Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Grasp Pose Detection with Object Detection

Recently, researchers have proposed various grasp detection methods that can be used to localize grasp configurations without estimating object pose [17, 7, 12, 16, 15, 2, 3, 1, 5, 10, 8, 20, 4]. These methods take as input a noisy and partially occluded RGBD image or point cloud and produce as output pose estimates of viable grasps. The underlying idea is to treat grasp perception analogously to object detection in computer vision. Given large amounts of grasp training data, a classifier or regression system is trained to detect parts of an image or a point cloud that can be grasped. Because these methods detect grasps independently of object identity, they typically generalize grasp knowledge to new objects well. Recently, we have proposed a variation on grasp detection that detects grasps in SE(3) with high accuracy. This method achieved a 93% grasp success rate in dense clutter [4] (grasps succeeded as a fraction of grasp attempts) – one of the highest reported. A key drawback of grasp pose detection in general is that it does not immediately provide a way to grasp a specific object of interest. Grasp detectors will typically find any viable grasp – not necessarily one on the desired object. This suggests that grasp detection might be integrated with object detection. One might think about this as a mixture of two experts: a grasp detection expert that estimates the probability that a grasp candidate is a true grasp; and an object detection expert that estimates the probability that the grasp lies on the object of interest. Grasps would be ranked according to the product of these two probabilities. There are a couple of reasons why the “mixture of experts” approach might compare favorably with more traditional approaches based on estimating the pose of a particular object instance (e.g. [14]). First, any grasp method that incorporates our grasp pose detection algorithm immediately inherits a high grasp success rate. Second, the mixture-of-experts method is able to benefit from training data from a variety of sources – not just training data derived from the particular object instance being grasped. By using training data from these different sources, the mixture-of-experts method might generalize grasp knowledge to new objects better and even perform better when the object instance to be grasped is known in advance. 2 Combined Grasp and Object Detection
Emergence of plasmaronic structure in the near-field optical response of graphene

The finite momentum optical response $\sigma({\boldsymbol{q}},\omega)$ of graphene can be probed with the innovative technique of infrared nanoscopy where mid-infrared radiation is confined by an atomic force microscope cantilever tip. In contrast to conventional $q\sim 0$ optical absorption which primarily involves Dirac fermions with momentum near the Fermi momentum, $k\sim k_F$, for finite $q$, $\sigma({\boldsymbol{q}},\omega)$ has the potential to provide information on many body renormalizations and collective phenomena which have been found at small $k< k_F$ near the Dirac point in electron-doped graphene. For electron-electron interactions, the low energy excitation spectrum characterizing the incoherent part of the quasiparticle spectral function of Dirac electrons with $k\sim k_F$ consists of a flat, small amplitude background which scales with chemical potential and Fermi momentum. However, probing of the states with $k$ near $k=0$ will reveal plasmarons, a collective state of a charge carrier and a plasmon. These collective modes in graphene have recently been seen in angle-resolved photoemission spectroscopy and here we describe how they manifest in near field optics.
Simulating the impacts of ecological protection policies on urban land use sustainability in Shenyang-Fushun, China

Chinese cities are undergoing rapid urban sprawl, dramatic landscape change, industrialisation, and ecological damage, which threaten urban sustainable development. The aim of our research was to answer the following question: is it possible to achieve sustainable development through rational ecological protection policies that harmonise future urbanisation, re‐industrialisation, economic development, and sustainable urban land use in these cities? To answer the question, we conducted a simulation of policy impacts on future urban growth and landscape change. We applied a widely used regional assessment and predictive model, SLEUTH, to explore the potential impacts of different ecological protection policy scenarios in the Shenyang-Fushun metropolitan region of China. Future urban growth and landscape changes were projected out to 2050, assuming three different ecological protection policy scenarios: (1) accelerated urban development (AUD), (2) managed urban development (MUD), and (3) limited urban development (LUD). Landscape metrics were used to compare and analyse the impacts of the future scenarios on urban growth and landscape change. Analysis reveals that urban growth and landscape fragmentation increase in all scenarios, but future urban growth area and landscape pattern differ among the scenarios. The AUD scenario will cause excessive urban growth, major land loss, and serious landscape fragmentation. The LUD scenario shows good land resource protection and low landscape fragmentation, but the urban growth rate is unrealistically low, reducing the extent of likely re-industrialisation and economic development. The MUD scenario shows moderate urban growth and landscape fragmentation. It would be the most suitable scenario for the industrial metropolitan area under study if the urban sprawl can be controlled, and it would better harmonise re-industrialisation, ecological health, and land preservation and achieve sustainable development in the area.
Efficient Construction of Tensor Ring Representations from Sampling

