nils-herrmann/clean_scirepeval huggingface dataset:
Difference in details: transfer learning case study of "cryptic" plants and moths

Can we classify species of very similar looking organisms quickly and accurately using only out of the box feature transfer? What if we only have small number of images? This experimental paper is part of on-going project on species recognition research and evaluates transfer learning and fine-tuning approaches on two highly specialized fine-grained datasets. The two fine-grained datasets were specifically assembled for the purpose of this research. These datasets consist of images of New Zealand native species of moths and ”cryptic” plants of Genus Corposma found also in New Zealand. We compare results from finetuning experiments with performance of transfer learning without fine-tuning. The latter results are based on features extracted from various levels of depth in the InceptionV3 network, including fully connected layers. The extracted features serve as inputs to a number of classification algorithms. We observe contrasting results for the two datasets. For the dataset of moths, the method based on features extracted from deep levels of the InceptionV3 network outperforms fine-tuning in accuracy (90.09% versus 87.18%). This is not the case for the dataset of cryptic plants (60.46% versus 74.37%). Despite both datasets being fine-grained in nature, these experimental differences could be attributed to intrinsically different morphology of organisms and warrant further investigation.
Recent Advances in Industrial Wireless Sensor Networks Toward Efficient Management in IoT

With the accelerated development of Internet-of-Things (IoT), wireless sensor networks (WSNs) are gaining importance in the continued advancement of information and communication technologies, and have been connected and integrated with the Internet in vast industrial applications. However, given the fact that most wireless sensor devices are resource constrained and operate on batteries, the communication overhead and power consumption are therefore important issues for WSNs design. In order to efficiently manage these wireless sensor devices in a unified manner, the industrial authorities should be able to provide a network infrastructure supporting various WSN applications and services that facilitate the management of sensor-equipped real-world entities. This paper presents an overview of industrial ecosystem, technical architecture, industrial device management standards, and our latest research activity in developing a WSN management system. The key approach to enable efficient and reliable management of WSN within such an infrastructure is a cross-layer design of lightweight and cloud-based RESTful Web service.
Flexible-Term Contracts for Road Franchising

Private participation in road projects is increasing around the world. The most popular franchising mechanism is a concession contract, which allows a private firm to charge tolls to road users during a pre-determined period in order to recover its investments. Concessionaires are usually selected through auctions at which candidates submit bids for tolls, payments to the government, or minimum term to hold the contract. This paper discusses, in the context of road franchising, how this mechanism does not generally yield optimal outcomes and it induces the frequent contract renegotiations observed in road projects. A new franchising mechanism is proposed, based on flexible-term contracts and auctions with bids for total net revenue and maintenance costs. This new mechanism improves outcomes compared to fixed-term concessions, by eliminating traffic risk and promoting the selection of efficient concessionaires.
Deep Retinal Image Understanding

This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in other fields of computer vision such as object detection and image classification, and we bring their power to the study of eye fundus images. DRIU uses a base network architecture on which two set of specialized layers are trained to solve both the retinal vessel and optic disc segmentation. We present experimental validation, both qualitative and quantitative, in four public datasets for these tasks. In all of them, DRIU presents super-human performance, that is, it shows results more consistent with a gold standard than a second human annotator used as control.
New types of deep neural network learning for speech recognition and related applications: an overview

In this paper, we provide an overview of the invited and contributed papers presented at the special session at ICASSP-2013, entitled “New Types of Deep Neural Network Learning for Speech Recognition and Related Applications,” as organized by the authors. We also describe the historical context in which acoustic models based on deep neural networks have been developed. The technical overview of the papers presented in our special session is organized into five ways of improving deep learning methods: (1) better optimization; (2) better types of neural activation function and better network architectures; (3) better ways to determine the myriad hyper-parameters of deep neural networks; (4) more appropriate ways to preprocess speech for deep neural networks; and (5) ways of leveraging multiple languages or dialects that are more easily achieved with deep neural networks than with Gaussian mixture models.
Determining Image Similarity from Pattern Matching of Abstract Syntax Trees of Tree Picture Grammars

