Miracle-dz/UniFAR-FTUs huggingface dataset:
Persistence and stability of macroinvertebrate communities in streams of Denali National Park, Alaska: implications for biological monitoring

1. Macroinvertebrate communities were studied from 1994 to 2001/2002 (except 1997) in six streams in Denali National Park, interior Alaska. All six streams were potential reference streams with no known impairment. 2. Abundance of individual taxa varied markedly from year to year. Overall, abundance decreased over the study period, particularly with respect to mayflies. Stonefly taxa showed lower persistence and were sometimes absent from a stream in any particular year. 3. Mean community persistence for the six streams, as measured by Jaccard's similarity coefficients between years, varied from 0.48 in the year pair 1999-2000 to 0.78 in 1998-99. Tattler Creek (a small stable stream) supported the most persistent macroinvertebrate community and Highway Pass Creek (a small, unstable creek) the least. Mean community persistence showed a significant relationship with mean winter snowfall (November to March) for the six streams. 4. The highest community compositional stability was found in Tattler Creek and the lowest in Highway Pass Creek, but stability varied markedly over time for the six streams, peaking in 1994-95 and reaching a minimum in 2000-01. Compositional stability was significantly related to the Pfankuch Index of channel stability. 5. The composition metrics % Chironomidae, % dominant taxa, % EPT, % Ephemeroptera and % Plecoptera, employed as part of the Alaska Stream Condition Index, varied over almost their entire range in these pristine streams across the 9 years of the study. 6. This study demonstrates the wide range of natural variation that occurs in benthic macroinvertebrate communities in these pristine central Alaskan streams, potentially limiting the applicability of composition metrics for the biological monitoring of water quality in these systems.
Using Self-Supervised Learning of Birdsong for Downstream Industrial Audio Classification

In manufacturing settings, workers rely on their sense of hearing and their knowledge of what sounds correct to help them identify machine quality problems based on the sound pitch, rhythm, timbre and other characteristics of the sound of the machine in operation. Using machine learning to classify these sounds has broad applications for automating the manual quality recognition work currently being done, including automating machine operator training, automating quality control detection, and diagnostics across manufacturing and mechanical service industries. We previously established that models taking input pitch information from music domains can dramatically improve classiﬁcation model performance on industrial machine audio leveraging a pretrained pitch model. In this work, we explore the use of self-supervised learning on pitch-intensive birdsong rather than a pre-trained model. To reduce our reliance on a pretrained pitch model and reduce the quantity of labeled industrial audio required, we implement self-supervised representation learning using plen-tiful, license-free, unlabeled, pitch-intensive wild birdsong recordings, with audio data augmentation to perform classiﬁcation on industrial audio. We show that: 1. We can preprocess the unlabeled birdsong data sample with unsupervised methods to eliminate low signal sample and mask low frequency noise leaving just desirable chirp-rich sample. 2. We can identify effective representations and approaches for learning birdsong pitch content by comparing select self-supervised pretext task training of temporal sequence prediction and
Machine Learning Based Identification for Android Malicious Applications

A BSTRACT Mobile malware is rapidly becoming a serious threat. There are many different types of mobile malwares in real world and they depend on the platforms or operating systems which are installed in mobile devices. The Fake Player Trojan is the first malware for android platform that was discovered in 2010. The number of android malicious applications has been consistently rising because android phones are widespread and steadily gaining popularity. Therefore, android malicious applications detection has become a popular research area. We propose a framework to identify different types of android malicious applications by using machine learning approaches in this paper. This framework intends to extract features from android applications and will identify malware application type with better accuracy results. In an evaluation with 1,000 applications, the proposed framework identifies malicious applications with accuracy rate over 90% and few false alarms.
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping

Object grasping is critical for many applications, which is also a challenging computer vision problem. However, for cluttered scene, current researches suffer from the problems of insufficient training data and the lacking of evaluation benchmarks. In this work, we contribute a large-scale grasp pose detection dataset with a unified evaluation system. Our dataset contains 97,280 RGB-D image with over one billion grasp poses. Meanwhile, our evaluation system directly reports whether a grasping is successful by analytic computation, which is able to evaluate any kind of grasp poses without exhaustively labeling ground-truth. In addition, we propose an end-to-end grasp pose prediction network given point cloud inputs, where we learn approaching direction and operation parameters in a decoupled manner. A novel grasp affinity field is also designed to improve the grasping robustness. We conduct extensive experiments to show that our dataset and evaluation system can align well with real-world experiments and our proposed network achieves the state-of-the-art performance. Our dataset, source code and models are publicly available at www.graspnet. net.
In situ Degradation of the Protein Chain of Potato Virus X at the N- and C-termini