In this note we propose an efficient method to compress a high dimensional function into a tensor ring format, based on alternating least-squares (ALS). Since the function has size exponential in $d$ where $d$ is the number of dimensions, we propose efficient sampling scheme to obtain $O(d)$ important samples in order to learn the tensor ring. Furthermore, we devise an initialization method for ALS that allows fast convergence in practice. Numerical examples show that to approximate a function with similar accuracy, the tensor ring format provided by the proposed method has less parameters than tensor-train format and also better respects the structure of the original function.
IMMUNOLOGICAL MODELS OF EPIDEMICS

Mathematical models for analyzing the spread of a disease are usually epidemiological or immunological. The former are mostly ODE-based models that use classes like susceptibles, recovered, infectives, latently infected, etc to describe the evolution of an epidemic in a population. Some of them also use structure variables, such as size or age. The latter describe the evolution of the immune system/pathogen in the infected host—usually resulting in death, recovery or chronic infection. There is valuable insight to be gained from combining these two types of models, as that may lead to a better understanding of the severity of an epidemic. In this article we propose a new type of model that combines the two by using variables of immunological nature as structure variables for epidemiological models. We then describe a practical application of the model to HIV infection.
Thermo-erosion along the Yedoma coast of the Buor Khaya peninsula, Laptev Sea, East Siberia

Coastal erosion in continuous permafrost regions with high ground ice content is affected by subsidence and mass transport due to thawing of ground ice. Using sites along the western coast of the Buor Khaya Peninsula, we introduce an index for thermo-erosion based on the change in position of the upper and lower edges of the coastal bluff through time. Field surveys of thermo-terraces and an Alas were compared with historical elevations and coastlines. Using survey TINs and photogrammetry-derived DEMs, coastal position change, volumetric land loss and the Normalized Difference Thermo-erosion Index (NDTI) was calculated. Coastline erosion varied from 0.4 to 3.3 m a over the 36 year period from 1974 to 2010, resulting in volumetric erosion of 7 000 to 67 000 m a per km of coastline. NDTI values show coastline erosion dominated by thermo-abrasive to thermo-denudative processes.
Phase Retrieval Using Alternating Minimization in a Batch Setting

This paper considers the problem of phase retrieval, where the goal is to recover a signal $z\in {\mathbb{c}}^n$ from the observations ${y}_{i}=\vert{a}_{i}^\ast{z}\vert, {i}=1,2,\ldots,{m}$. While many algorithms have been proposed, the alternating minimization algorithm has been one of the most commonly used methods, and it is very simple to implement. Current work [26] has proved that when the observation vectors $\{{a}_{i}\}_{i=1}^{m}$ are sampled from a complex Gaussian distribution $N(\mathrm{0}, \mathbf{I})$, it recovers the underlying signal with a good initialization when ${m}=O({n})$, or with random initialization when ${m}=O({n}^{2})$, and it conjectured that random initialization succeeds with ${m}=O({n})$. This work proposes a modified alternating minimization method in a batch setting, and proves that when ${m}=O({n}{log^{3}n})$, the proposed algorithm with random initialization recovers the underlying signal with high probability. The proof is based on the observation that after each iteration of alternating minimization, with high probability, the angle between the estimated signal and the underlying signal is reduced.
Style Counsel: Seeing the (Random) Forest for the Trees in Adversarial Code Stylometry

The results of recent experiments have suggested that code stylometry can successfully identify the author of short programs from among hundreds of candidates with up to 98% precision. This potential ability to discern the programmer of a code sample from a large group of possible authors could have concerning consequences for the open-source community at large, particularly those contributors that may wish to remain anonymous. Recent international events have suggested the developers of certain anti-censorship and anti-surveillance tools are being targeted by their governments and forced to delete their repositories or face prosecution. In light of this threat to the freedom and privacy of individual programmers around the world, we devised a tool, Style Counsel, to aid programmers in obfuscating their inherent style and imitating another, overt, author's style in order to protect their anonymity from this forensic technique. Our system utilizes the implicit rules encoded in the decision points of a random forest ensemble in order to derive a set of recommendations to present to the user detailing how to achieve this obfuscation and mimicry attack.
Underlying structure of e-learning readiness among Palestinian secondary school teachers

This paper reports on the results of an exploratory factor analysis procedure applied on the e-learning readiness data obtained from a survey of four hundred and seventy-five (N = 475) teachers from secondary schools in Nablus, Palestine. The data were collected using a 23-item, self-developed Likert questionnaire measuring e-learning readiness based on Chapnick’s conception of the construct. Principal axis factoring (PAF) with Promax rotation applied on the data extracted four distinct factors supporting four of Chapnick’s e-learning readiness dimensions, namely technological, psychological, infrastructure, and equipment readiness. Together these four dimensions explained 56% of the variance. A reliability analysis produced high internal consistency estimates ranging between . 81 (equipment readiness) and . 91 (technological readiness) for the extracted factor structure. These findings provide sound empirical support for the construct validity of the items and for the existence of these four factors that measure e-learning readiness.
ANALYSIS OF MOBILE LASER SCANNING DATA AND MULTI-VIEW IMAGE RECONSTRUCTION

