Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Way Feature-Aligned And Attention-Rectified Adversarial Training

Adversarial training increases robustness by augmenting training data with adversarial examples. However, vanilla adversarial training may be overfitting to certain adversarial attacks. Small perturbations in images bring in error which is gradually amplified when forwarded through the model so that the error leads to wrong classification. Besides, small perturbations will also distract classifier’s attention to significant features that are relevant to the true label. In this paper, we propose a novel two-way feature-aligned and attention-rectified adversarial training (FAAR) to improve adversarial training (AT). FAAR utilizes two-way feature alignment and attention rectification to mitigate the problems mentioned above. FAAR effectively suppresses perturbations in lowlevel, high-level and global features by moving features of perturbed images towards those of clean images with twoway feature alignment. It also leads the model into focusing more on useful features which are correlated with true label through rectifying gradient-weighted attention. Besides, feature alignment activates attention rectification by reducing perturbations in high-level feature. Our proposed method FAAR surpasses other existing AT methods in three aspects. First, it pushes the model to keep invariant when dealing with different adversarial attacks and different magnitude of perturbations. Second, it can be applied to any convolution neural networks. Third, the training process is end-to-end. For experiments, FAAR shows promising defense performance on CIFAR-10 and ImageNet.
Self-Heating in FDSOI UTBB MOSFETs at Cryogenic Temperatures and its Effect on Analog Figures of Merit

This work studies the self-heating (SH) effect in ultra-thin body ultra-thin buried oxide (UTBB) FDSOI MOSFETs at cryogenic temperatures down to 77 K. S-parameter measurements in a wide frequency range, with the so-called RF technique, are employed to assess SH parameters and related variation of analog figures of merit (FoMs) at different temperatures. Contrary to the expectations, the effect of self-heating on analog FoMs is slightly weaker at cryogenic temperatures with respect to room-temperature case. The extracted thermal resistance and channel temperature rise at 300 K and 77 K in short-channel devices are of the same order of magnitude. The observed increase in SH characteristic frequency with temperature reduction emphasizes the advantage of the RF technique for the fair analysis of SH-related features in advanced technologies at cryogenic temperatures.
Effective Decoupled Planning for Continuous Multi-Agent Pickup and Delivery

Continuous MAPD (Multi-Agent Pickup and Delivery) problem is widely encountered in material transportation systems. TP (Token Passing) is a conflict-free, deadlock-free algorithm for continuous MAPD problems. However, when TP is used, the number of tasks simultaneously handled by agents is limited by the number of task endpoint locations. To solve this problem, ITP (Improved Token Passing) is proposed in this paper. An extra path planning procedure is introduced to improve agent utilization. A complete and optimal A*-based algorithm is designed to solve the low level single agent path planning problem. ITP increases system throughput by exploiting all available agents, and remains conflict-free and deadlock-free. Experiment result shows that ITP outperforms TP in terms of system makespan under heavy workload in congested workspace.
Longitudinal Magnetic Field Effect in Critical Current Characteristics of YBCO Superconducting Tape

This study’s primary purpose is to identify the critical current characteristics of YBCO superconducting tapes through experiments, discussing the improvement of the critical current density of YBCO superconducting tapes by using longitudinal magnetic field effect to lay foundations to the future application of YBCO new superconducting cable. In the experiment, we measured the critical current values of YBCO superconducting tapes at different temperatures, different magnetic field intensity, and different angles. Through the analysis of experimental results, more useful information can be provided for superconducting cable developers. The experimental results show that the longitudinal magnetic field has a modest effect on the critical current at low magnetic field intensity while it has a conspicuous effect on the critical current at high magnetic field intensity.
Treatment of dairy waste by anaerobic digestion to produce methane as green energy