This paper studies the use of tree edit distance for pattern matching of abstract syntax trees of images generated with tree picture grammars. This was done with a view to measuring its effectiveness in determining image similarity, when compared to current state of the art similarity measures used in Content Based Image Retrieval (CBIR). Eight computer based similarity measures were selected for their diverse methodology and effectiveness. The eight visual descriptors and tree edit distance were tested against some of the images from our corpus of thousands of syntactically generated images. The first and second sets of experiments showed that tree edit distance and Spacial Colour Distribution (SpCD) are the most suited for determining similarity of syntactically generated images. A third set of experiments was performed with tree edit distance and SpCD only. Results obtained showed that while both of them performed well in determining similarity of the generated images, the tree edit distance is better able to detect more subtle human observable image differences than SpCD. Also, tree edit distance more closely models the generative sequence of these tree picture grammars.
Endophytic microorganisms: a review on insect control and recent advances on tropical plants

In the past two decades, a great deal of information on the role of endophytic microorganisms in nature has been collected. The capability of colonizing internal host tissues has made endophytes valuable for agriculture as a tool to improve crop performance. In this review, we addressed the major topics concerning the control of insects-pests by endophytic microorganisms. Several examples of insect control are described, notably those involving the interactions between fungi and grazing grasses from temperate countries. The mechanisms by which endophytic fungi control insect attacks are listed and include toxin production as well as the influence of these compounds on plant and livestock and how their production may be affected by genetic and environmental conditions. The importance of endophytic entomopathogenic fungi for insect control is also addressed. As the literature has shown, there is a lack of information on endophytes from tropical hosts, which are more severely affected by pests and diseases. Having this in mind, we have included an updated and extensive literature in this review, concerning new findings from tropical plants, including the characterization of endophytic fungi and bacteria microbiota from several Amazon trees, citrus and
Trivial or Knot : A Software Tool and Algorithms for Knot Simplification

A special type of representation for knots and for local knot manipulations is described and used in a software tool called TOK to implement a number of algorithms on knots. Two algorithms for knot simplification are described: simulated annealing applied to the knot representation, and a “divide-simplify-join” algorithm. Both of these algorithms make use of the compact knot representation and of the basic mechanism TOK provides for carrying out a predefined knot manipulation on the knot representation. The simplification algorithms implemented with the TOK system exploit local knot manipulations and have proven themselves effective for simplifying even very complicated knots in reasonable time.  What is Knot Theory? Knots are very complicated mathematical objects that have intuitive, real-world counterparts. This makes them very interesting to study. A tangle in a (frictionless ) rope is a knot if when the ends of the rope are pulled in opposite directions, the tangle is not unraveled. Given a pile of rope with two ends sticking out, it is difficult, or even impossible to say by inspection whether or not the rope is truly knotted. An even more difficult problem is to decide if two piles of tangled rope are equivalent; meaning that one pile may be stretched and deformed to look like the other pile without tearing the rope.  1 illustrates that equivalence is sometimes not obvious even for simple knots. Figure 1. (a) Two trefoil knots (b) Two trivial knots Knot theory studies an abstraction of the intuitive “knot on a rope” notion. The theory deals with questions such as proving knottedness, and classifying types of knottedness. In a more abstract sense we may say that knot theory studies the placement problem: “Given spaces X andY, classify howX may be placed inY” . Here a placement is usually an embedding, and classification often means up to some form of movement. In these terms classical knot theory studies embeddings of a circle in Euclidean three space. (Hence we consider the two ends of the rope tied together) There are two main schools in knot theory research. The first is called combinatorial or pictorial knot theory. Here the main idea is to associate with the mathematical object a drawing that represents the knot, and to study various combinatorical properties of this drawing. The second school considers the abstract notion of a knot as an embedding and studies the topology of the so called complementary space of the image of the embedding, by applying to this space the tools of Algebraic Topology. This paper dwells in the first realm pictorial knot theory. Following is a brief description of the basic theory that is needed to understand the TOK knot manipulation tool. For a more comprehensive overview see [1][2][3].  Electrical Engineering Department Technion, 3200 Haifa, Israel danl@tx.technion.ac.il orli@tx.technion.ac.il ** Computer Science Department Technion, 3200 Haifa, Israel (on sabbatical at AT&T Bell Laboratories, Murray Hill, NJ 07974, USA)
Forward-flyback mixed ZVS DC-DC converter with non-dissipative LC snubber circuit

