heat transfer capacity is sensitive to the dielectric properties of materials. Influences from chemical potential and temperature are also discussed. Our method can be applied to a wide range of materials including systems with inhomogeneities.
SSIM-Based CTU-Level Joint Optimal Bit Allocation and Rate Distortion Optimization

Structural similarity (SSIM)-based distortion $D_\text{SSIM}$ is more consistent with human perception than the traditional mean squared error $D_\text{MSE}$. To achieve better video quality, many studies on optimal bit allocation (OBA) and rate-distortion optimization (RDO) used $D_\text{SSIM}$ as the distortion metric. However, many of them failed to optimize OBA and RDO jointly based on SSIM, thus causing a non-optimal R-$D_\text{SSIM}$ performance. This problem is due to the lack of an accurate R-$D_\text{SSIM}$ model that can be used uniformly in both OBA and RDO. To solve this problem, we propose a $D_\text{SSIM}$-$D_\text{MSE}$ model first. Based on this model, the complex R-$D_\text{SSIM}$ cost in RDO can be calculated as simpler R-$D_\text{MSE}$ cost with a new SSIM-related Lagrange multiplier. This not only reduces the computation burden of SSIM-based RDO, but also enables the R-$D_\text{SSIM}$ model to be uniformly used in OBA and RDO. Moreover, with the new SSIM-related Lagrange multiplier in hand, the joint relationship of R-$D_\text{SSIM}$-$\lambda_\text{SSIM}$ (the negative derivative of R-$D_\text{SSIM}$) can be built, based on which the R-$D_\text{SSIM}$ model parameters can be calculated accurately. With accurate and unified R-$D_\text{SSIM}$ model, SSIM-based OBA and SSIM-based RDO are unified together in our scheme, called SOSR. Compared with the HEVC reference encoder HM16.20, SOSR saves 4%, 10%, and 14% bitrate under the same SSIM in all-intra, hierarchical and non-hierarchical low-delay-B configurations, which is superior to other state-of-the-art schemes.
Spreading and fragmentation of particle-laden liquid sheets

The fragmentation of liquid sheets produces a collection of droplets. The size distribution of the droplets has a considerable impact on the coating efficiency of sprays and the transport of contaminants. Although many processes commonly used particulate suspensions, the influence of the particles on the spreading dynamics of the sheet and its subsequent fragmentation has so far been considered negligible. In this paper, we consider experimentally a transient suspension sheet that expands radially. We characterize the influence of the particles on the dynamics of the liquid sheet and the fragmentation process. We highlight that the presence of particles modifies the thickness and reduces the stability of the liquid sheet. Our study suggests that particles can significantly modify the dynamics of liquid films through capillary effects, even for volume fractions much smaller than the maximum packing.
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction

Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction. However, few existing works excel in solving the overlapping triple problem where multiple relational triples in the same sentence share the same entities. In this work, we introduce a fresh perspective to revisit the relational triple extraction task and propose a novel cascade binary tagging framework (CasRel) derived from a principled problem formulation. Instead of treating relations as discrete labels as in previous works, our new framework models relations as functions that map subjects to objects in a sentence, which naturally handles the overlapping problem. Experiments show that the CasRel framework already outperforms state-of-the-art methods even when its encoder module uses a randomly initialized BERT encoder, showing the power of the new tagging framework. It enjoys further performance boost when employing a pre-trained BERT encoder, outperforming the strongest baseline by 17.5 and 30.2 absolute gain in F1-score on two public datasets NYT and WebNLG, respectively. In-depth analysis on different scenarios of overlapping triples shows that the method delivers consistent performance gain across all these scenarios. The source code and data are released online.
Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream Dataset

Robotic grasping plays an important role in the field of robotics. The current state-of-the-art robotic grasping detection systems are usually built on the conventional vision, such as RGB-D camera. Compared to traditional frame-based computer vision, neuromorphic vision is a small and young community of research. Currently, there are limited event-based datasets due to the troublesome annotation of the asynchronous event stream. Annotating large scale vision dataset often takes lots of computation resources, especially the troublesome data for video-level annotation. In this work, we consider the problem of detecting robotic grasps in a moving camera view of a scene containing objects. To obtain more agile robotic perception, a neuromorphic vision sensor (DAVIS) attaching to the robot gripper is introduced to explore the potential usage in grasping detection. We construct a robotic grasping dataset named Event-Stream Dataset with 91 objects. A spatio-temporal mixed particle filter (SMP Filter) is proposed to track the led-based grasp rectangles which enables video-level annotation of a single grasp rectangle per object. As leds blink at high frequency, the Event-Stream dataset is annotated in a high frequency of 1 kHz. Based on the Event-Stream dataset, we develop a deep neural network for grasping detection which consider the angle learning problem as classification instead of regression. The method performs high detection accuracy on our Event-Stream dataset with 93% precision at object-wise level. This work provides a large-scale and well-annotated dataset, and promotes the neuromorphic vision applications in agile robot.
MultiMix: A Robust Data Augmentation Strategy for Cross-Lingual NLP

Transfer learning has yielded state-of-the-art results in many supervised natural language processing tasks. However, annotated data for every target task in every target language is rare, especially for low-resource languages. In this work, we propose MultiMix, a novel data augmentation method for semi-supervised learning in zero-shot transfer learning scenarios. In particular, MultiMix targets to solve cross-lingual adaptation problems from a source (language) distribution to an unknown target (language) distribution assuming it has no training labels in the target language task. In its heart, MultiMix performs simultaneous self-training with data augmentation and unsupervised sample selection. To show its effectiveness, we have performed extensive experiments on zero-shot transfers for cross-lingual named entity recognition (XNER) and natural language inference (XNLI). Our experiments show sizeable improvements in both tasks outperforming the baselines by a good margin.
Nested Tailbiting Convolutional Codes for Secrecy, Privacy, and Storage

The key agreement problem with biometric or physical identifiers and two terminals for key enrollment and reconstruction is considered. A nested convolutional code construction that performs lossy compression with side information is proposed. Nested convolutional codes are an alternative to nested polar codes and nested random linear codes that achieve all points of the key-leakage-storage regions of the generated-secret and chosen-secret models for long block lengths. Our design uses a convolutional code for vector quantization during enrollment and a subcode of it for error correction during reconstruction. Physical identifiers with small bit error probability are considered to illustrate the gains of the proposed construction. One variant of nested convolutional codes improves on all previous constructions in terms of the key vs. storage rate ratio but it has high complexity. Another variant of nested convolutional codes with lower complexity performs similarly to previously designed nested polar codes. The results suggest that the choice of convolutional or polar codes for key agreement with identifiers depends on the complexity constraints.