Treatment and management of lactoserum from dairy industry has become a global issue. In fact, generated effluents have high organic loads (lactose, protein, vitamin, minerals etc.) constituting a major source of environmental pollution. In a sustainable green approach, anaerobic digestion is considered a promising solution for the treatment of lactoserum or cheese whey. At first, we conducted a biochemical characterization showing a lactose composition of 52 and 46 g/kg, for sweet and acidic cheese whey, respectively, with an average COD of about 70 g/kg. The biodegradability of sweet lactoserum showed an extremely high value of 97% equivalent to 0.34 $\mathrm{N}\mathrm{m}^{3}\mathrm{C}\mathrm{H}_{4}\cdot \mathrm{k}\mathrm{g}\ \mathrm{C}\mathrm{O}\mathrm{D}^{-1}$. However, low biodegradability was noted for acidic lactoserum with a value of17%. This highlights the importance of the use of the sweet one in terms of energy recovery. For the optimization of the process, we also tried an incursion towards little explored tracks by evaluating the intensification of methane production, applying ultrasounds pretreatment on inoculum. The best yield was obtained after sonication with an energy input of 90 kJ/kg.
YOLO-compact: An Efficient YOLO Network for Single Category Real-time Object Detection

In practical applications, the number of category in object detection is always single. In this paper, an efficient YOLO-compact network designed for single category real-time object detection is proposed. This paper first explored a series of methods for converting a large and deep network to a compact and efficient network, through a series of ablation experiments. Then these methods were assembled to YOLOv3 network, which obtains the YOLO-compact’s infrastructure. A network structure design approach that separates the down sampling layer from all network modules was proposed, which facilitates the modular design of the network. The shortcut structure in the RFB module is changed to the direct connection structure, and the 1×3+3×1+3×3 convolution structure is used instead of 5×5 convolution, which obtains efficient RFB-c module. The residual bottleneck block has been improved, by removing the last 1×1 convolution layer and using 3×3 depth wise separable convolution. Since pedestrian is the most representative object in practical applications, this paper uses the result on person category in VOC2007 test set to represent the network performance. The model size of YOLO-compact is only 9MB, which is 3.7 times smaller than tiny-yolov3, 6.7 times smaller than tiny-yolov2, and 26 times smaller than YOLOv3. The AP result of YOLO-compact is 86.85%, which is 37% higher than tiny-yolov3, 32% higher than tiny-yolov2, and even 2.7% higher than the YOLOv3.
A Tomato Leaf Diseases Classification Method Based on Deep Learning

In the process of planting crops, the detection of diseases in the leaf parts is one of the key links to the prevention and control of crop diseases. This paper takes tomato leaves as experimental objects, and uses the deep learning method to extract the disease features on leaf surface, including three most common species (Spot blight, Late blight and Yellow leaf curl disease). After continuous iterative learning, the network can predict the category of each disease picture. For each of the three diseases, 1000 pictures were selected, divided into 900 pictures for training set (2700 in total) and 100 pictures for test set (300 in total). The experiment takes Resnet-50 as the basic network model. For comparison, the activation function of the network was changed into Leaky-ReLU and the kernel size of the first convolutional layer was modified to 11×11. After the improvement, the training accuracy in training set is 98.3% (increased by 0.6%) and the test accuracy in test set is 98.0% (increased by 2.3%).
The individualistic concept of the plant association

The units of vegetation, which form the subject of our discussions this week, were just as easily visible to primitive man as they are to us today. They were equally visible to the sages of Greece and Rome two thousand years ago, to our European ancestors a thousand years ago. Through all these long years they were neglected by scientists, whose thoughts were turned in different directions, but they were recognized by -the laity. As a result the languages of Europe all contain a number of terms which refer, usually rather loosely, to vegetation.
An Introduction to Pollen Analysis.

POLLEN analysis is the term applied to the quantitative analysis of material containing pollen, by microscopic recognition of the species and genera of plants from which the pollen came. The pollen membranes have qualities of shape, size, surface, and structure which permit these identifications, and their preservation is often excellent. An Introduction to Pollen Analysis By Dr. G. Erdtman. (A New Series of Plant Science Books, Vol. 12.) Pp. xvi + 240. (Waltham, Mass.: Chronica Botanica Co.; London: Wm. Dawson and Sons, Ltd., 1943.) 5 dollars.
CHROMOSOME NUMBERS IN THE COMPOSITAE.

