Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the impacts of climate change on interactions between different lepidopteran stemborer species

Multi-species of insects which utilise the same host plant are often involved in intraspecific and interspecific, competitive or facilitative interactions with one other. These interactions are influenced by temperature and rainfall. Lepidopteran stemborers are major pests of cereal crops in sub-Saharan Africa. Two indigenous noctuids, Busseola fusca and Sesamia calamistis and one exotic crambid Chilo partellus occur as single and mixed-species communities infesting cereal crops in East and southern Africa. The composition of these communities however varies with altitude and seasons. Surveys were conducted in maize fields in different localities along an altitudinal gradient in the major agro-ecological zones in Kenya and Tanzania. Another monitoring survey to study the spatio-temporal dynamics of infestation by the community of the three stemborer species was conducted over three seasons, in one locality in the midaltitudinal ranges in Kenya. Destructive sampling of infested maize plants was done in the fields in all localities, to record the species and the number of larvae of each species inside maize stems. The results of this study confirmed that stemborer communities are composed of single and mixed species of B. fusca, S. calamistis and C. partellus. The composition of the stemborer species varied with the different agro-ecological zones and also along altitudinal gradients. Temperature was the most influencing factor on the composition of stemborer communities. Similarly, the stemborer community in the midaltitude locality was composed of singleand multi-species of B. fusca, S. calamistis and C. partellus with single-species populations of B. fusca dominating the community in all the three seasons. However, infestation levels and larval density of S. calamistis were higher in one season than another. The pattern of infestations between the stemborer species followed a clustered distribution in all seasons. Temperature and rainfall had significant effects on these dynamics at the micro-level of stemborer community.
Extraction and characterization of collagen from calipash of Chinese soft-shelled turtle (Pelodiscus sinensis).

Collagen is one of the predominant proteins in the living body,and the essential protein component in connective tissue.Compared with collagen from land animals(such as pig,cow),aquatic collagen has drawn extensive attention for its excellent physiochemical properties and physiologically active functions.Chinese soft-shelled turtle(Pelodiscus sinensis) is a rare and precious traditional aquatic animal in China.It has a high medicinal and nutritional value with plenty of calipash collagen.The collagen from calipash of P.sinensis was extracted by pepsin digestion under acid condition.The effects of pepsin addition,substrate concentration and extraction time on yield of collagen were investigated.5 mg/mL pepsin addition,4% substrate concentration and 14 hours extraction time were established as optimal conditions for preparing calipash collagen.The extracted crude collagen was purified by salt precipitation and dialysis,and then its biochemical characterization was determined by the methods of SDS-PAGE,DSC,UV and FT-IR scanning.The result indicated that the extracted calipash collagen was of high purity and belonged to typical collagen Ⅰ with two α and one β chains.It can be assumed that the calipash collagen of P.sinensis might be a useful substitute for porcine and bovine collagens.This study laid a scientific foundation for further exploitation and utilization of the calipash collagen polypeptide of Chinese soft-shelled turtle.
Comparative analysis of room temperature plasmonic graphene hot electron bolometric photodetectors

We appraise a waveguide-integrated plasmonic graphene photodetector based on the hot carrier photo-bolometric effect, with performance characterized simultaneously by high responsivity, on the scale of hundreds of A/W, and high speed on the scale of 100’s of GHz. Performance evaluation is based on a theory of bolometric effect originating from the band nonparabolicity of graphene. Results compare favorably with the state-of-the-art plasmonic bolometric photodetectors, predicting up to two orders of magnitude increase in a responsivity while keeping speed on the same level, defined by the electron-lattice scattering time in graphene.
Identification of Challenging Highway-Scenarios for the Safety Validation of Automated Vehicles Based on Real Driving Data

For a successful market launch of automated vehicles (AVs), proof of their safety is essential. Due to the open parameter space, an infinite number of traffic situations can occur, which makes the proof of safety an unsolved problem. With the so-called scenario-based approach, all relevant test scenarios must be identified. This paper introduces an approach that finds particularly challenging scenarios from real driving data (RDD) and assesses their difficulty using a novel metric. Starting from the highD data, scenarios are extracted using a hierarchical clustering approach and then assigned to one of nine pre-defined functional scenarios using rule-based classification. The special feature of the subsequent evaluation of the concrete scenarios is that it is independent of the performance of the test vehicle and therefore valid for all AVs. Previous evaluation metrics are often based on the criticality of the scenario, which is, however, dependent on the behavior of the test vehicle and is therefore only conditionally suitable for finding "good" test cases in advance. The results show that with this new approach a reduced number of particularly challenging test scenarios can be derived.
An Approximate Carry Estimating Simultaneous Adder with Rectification