This paper presents a zero-voltage switching (ZVS) forward-flyback DC-DC converter, which is able to process and deliver power efficiently over very wide input voltage variation. The proposed ZVS forward flyback DC/DC converter is part of a Micro-inverter to perform input voltage regulation to achieving maximum power point tracking for Photo-voltaic panel. The converter operates at boundary between current continuous and discontinuous mode to achieve ZVS. Variable frequency with fixed off time is used for reducing core losses of the transformer, achieving high efficiency. In addition, non-dissipative LC snubber circuit is used to get both benefits: 1) the voltage spike is restrained effectively while the switch is turned off with high current in primary side; 2) the main switch still keeps ZVS feature. Finally, experiment results provided from a 200W prototype (30Vdc-50Vdc input, 230Vdc output) validate the feasibility and superior performance of the proposed converter.
Nutritional status of school-age children - A scenario of urban slums in India

One of the greatest problems for India is undernutrition among children. The country is still struggling with this problem. Malnutrition, the condition resulting from faulty nutrition, weakens the immune system and causes significant growth and cognitive delay. Growth assessment is the measurement that best defines the health and nutritional status of children, while also providing an indirect measurement of well-being for the entire population.



A cross-sectional study, in which we explored nutritional status in school-age slum children and analyze factors associated with malnutrition with the help of a pre-designed and pre-tested questionnaire, anthropometric measurements and clinical examination from December 2010 to April 2011 in urban slums of Bareilly, Uttar-Pradesh (UP), India.



The mean height and weight of boys and girls in the study group was lower than the CDC 2000 (Centers for Disease Control and Prevention) standards in all age groups. Regarding nutritional status, prevalence of stunting and underweight was highest in age group 11 yrs to 13 yrs whereas prevalence of wasting was highest in age group 5 yrs to 7 yrs. Except refractive errors all illnesses are more common among girls, but this gender difference is statistically significant only for anemia and rickets. The risk of malnutrition was significantly higher among children living in joint families, children whose mother's education was [less than or equal to] 6th standard and children with working mothers.



Most of the school-age slum children in our study had a poor nutritional status. Interventions such as skills-based nutrition education, fortification of food items, effective infection control, training of public healthcare workers and delivery of integrated programs are recommended.
A fast and robust text spotter

We introduce an algorithm for text detection and localization ("spotting") that is computationally efficient and produces state-of-the-art results. Our system uses multi-channel MSERs to detect a large number of promising regions, then subsamples these regions using a clustering approach. Representatives of region clusters are binarized and then passed on to a deep network. A final line grouping stage forms word-level segments. On the ICDAR 2011 and 2015 benchmarks, our algorithm obtains an F-score of 82% and 83%, respectively, at a computational cost of 1.2 seconds per frame. We also introduce a version that is three times as fast, with only a slight reduction in performance.
Network Pharmacology Strategies Toward Multi-Target Anticancer Therapies: From Computational Models to Experimental Design Principles

Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.
Learning discriminative instance attribute for zero-shot classification

We study the problem of zero-shot classification in which we don't have labeled data in target domain. Existing approaches learn a model from source domain and apply it without adaptation to target domain, which is prone to domain shift problem. To solve the problem, we propose a novel Learning Discriminative Instance Attribute(LDIA) method. Specifically, we learn a projection matrix for both the source and target domain jointly and also use prototype in the attribute space to regularise the learned projection matrix. Therefore, the information of the source domain can be effectively transferred to the target domain. The experimental results on two benchmark datasets demonstrate that the proposed LDIA method exceeds competitive approaches for zero-shot classification task.
Using glTF for streaming CityGML 3D city models