Summary The amino acid composition, behaviour in SDS-polyacrylamide gel electrophoresis and electrophoretic patterns of cyanogen bromide peptides were studied for the protein subunits of different preparations of potato virus X (PVX). The results indicate that the protein subunits of PVX can be partially degraded in the intact virus at the N-terminus by reducing agent-dependent proteases in crude plant sap and by trypsin, and at the C-terminus by reducing agent-independent proteases occurring in some virus preparations.
Load-aware Spectrum Allocation based on Interference Graph Adapting to Radio Characteristics

: Nowadays, load-driven spectrum allocation in wireless networks is becoming more important since the surging mobile trafﬁc are usually distributed quite uneven. Related approaches have been proposed to assign channels of different widths for APs in WLAN. But a challenge usually being ignored is that radio characteristics will affect the interference graph which is a key basis in the allocation process. In this paper, we explain inﬂuences of radio features on interference graph and give a simple method to estimate the more accurate interference graph accordingly. In addition, a load-aware spectrum allocation mechanism for APs is proposed. The mechanism ﬁrstly decides widths of channels required by APs and estimates the more accurate interference graph based on several characteristics like channel-width, central frequency. Then it arranges spectrum blocks for APs based on the new interference graph. Simulations and analysis show that spectrum allocation method using wrong interference graph will generate great spectrum overlaps between interfering APs, about 1350MHz. And our mechanism could improve spectrum utilization efﬁciency by nearly two times while generating only few overlaps, about 250MHz.
Ovipositing behaviour of Compsobracon mirabilis (Szépligeti) (Hymenoptera, Braconidae) in a cerrado habitat, southeastern Brazil

The oviposition behaviour of the braconid parasitoid, Compsobracon mirabilis ( Szepligeti, 1901) is described. Observations were conducted in a cerrado region located in Tres Marias, Minas Gerais, Brazil. The oviposition occurred in a branch of Alibertia concolor (Cham.) K. Schum. 1889 (Rubiaceae), inside of which there were thirteen larvae of an unidentified species of Lepidoptera.
OFDM Waveform Optimization for Joint Communications and Sensing

The proliferation of mobile devices has resulted in the frequencies of operation of communication systems to coincide with those of the radar systems, causing mutual interference. To minimize the level of interference, a novel approach is to combine both systems in a single platform. This presentation considers a joint radar and communication system, where the radar transmitter and receiver as well as the communication transmitter are the same device while the communication receiver is at some distance. The same waveform is used for both systems and it is optimized such that it maximizes the performance of both systems
Climate Change Adaptation in Seychelles: Actors, Actions, Barriers and Strategies for Improvement

Small island developing states (SIDS) are particularly vulnerable to the impacts of climate change such as increased temperatures, sea level rise (SLR), and precipitation (Robinson, 2015). Chapter 29 of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) (also called ‘AR5’) on SIDS reiterated that island vulnerability is often ‘a function of four key stressors: physical, socio-economic, socioecological and climate-induced, whose reinforcing mechanisms are important in determining the magnitude of impacts’ (Nurse et al., 2014, p1635). These vulnerabilities according to two previous studies include: rapidly increasing populations; weak governance structures; remoteness from world markets; sensitivity to external and global shocks; small size; and fragile ecosystems that are susceptible to natural hazards (Kelman, 2014; Guillotreau et al., 2012). Consequently, high priority is given to climate change adaptation compared to mitigation in SIDS. Seychelles reinforces this position in its National Communications and also in its Intended Nationally Determined Contributions (INDCs) submitted to the United Nations Framework Convention on Climate Change (UNFCCC).
Growth of Botrytis cinerea and Strawberry Quality in Ozone-enriched Atmospheres

Botrytis cinerea cultures were stored on potato dextrose agar in air with or without 1.5 μL/L ozone at 2 °C. Cultures stored in the presence of ozone grew slower, Strawberry fruits (Fraganta X anariassa cv. Camarosa) were stored for 3 d at 2°C in air with or without 1.5 μL/L ozone and then transferred to room temperature. Each group was Inoculated with B. cinema grown in air with or without ozone. Visible mycelial growth developed more rapidly on fruit previously stored in air. Ozone enriched cold storage of naturally infected 'Camarosa' fruit reduced decay incidence, weight loss, and fruit softening, but resulted in a reversible loss of fruit aroma.
Molecular Mechanism of HSF1-Upregulated ALDH2 by PKC in Ameliorating Pressure Overload-Induced Heart Failure in Mice