Approximate computing has in recent times found significant applications towards lowering power, area, and time requirements for arithmetic operations. Several works done in recent years have furthered approximate computing along these directions. In this work, we propose a new approximate adder that employs a carry prediction method. This allows parallel propagation of the carry allowing faster calculations. In addition to the basic adder design, we also propose a rectification logic which would enable higher accuracy for larger computations. Experimental results show that our adder produces results 91.2% faster than the conventional ripple-carry adder. In terms of accuracy, the addition of rectification logic to the basic design produces results that are more accurate than state-of-the-art adders like SARA[13] and BCSA[5] by 74%.
High-Harmonic Generation from Resonant Dielectric Metasurfaces Empowered by Bound States in the Continuum

The concept of optical bound states in the continuum (BICs) underpins the existence of strongly localized waves embedded into the radiation spectrum that can enhance the electromagnetic fields in subwavelength photonic structures. Early studies of optical BICs in waveguides and photonic crystals uncovered their topological properties, and the concept of quasi-BIC metasurfaces facilitated applications of strong light-matter interactions to biosensing, lasing, and low-order nonlinear processes. Here we employ BIC-empowered dielectric metasurfaces to generate efficiently high optical harmonics up to the 11th order. We optimize a BIC mode for the first few harmonics and observe a transition between perturbative and nonperturbative nonlinear regimes. We also suggest a general strategy for designing subwavelength structures with strong resonances and nonperturbative nonlinearities. Our work bridges the fields of perturbative and nonperturbative nonlinear optics on the subwavelength scale.
Keypoint-Aligned Embeddings for Image Retrieval and Re-identification

Learning embeddings that are invariant to the pose of the object is crucial in visual image retrieval and re-identification. The existing approaches for person, vehicle, or animal re-identification tasks suffer from high intra-class variance due to deformable shapes and different camera viewpoints. To overcome this limitation, we propose to align the image embedding with a predefined order of the key-points. The proposed keypoint aligned embeddings model (KAE-Net) learns part-level features via multi-task learning which is guided by keypoint locations. More specifically, KAE-Net extracts channels from a feature map activated by a specific keypoint through learning the auxiliary task of heatmap reconstruction for this keypoint. The KAE-Net is compact, generic and conceptually simple. It achieves state of the art performance on the benchmark datasets of CUB-200-2011, Cars196 and VeRi-776 for retrieval and re-identification tasks.
Disappearing Errors in a Conversion Model

The same basic differential equation model has been adapted for time-dependent conversions of members of a population among different states. The conversion model has been applied in different contexts such as epidemiological infections, the Bass model for the diffusion of innovations, and the ideodynamic model for public opinion. For example, the ideodynamic version of the model predicts changes in public opinions in response to persuasive messages extending back to an indefinite past. All messages are measured with error, and this chapter discusses how errors in message measurements disappear with time so that predicted opinion values gradually become unaffected by past measurement errors. Prediction uncertainty is discussed using formal statistics, sensitivity analysis and bootstrap variance calculations. This chapter presents ideodynamic predictions for opinion time series about the Toyota car manufacturer calculated from daily Twitter scores over two and half years. During this time, there was a sudden onslaught of bad news for Toyota, and the model could accurately predict the accompanying drop in favorable Toyota opinion and rise in unfavorable opinion.
Leveraging Kernelized Synergies on Shared Subspace for Precision Grasping and Dexterous Manipulation

Manipulation in contrast to grasping is a trajectorial task that needs to use dexterous hands. Improving the dexterity of robot hands increases the controller complexity and thus requires to use the concept of postural synergies. Inspired from postural synergies, this research proposes a new framework called kernelized synergies that focuses on the reusability of the same subspace for precision grasping and dexterous manipulation. In this work, the computed subspace of postural synergies is parameterized by kernelized movement primitives (KMPs) to preserve its grasping and manipulation characteristics and allows its reuse for new objects. The grasp stability of the proposed framework is assessed with the force closure quality index, as a cost function. For performance evaluation, the proposed framework is initially tested on two different simulated robot hand models using the Syngrasp toolbox and experimentally, four complex grasping and manipulation tasks are performed and reported. Results confirm the hand agnostic approach of the proposed framework and its generalization to distinct objects irrespective of their dimensions.
Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks

\textit{Deep Neural Networks} (DNNs) have been shown to be prone to adversarial attacks. With a growing need to enable intelligence in embedded devices in this \textit{Internet of Things} (IoT) era, secure hardware implementation of DNNs has become imperative. Memristive crossbars, being able to perform \textit{Matrix-Vector-Multiplications} (MVMs) efficiently, are used to realize DNNs on hardware. However, crossbar non-idealities have always been devalued since they cause errors in performing MVMs, leading to degradation in the accuracy of the DNNs. Several software-based adversarial defenses have been proposed in the past to make DNNs adversarially robust. However, no previous work has demonstrated the advantage conferred by the non-idealities present in analog crossbars in terms of adversarial robustness. In this work, we show that the intrinsic hardware variations manifested through crossbar non-idealities yield adversarial robustness to the mapped DNNs without any additional optimization. We evaluate resilience of state-of-the-art DNNs (VGG8 \& VGG16 networks) using benchmark datasets (CIFAR-10 \& CIFAR-100) across various crossbar sizes towards both hardware and software adversarial attacks. We find that crossbar non-idealities unleash greater adversarial robustness ($>10-20\%$) in DNNs than baseline software DNNs. We further assess the performance of our approach with other state-of-the-art efficiency-driven adversarial defenses and find that our approach performs significantly well in terms of reducing adversarial losses.
Gesture Recognition from Skeleton Data for Intuitive Human-Machine Interaction

Human gesture recognition has assumed a capital role in industrial applications, such as Human-Machine Interaction. We propose an approach for segmentation and classification of dynamic gestures based on a set of handcrafted features, which are drawn from the skeleton data provided by the Kinect sensor. The module for gesture detection relies on a feedforward neural network which performs framewise binary classification. The method for gesture recognition applies a sliding window, which extracts information from both the spatial and temporal dimensions. Then we combine windows of varying durations to get a multi-temporal scale approach and an additional gain in performance. Encouraged by the recent success of Recurrent Neural Networks for time series domains, we also propose a method for simultaneous gesture segmentation and classification based on the bidirectional Long Short-Term Memory cells, which have shown ability for learning the temporal relationships on long temporal scales. We evaluate all the different approaches on the dataset published for the ChaLearn Looking at People Challenge 2014. The most effective method achieves a Jaccard index of 0.75, which suggests a performance almost on pair with that presented by the state-of-the-art techniques. At the end, the recognized gestures are used to interact with a collaborative robot.
Mechanical Metastructures of Triple Periodic Carbon Clathrates

Clathrates are lightweight, cage-like, fully-sp3 three dimensional (3D) structures that are experimentally-available for several host elements of the IV group. However, carbon clathrates are as yet hypothetical structures. Herein, the mechanical properties of Type-I-C46 Type-II-C34 and Type-H-C34 carbon clathrates are explored by first-principles calculations. It is revealed that those carbon clathrates show distinct anisotropic patterns in ideal tensile/shear strengths and critical tensile/shear strains, with maximum ideal tensile strength of Type-I carbon clathrate that is superior over that of diamond in direction. However, it is identified isotropy in shear Youngs modulus, and in terms of tensile/shear Youngs moduli, they are sorted as Type-I > Type-II > Type-H carbon clathrates. There are distinct critical load-bearing bond configurations that explain their distinct mechanical behaviors. Moreover, those carbon clathrates are intrinsically indirect semiconductors, and their electronic properties can be greatly dictated by mechanical strain. Carbon clathrates can be potentially utilized as lightweight technically robust engineering metastructures and in electromechanical devices.
KIC 9406652: A laboratory for tilted disks in cataclysmic variable stars

KIC 9406652 is a cataclysmic variable, sub-classified as an “IW And-type” star, which shows repeated standstills with oscillatory variations terminated by brightening. This system shows negative superhumps, semi-periodic variations having periods slightly shorter than the ∼6 hr orbital period, and super-orbital signals having ∼4 d periods, both of which are believed to originate from a precessing, tilted accretion disk. We have re-examined its Kepler light curve over 1500 d. In accordance with a cycle of IW And-type light variation, the frequency of negative superhumps showed reproducible variation: a rapid drop during the brightening and a gradual increase during the standstill. This is interpreted as a drastic change in the radial mass distribution and the expansion of the tilted disk, which is not expected from the existing models of IW And stars. The constancy in flux amplitudes of the negative superhumps confirms that their light source is the bright spot sweeping across the surface of the tilted disk. The frequencies of the negative superhumps and super-orbital signals varied in unison on long timescales, suggesting their common origin: the tilted disk. The orbital signals at the brightening were dominated by the irradiation of the secondary star and varied with the orientation of the tilted disk; the amplitude was maximized at the minimum of the super-orbital signals, and the light maximum shifted to early orbital phases as the super-orbital phase advanced. This is the first direct evidence that the disk is tilted out of the binary orbital plane and is retrogradely precessing. The tilt angle of the disk inferred from semi-amplitudes of super-orbital signals was lower than 3°. The diversity in light curves of the negative superhumps supports this and suggests that part of the gas stream overflows the disk edge. This study thus offers rich information about the tilted disk in cataclysmic variables.
The Impact of Indirect Machine Translation on Sentiment Classification