The combination of laser scanning (LS, active, direct 3D measurement of the object surface) and photogrammetry (high geometric and radiometric resolution) is widely applied for object reconstruction (e.g. architecture, topography, monitoring, archaeology). Usually the results are a coloured point cloud or a textured mesh. The geometry is typically generated from the laser scanning point cloud and the radiometric information is the result of image acquisition. In the last years, next to significant developments in static (terrestrial LS) and kinematic LS (airborne and mobile LS) hardware and software, research in computer vision and photogrammetry lead to advanced automated procedures in image orientation and image matching. These methods allow a highly automated generation of 3D geometry just based on image data. Founded on advanced feature detector techniques (like SIFT (Scale Invariant Feature Transform)) very robust techniques for image orientation were established (cf. Bundler). In a subsequent step, dense multi-view stereo reconstruction algorithms allow the generation of very dense 3D point clouds that represent the scene geometry (cf. Patch-based Multi-View Stereo (PMVS2)). Within this paper the usage of mobile laser scanning (MLS) and simultaneously acquired image data for an advanced integrated scene reconstruction is studied. For the analysis the geometry of a scene is generated by both techniques independently. Then, the paper focuses on the quality assessment of both techniques. This includes a quality analysis of the individual surface models and a comparison of the direct georeferencing of the images using positional and orientation data of the on board GNSS-INS system and the indirect georeferencing of the imagery by automatic image orientation. For the practical evaluation a dataset from an archaeological monument is utilised. Based on the gained knowledge a discussion of the results is provided and a future strategy for the integration of both techniques is proposed.
Quasi equilibrium construction for the long time limit of glassy dynamics

In this paper we review a recent proposal to understand the long time limit of glassy dynamics in terms of an appropriate Markov chain []. The advantages of the resulting construction are many. The first one is that it gives a quasi equilibrium description on how glassy systems explore the phase space in the slow relaxation part of their dynamics. The second one is that it gives an alternative way to obtain dynamical equations starting from a dynamical rule that is static in spirit. This provides a way to overcome the difficulties encountered in the short time part of the dynamics where current conservation must be enforced. We study this approach in detail in a prototypical mean field disordered spin system, namely the p-spin spherical model, showing how we can obtain the well known equations that describe its dynamics. Then we apply the same approach to structural glasses. We first derive a set of dynamical Ornstein-Zernike equations which are very general in nature. Finally we consider two possible closure schemes for them, namely the hypernetted chain approximation of liquid theory and a closure of the BBGKY hierarchy that has been recently introduced by Szamel. From both approaches we finally find a set of dynamical mode-coupling-like equations that are supposed to describe the system in the long time/slow dynamics regime.
Analytical Approach for Loss Minimization in Distribution Systems by Optimum Placement and Sizing of Distributed Generation

Distributed Generation has drawn the attention of industrialists and researchers for quite a time now due to the advantages it brings loads. In addition to cost-effective and environmentally friendly, but also brings higher reliability coefficient power system. The DG unit is placed close to the load, rather than increasing the capacity of main generator. This methodology brings many benefits, but has to address some of the challenges. The main is to find the optimal location and size of DG units between them. The purpose of this paper is distributed generation by adding an additional means to reduce losses on the line. This paper attempts to optimize the technology to solve the problem of optimal location and size through the development of multi-objective particle swarm. The problem has been reduced to a mathematical optimization problem by developing a fitness function considering losses and voltage distribution line. Fitness function by using the optimal value of the size and location of this algorithm was found to be minimized. IEEE-14 bus system is being considered, in order to test the proposed algorithm and the results show improved performance in terms of accuracy and convergence rate.
Efficient Multi-keyword Ranked Search over Outsourced Cloud Data based on Homomorphic Encryption

With the development of cloud computing, more and more data owners are motivated to outsource their data to the cloud server for great flexibility and less saving expenditure. Because the security of outsourced data must be guaranteed, some encryption methods should be used which obsoletes traditional data utilization based on plaintext, e.g. keyword search. To solve the search of encrypted data, some schemes were proposed to solve the search of encrypted data, e.g. top-k single or multiple keywords retrieval. However, the efficiency of these proposed schemes is not high enough to be impractical in the cloud computing. In this paper, we propose a new scheme based on homomorphic encryption to solve this challenging problem of privacy-preserving efficient multi-keyword ranked search over outsourced cloud data. In our scheme, the inner product is adopted to measure the relevance scores and the technique of relevance feedback is used to reflect the search preference of the data users. Security analysis shows that the proposed scheme can meet strict privacy requirements for such a secure cloud data utilization system. Performance evaluation demonstrates that the proposed scheme can achieve low overhead on both computation and communication.
Progressive Attention Guided Recurrent Network for Salient Object Detection