Reports of 150 original chromosome counts are recorded, including reports of 22 genera and 57 species and subspecific taxa in tribe Lactuceae. Also included are first reports for 12 specific or subspecific taxa. x = 9 appears to be the ancestral base of the tribe. Chromosome numbers are known for over 85% of the genera of the tribe and the frequency of polyploidy is ca. 23%, which is about one-half that of the angiosperms.
A Monocular SLAM System with Mask Loop Closing

In recent years, monocular simultaneous localization and mapping (SLAM) algorithms have achieved good performance, while most of them could not deal with dynamic environments. This paper proposes a novel monocular SLAM system with mask loop closing (MLC-SLAM) to deal with dynamic objects. The proposed system combines the direct and feature-based methods to track fast and robustly. The proposed approach achieves motion refinement and loop closing in the feature-based module, which exploits semantic information by training a visual vocabulary offline with semantic labels from the mask network. Evaluation on the TUM mono VO dataset shows that MLC-SLAM achieves better performance than the state-of-the-art monocular SLAM algorithms: DSO and ORB-SLAM, in terms of accuracy, robustness, and the performance of loop closing.
Text to Image Synthesis With Bidirectional Generative Adversarial Network

Generating realistic images from text descriptions is a challenging problem in computer vision. Although previous works have shown remarkable progress, guaranteeing semantic consistency between text descriptions and images remains challenging. To generate semantically consistent images, we propose two semantics-enhanced modules and a novel Textual-Visual Bidirectional Generative Adversarial Network (TVBi-GAN). Specifically, this paper proposes a semanticsenhanced attention module and a semantics-enhanced batch normalization module. These modules improve consistency of synthesized images by involving precisely semantic features. What’s more, an encoder network is proposed to extract semantic features from images. During the adversarial process, the encoder could guide our generator to explore corresponding features behind descriptions. With extensive experiments on CUB and COCO datasets, we demonstrate that our TVBi-GAN outperforms state-of-the-art methods.
Biological notes on some Pyramidellidae (Gastropoda: Opisthobranchia) from Norway

Abstract Host-parasite relationships for several species of shallow water pyramidellids from Norwegian waters are described. Pomatoceros reefs were found to be substrate for as many as seven species of pyramidellids simultaneously. Circumstantial evidence for Limaria hians as host for Brachystomia rissoides (Hanley) is provided. Odostomia acuta Jeffreys is shown to be an associate of the sabellid, Myxicola infundibulum. Eggmasses of O. acuta were found embedded in the gelatinous walls of the tube of this polychaete. Eggs and recently hatched larvae of O. acuta are described for the first time. The first Norwegian record of Evalea perezi Dautzenberg & Fischer as an associate of Phascolion strombi is given.
On the Security of Permissioned Blockchain Solutions for IoT Applications

The blockchain has found numerous applications in many areas with the expectation to significantly enhance their security. The Internet of things (IoT) constitutes a prominent application domain of blockchain, with a number of architectures having been proposed for improving not only security but also properties like transparency and auditability. However, many blockchain solutions suffer from inherent constraints associated with the consensus protocol used. These constraints are mostly inherited by the permissionless setting, e.g. computational power in proof-of-work, and become serious obstacles in a resource-constrained IoT environment. Moreover, consensus protocols with low throughput or high latency are not suitable for IoT networks where massive volumes of data are generated. Thus, in this paper we focus on permissioned blockchain platforms and investigate the consensus protocols used, aiming at evaluating their performance and fault tolerance as the main selection criteria for (in principle highly insecure) IoT ecosystem. The results of the paper provide new insights on the essential differences of various consensus protocols and their capacity to meet IoT needs.
Captura de aves silvestres no semiárido brasileiro: técnicas cinegéticas e implicações para conservação