Sentiment classification has been crucial for many natural language processing (NLP) applications, such as the analysis of movie reviews, tweets, or customer feedback. A sufficiently large amount of data is required to build a robust sentiment classification system. However, such resources are not always available for all domains or for all languages. In this work, we propose employing a machine translation (MT) system to translate customer feedback into another language to investigate in which cases translated sentences can have a positive or negative impact on an automatic sentiment classifier. Furthermore, as performing a direct translation is not always possible, we explore the performance of automatic classifiers on sentences that have been translated using a pivot MT system. We conduct several experiments using the above approaches to analyse the performance of our proposed sentiment classification system and discuss the advantages and drawbacks of classifying translated sentences.
Bandit Data-driven Optimization: AI for Social Good and Beyond

The use of machine learning (ML) systems in real-world applications entails more than just a prediction algorithm. AI for social good applications, and many real-world ML tasks in general, feature an iterative process which joins prediction, optimization, and data acquisition happen in a loop. We introduce bandit data-driven optimization, the first iterative prediction-prescription framework to formally analyze this practical routine. Bandit data-driven optimization combines the advantages of online bandit learning and offline predictive analytics in an integrated framework. It offers a flexible setup to reason about unmodeled policy objectives and unforeseen consequences. We propose PROOF, the first algorithm for this framework and show that it achieves no-regret. Using numerical simulations, we show that PROOF achieves superior performance over existing baseline.
End-to-End 3D Multi-Object Tracking and Trajectory Forecasting

3D multi-object tracking (MOT) and trajectory forecasting are two critical components in modern 3D perception systems. We hypothesize that it is beneficial to unify both tasks under one framework to learn a shared feature representation of agent interaction. To evaluate this hypothesis, we propose a unified solution for 3D MOT and trajectory forecasting which also incorporates two additional novel computational units. First, we employ a feature interaction technique by introducing Graph Neural Networks (GNNs) to capture the way in which multiple agents interact with one another. The GNN is able to model complex hierarchical interactions, improve the discriminative feature learning for MOT association, and provide socially-aware context for trajectory forecasting. Second, we use a diversity sampling function to improve the quality and diversity of our forecasted trajectories. The learned sampling function is trained to efficiently extract a variety of outcomes from a generative trajectory distribution and helps avoid the problem of generating many duplicate trajectory samples. We show that our method achieves state-of-the-art performance on the KITTI dataset. Our project website is at this http URL.
Keldysh rotation in the large-
N
 expansion and string theory out of equilibrium

We analyze the large-$N$ expansion of general non-equilibrium systems with fluctuating matrix degrees of freedom and $SU(N)$ symmetry, using the Schwinger-Keldysh formalism and its closed real-time contour with a forward and backward component. In equilibrium, the large-$N$ expansion of such systems leads to a sum over topologies of two-dimensional surfaces of increasing topological complexity, predicting the possibility of a dual description in terms of string theory. We extend this argument away from equilibrium, and study the universal features of the topological expansion in the dual string theory. We conclude that in non-equilibrium string perturbation theory, the sum over worldsheet topologies is further refined: Each worldsheet surface $\Sigma$ undergoes a triple decomposition into the part $\Sigma^+$ corresponding to the forward branch of the time contour, the part $\Sigma^-$ on the backward branch, and the part $\Sigma^\wedge$ that corresponds to the instant in the far future where the two branches of the time contour meet. The sum over topologies becomes a sum over the triple decompositions. We generalize our findings to the Kadanoff-Baym time contour relevant for systems at finite temperature, and to the case of closed and open, oriented or unoriented strings. Our results are universal, and follow solely from the features of the large-$N$ expansion without any assumptions about the worldsheet dynamics.
Museum Accessibility Through Wi-Fi Indoor Positioning

Accessibility has long been a primary concern for major museums around the world. This is no exception for the Museo Nacional de Artes Visuales (MNAV, National Museum of Visual Arts) in Uruguay. Having a special interest in achieving accessibility for visually impaired visitors, the MNAV sought to implement a new system to allow these visitors a seamless tour around a new exhibit. We present here the system we developed and the lessons we learned from its deployment and usage. In particular, we used Wi-Fi indoor positioning techniques, so that visually impaired visitors could hear relevant audios through an Android app from their own smartphones based on their location inside the museum. The system was further adapted and used to assist the general public during their visits, allowing access to texts, audios and images according to their position. We furthermore share the complete source code and the dataset used to train the system.
Decision Tree J48 at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text (Hinglish)

This paper discusses the design of the system used for providing a solution for the problem given at SemEval-2020 Task 9 where sentiment analysis of code-mixed language Hindi and English needed to be performed. This system uses Weka as a tool for providing the classifier for the classification of tweets and python is used for loading the data from the files provided and cleaning it. Only part of the training data was provided to the system for classifying the tweets in the test data set on which evaluation of the system was done. The system performance was assessed using the official competition evaluation metric F1-score. Classifier was trained on two sets of training data which resulted in F1 scores of 0.4972 and 0.5316.