Effective convolutional features play an important role in saliency estimation but how to learn powerful features for saliency is still a challenging task. FCN-based methods directly apply multi-level convolutional features without distinction, which leads to sub-optimal results due to the distraction from redundant details. In this paper, we propose a novel attention guided network which selectively integrates multi-level contextual information in a progressive manner. Attentive features generated by our network can alleviate distraction of background thus achieve better performance. On the other hand, it is observed that most of existing algorithms conduct salient object detection by exploiting side-output features of the backbone feature extraction network. However, shallower layers of backbone network lack the ability to obtain global semantic information, which limits the effective feature learning. To address the problem, we introduce multi-path recurrent feedback to enhance our proposed progressive attention driven framework. Through multi-path recurrent connections, global semantic information from the top convolutional layer is transferred to shallower layers, which intrinsically refines the entire network. Experimental results on six benchmark datasets demonstrate that our algorithm performs favorably against the state-of-the-art approaches.
Insects as Food from Deserted Areas in Mexico

Aims: The aim of this paper is to detect the edible insects from an arid zone of the State of Hidalgo, assess their macronutrient value and deliver that information to the local population. Place and Duration of Study: Convenience sampling of wild insects were provided at an arid zone of the Hidalgo State, northeast of Actopan city located 2,100 masl with an arid and semiarid climate throughout 2015. Methodology: Macronutrient content of insects from different order (three species of Lepidoptera, two species of Coleoptera, three species of Hymenoptera, one of Orthoptera and one of Hemiptera) were analyzed according to AOAC (1995) methods [1-3]. Results: Results ranged as follows: proteins from 9.85% to 74.85%; lipids from 5.85% to 55.85%, total ashes from 2.95% to 7.15%; fiber from 0.72% to 6.75% and soluble carbohydrates from 8.87% to 79.16%. Data showed that insects have good nutritional values. Their reproduction is seasonal Original Research Article but some species can be found all year long and hence represent good alternative to provide food security. Conclusion: Wild insects collected in an arid zone of Hidalgo State showed adequate chemical composition values for human diet, with significant levels of proteins, low dietary fiber content, high values of fatty acids essential for nutrition and minerals. These small wild animals are a promising source of food to overcome malnutrition problems faced by poor population and provide food security.
Occurrence and effectiveness of an indigenous strain of Myrothecium roridum Tode: Fries as a bioherbicide for water hyacinth (Eichhornia crassipes) in Nigeria

In a study to isolate fungal pathogens with potential for the biocontrol of water hyacinth (Eichhornia crassipes), some lakes in the Lagos State and its environs, Nigeria, were surveyed for diseased water hyacinth (E. crassipes). The fungi present in the diseased tissue were isolated and identified as: Aspergillus niger, Aspergillus flavus, Penicillium sp. , Curvularia pallescens, Fusarium solani and Myrothecium roridum. The pathogenicity of isolates of these fungi on fresh, non-diseased water hyacinth plants was investigated. Myrothecium was the only species capable of inducing disease symptoms. Necrosis was observed on water hyacinth leaves three days post inoculation (DPI) with M. roridum (1 × 106 spores/ml). The leaves and the petioles were withered at the end of day 24, and the disease incidence and disease severity were 100% and 8.67%, respectively. Molecular analysis of the internal transcribed spacer rDNA of the M. roridum isolate from water hyacinth showed >98% homology to authenticated sequences of M. roridum. The isolate, deposited at the International Mycological Institute, UK, as M. roridum Tode: Fries (IMI 394934), possesses the level of virulence needed in a potential mycoherbicide for use in the management of water hyacinth.
From Contemplation to Action: Mechanisms of Change in the Mentoring Academy

Mentoring is fundamental to the professional development of research scientists in academic health centers (AHC). Qualified mentors can support the development of competencies considered most significant in training research scientists. Yet AHC faculty may have little preparation in and knowledge of how to mentor. Emerging AHC mentor academies provide educational environments whereby faculty can learn the art and practice of mentoring. However, little is known about their effectiveness. Using the Transtheoretical Change Model (TTM), this study explored how 23 mentors used newly learned information to change their communication styles and develop shared expectations with mentees. Based on an inductive analysis of 46 reflective writing assignments, the results showed that the mentor academy enabled progress through the stages of contemplation and preparation, however, mentors rarely implemented new knowledge to make changes in their approach to mentoring. The authors suggest instructional strategies that will promote actionable change and accountability for implementation.