A captura de aves silvestres é uma prática bastante comum no Nordeste brasileiro e envolve diversas técnicas de caça, as quais são adotadas conforme o tipo de espécie e a finalidade a que se destina o animal captuado. Esta pesquisa buscou caracterizar as técnicas de captura de aves utilizadas por populações urbano-rurais na região do Seridó Ocidental, no Estado do Rio Grande do Norte. A área de estudo englobou os municípios de Caicó, São João do Sabugi, Serra Negra do Norte, Timbaúba dos Batistas. As informações foram obtidas entre os meses de setembro de 2009 e março de 2010 através de entrevistas semiestruturadas aplicadas a 120 moradores locais que utilizavam ou interagiam com aves silvestres de alguma forma. Foram identificadas 11 técnicas de captura: “assaprã”, “manual”, “visgo”, “arapuca”, “arremedo”, “espera”, “fôjo”, “sangra”, “rede”, caça ativa noturna e caça com cachorros. O assaprã, a espera e a manual foram as técnicas mais disseminadas na área de estudo, apresentando, respectivamente, 76, 43 e 39 citações. Os sertanejos detêm um conjunto de informações relacionadas às aves, tais como habitat, alimentação, período do ano e técnicas apropriadas para a captura, que são de suma importância para o sucesso na atividade cinegética. Assim, o conhecimento dos diferentes tipos de técnicas de captura é importante para definir quais as melhores estratégias poderão ser adotadas pelos órgãos ambientais, de modo a garantir tanto a sobrevivência das espécies utilizadas quanto das comunidades que delas dependem.
Multi-scale Learning based Malware Variant Detection using Spatial Pyramid Pooling Network

Due to the enormous growth of the Internet, cyberspace faces ever-increasing threats. Therefore, the role of a security framework to handle malware threats is essential in the current era. Some malware files are capable of forming a network of infected systems that are exploited by the attackers to perform many cyber attacks like distributed denial of service (DDOS), Phishing, etc. Malware is also employed to steal critical information from the host systems. Apart from the traditional approaches like static and dynamic analysis that suffer from various challenges such as sensitivity to obfuscation methods and computational overhead, image-based malware detection approaches are also studied by many researchers. The existing approaches need a fixed size image input. Instead, a multi-scale learning method can be utilized to enhance the performance of the detection model. Hence, in this work, spatial pyramid pooling (SPP) based malware variant detection models are proposed and their performance is compared with the existing relevant works. The experiments reveal that the proposed technique produces an accuracy of 99% and it outperforms the existing relevant works.
Influence of Hunger Level and Prey Densities on Movement Patterns in Three Species of Pterostichus Beetles (Coleoptera: Carabidae)

The foraging behavior of hungry and satiated individuals of three species of polyphagous and predatory carabids was studied by tracking beetles in cereal fields, using a portable harmonic radar system. Fine-scale movement patterns were investigated in both low and high density of the bird cherry-oat aphid, Rhopalosiphum padi (L.). In low aphid density, hungry individuals of the day-active Pterostichus cupreus L. switched from directed to random movements (i.e., a correlated random walk), whereas both satiated P. cupreus and hungry individuals of the night-active P. melanarius Illiger displayed directed movements only. Conversely, the movement patterns of satiated P. melanarius and both satiated and hungry individuals of the night-active P. niger Schaller could be described as a correlated random walk. In high aphid density, hungry and satiated P. cupreus displayed directed movements. However, hungry P. cupreus exhibited more efficient systematic search activities; foraging on aphids near the base of cereal plants. The movement patterns of both hungry and satiated P. melanarius could be described as a correlated random walk related to burrowing into the soil. Satiated P. niger displayed directed movements but never foraged, whereas hungry P. niger exhibited a correlated random walk related to near-search activities, foraging on coccinelids near the base of cereal plants. No sex-related difference in maximum sprint speed was detected when the beetles were released on a smooth, regular surface. Conversely, only female carabids were found to alter their speed of movement according to differences in both hunger level and prey (aphid) density in cereal fields. The influence of motivation (hunger level) on movement patterns and foraging behavior exhibited by P. cupreus , P. melanarius , and P. niger and the associated species-specific responses to differences in aphid density are discussed in relation to energy costs and predation efficiency.
From Space to Earth: physical and biological impacts of glacier dynamics in the marine system by means of Remote Sensing at Almirantazgo Bay, Antarctica