In this paper, we describe an approach for efficiently streaming large and highly detailed 3D city models, which is based on open standards and open source developments. This approach meets both the rendering performance requirements in WebGL enabled web browsers and the requirements by 3D Geographic Information Systems regarding data structuring, geo-referencing and accessibility of feature properties. 3D city models are assumed to be available as CityGML data sets due to its widespread adoption by public authorities. The Cesium.js open source virtual globe is used as a platform for embedding custom 3D assets. glTF and related formats are used for efficiently encoding 3D data and for enabling streaming of large 3D models. In order to fully exploit the capabilities of web browsers and standard internet protocols, a series of filtering and data processing steps must be performed, which are described in this paper.
Constructing Probability Boxes and Dempster-Shafer Structures

This report summarizes a variety of the most useful and commonly applied methods for obtaining Dempster-Shafer structures, and their mathematical kin probability boxes, from empirical information or theoretical knowledge. The report includes a review of the aggregation methods for handling agreement and conflict when multiple such objects are obtained from different sources.  The work described in this report was performed for Sandia National Laboratories under Contract No.
A Single-Item Inventory Model for a Nonstationary Demand Process

In this paper, we consider an adaptive base-stock policy for a single-item inventory system, where the demand process is non-stationary. In particular, the demand process is an integrated moving average process of order (0, 1, 1), for which an exponential-weighted moving average provides the optimal forecast. For the assumed control policy we characterize the inventory random variable and use this to find the safety stock requirements for the system. From this characterization, we see that the required inventory, both in absolute terms and as it depends on the replenishment lead-time, behaves much differently for this case of non-stationary demand compared with stationary demand. We then show how the single-item model extends to a multistage, or supply-chain context; in particular we see that the demand process for the upstream stage is not only non-stationary but also more variable than that for the downstream stage. We also show that for this model there is no value from letting the upstream stages see the exogenous demand. The paper concludes with some observations about the practical implications of this work.
A Taxonomy and Future Directions for Sustainable Cloud Computing: 360 Degree View

The cloud-computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT)--based applications, the use of cloud services is increasing exponentially. The next generation of cloud computing must be energy efficient and sustainable to fulfill end-user requirements, which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The use of a large number of cloud datacenters increases cost as well as carbon footprints, which further affects the sustainability of cloud services. In this article, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. The current research on sustainable cloud computing is organized into several categories: application design, sustainability metrics, capacity planning, energy management, virtualization, thermal-aware scheduling, cooling management, renewable energy, and waste heat utilization. The existing techniques have been compared and categorized based on common characteristics and properties. A conceptual model for sustainable cloud computing has been presented along with a discussion on future research directions.
Modulation recognition using hierarchical deep neural networks

We outline the core components of a modulation recognition system that uses hierarchical deep neural networks to identify data type, modulation class and modulation order. Our system utilizes a flexible front-end detector that performs energy detection, channelization and multi-band reconstruction on wideband data to provide raw narrowband signal snapshots. We automatically extract features from these snapshots using convolutional neural network layers, which produce decision class estimates. Initial experimentation on a small synthetic radio frequency dataset indicates the viability of deep neural networks applied to the communications domain. We plan to demonstrate this system at the Battle of the Mod Recs Workshop at IEEE DySpan 2017.
CRISPR Critters and CRISPR Cracks.

This essay focuses on possible nonhuman applications of CRISPR/Cas9 that are likely to be widely overlooked because they are unexpected and, in some cases, perhaps even "frivolous." We look at five uses for "CRISPR Critters": wild de-extinction, domestic de-extinction, personal whim, art, and novel forms of disease prevention. We then discuss the current regulatory framework and its possible limitations in those contexts. We end with questions about some deeper issues raised by the increased human control over life on earth offered by genome editing.
An Omnidirectional MEMS Ultrasonic Energy Harvester for Implanted Devices

This paper presents the design and characterization of a microelectromechanical systems (MEMS)-based energy harvester with target applications, including implanted biomedical sensors and actuators. The harvester is designed to utilize ultrasonic waves from an external transmitter for mechanical excitation, with electrostatic transducers being used to convert the vibrations of a central mass structure into electrical energy. The device features a novel 3-degrees of freedom design, which enables energy to be produced by the harvester in any orientation. The harvester is fabricated using a conventional silicon-on-insulator MEMS process, with experimental testing showing that the system is able to generate 24.7, 19.8, and 14.5nW of electrical power, respectively, via the device's x-, y- and z-axis resonance modes over a 15-s period.