To determine the biological and physical mechanisms between Lange glacier and its pro-glacier marine system located in Almirantazgo bay, King George Island, Antarctica, specific variables were determined through remote sensing approaches shown in this work. These preliminary results will allow relating the dynamics of both systems, the glacier, and the marine ecosystem. The information for the estimation of surface flux velocity of the glacier was derived through Radar satellite images (Sentinel-1) by means of offset tracking, the bathymetry was derived from a Multi-beam Echo Sounder, and the Digital Elevation Modell was obtained by means of a Remotely Piloted Aircraft; Finally, the biological parameters were derived from MODIS and OLCI images for the analysis of satellite data to have a first insight to the characteristics of the marine system. This information will help to build the first frame needed to study through remote sensing approaches, the mechanisms that govern the interface among “Lang Glacier” and the “Almirantazgo Bay” at King George Island, Antarctica.
Microstrip Matrix Butler Area which is Reduced by the use of Compact Directional Couplers

The paper developed and investigated a microstrip matrix Butler area which is reduced by the use of compact directional couplers. The proposed miniature matrix operates in the frequency band 76 MHz. The intersection of two microstrip lines is implemented as a crossover. Matrix modeling is implemented in the NI-AWR Design Environment program. The manufactured model showed a high similarity with the simulation results. The area of the compact matrix is 3.6 times smaller than the area of the standard design, but there was a narrowing of the frequency band by 2.4%.
A 28nm 1.5Mb Embedded 1T2R RRAM with 14.8 Mb/mm2 using Sneaking Current Suppression and Compensation Techniques

For the first time, 1T2R RRAM cell using PMOS selector is proposed and demonstrated with improved reset voltage and high density for embedded NVM. Hierarchical bit line and 3-state cell storage confine the associated sneaking current locally within a small zone and further suppress it by >90%. Self-adaptive write driver with current limiter and sneaking current compensator realizes power saving and accurate current compliance for set. To suit PMOS selector, reverse read using current sensing with dummy reference brings fast and reliable read. A 1.5Mb RRAM test chip in 28nm improved the record storage density by 40% to 14.8 Mb/mm2. Reliable read is performed at low VDD of 0.6V@(−40, 125)°C.
The Relation of Respiratory Metabolism of Insects to their Susceptibility to Fumigants

The susceptibility of an insect to a fumigant varies with the rate of respiratory metabolism. Certain factors increase the rate of metabolism; others decrease it and automatically increase the susceptibility of the insect. Of the known important are: An increase in temperature; an increase in the carbon dioxide content of the fumigation chamber; and a decrease in the oxygen content of the fumigation chamber.
Automatic Detection of Anatomical Landmarks on Geometric Mesh Data using Deep Semantic Segmentation

Anatomical landmark detection is the first step towards the analysis of 3D medical data. In this paper, we annotate the biologically significant landmarks on sphere-like meshes using deep semantic segmentation. A triplet candidate pool and the cutting path are firstly defined to parameterize 3D mesh model into 2D planar flat-torus. A deep convolutional network is utilized to learn geometric surface properties and then segment landmark areas. The landmarks are finally localized within their areas by incorporating the local neighborhood features. Extensive experiments are conducted on our newly-constructed scapula dataset, where we demonstrate the accuracy and efficacy of the proposed approach.