Evidences abound that HSF1 and ALDH2 are of cardioprotective effect, yet there is still no report on whether HSF1 can regulate ALDH2 to delay the occurrence of heart failure. We first established the pressure overload-induced heart failure model of mice by transverse aortic constriction (TAC) and discovered that, in the forming period of heart failure, changes of HSF1 and ALDH2 expression recorded the consistent trend. When HSF1 was upregulated/downregulated to delay/promote the occurrence of heart failure, PKC and ALDH2 also showed increased/decreased expression. And when ALDH2 was upregulated/downregulated, the role of HSF1 in delaying the occurrence of heart failure strengthened/weakened. Next, we used mechanical stretch to establish a pressure-stimulated myocardial hypertrophy model and discovered an increased expression of both HSF1 and ALDH2. When HSF1 was upregulated/downregulated to increase/decrease the expression of myocardial hypertrophy gene beta-MHC, PKC and ALDH2 recorded an increased/decreased expression. When an inhibitor was used to downregulate the expression of PKC in cardiomyocytes, we found that the role of HSF1 in upregulating ALDH2 beta-MHC weakened. These findings suggest that HSF1 can upregulate the expression of ALDH2 via PKC to promote pressure-stimulated myocardial compensatory hypertrophy, which is an important molecular pathway for HSF1 to ameliorate heart failure.
PLANT ASSOCIATIONS OF CUMBRES DE MAJALCA NATIONAL PARK, CHIHUAHUA, MEXICO

Abstract Vegetation types in Cumbres de Majalca National Park were classified using cluster analysis from 195 line transects and 975 1-m2 quadrats at 39 sites. Six plant associations emerged from oak-grassland and oak-conifer communities. Each association is described using cover, constancy, elevation, physiographic, and soil parameters. A vegetation profile and a map of the plant associations are presented.
A Highly Reliable Platform with a Serpentine Antenna for IEEE 802.15.4 over a Wireless Sensor Network

: - Wireless sensor networks (WSNs) are a developing area in the modern health-care service industry. Traditional circuit layout rules generally cannot be used for the stringent requirements of such systems due to unstable signals and non-ideal conditions. We propose and have implemented a hardware platform to eliminate the poor layout rules. During the design phase, our proposed design procedure can help analyze the signal propagation conditions of the printed circuit board (PCB) design by simulating board-level signal integrity. Moreover, this procedure leads to better layout rule parameters. A designer can effectively build a reliable hardware platform for the design of microcontroller unit schematics with superior system performance using our proposed layout rules. Such layout technologies are the major goal of original design manufacturers in their overall PCB designs. Moreover, since the PCB serpentine antenna has the advantages of low cost, light weight, simple construction, and easy integration, we used formulas and simulation software tools to analyze its characteristics and then constructed a prototype with better performance for IEEE 802.15. 4 wireless antenna applications. Our experiments showed that the transmission distance can be greater than 18 m without any loss of data over a WSN when using this antenna.
A new ns-3 WLAN error rate model – Deﬁnition, validation of the ns-3 implementation and comparison to physical layer measurements with AWGN channel

In network simulations a correct representation of the physical layer is essential in order to achieve reliable results which are comparable to real hardware performance. The network simulator ns-3 speciﬁes two error rate models for the calculation of the bit error rates and corresponding packet error rates for orthogonal frequency division multiplexing, the YANS [3] and NIST [4] error rate models. In [4] both models are validated and the NIST model is recommended for the calculations because of the overly optimistic results in the YANS model. Still some inaccuracies are present in both of the models which are discussed in this work. A new model is presented in this work and changes to both models present are proposed, which lead to results comparable to real hardware performance. The upper bounds calculated in the models are compared with measurements over an AWGN channel with a typical IEEE 802.11 a / b / g / n wireless LAN module.
Dual mode control strategy for complex and flexible manufacturing systems

The manufacturing industry is shifting towards smart manufacturing, in which both energy e ﬃ ciency and ﬂexibility are some of the main objectives of this transformation. In this regard, the control strategies for manufacturing systems should be able to support the requirements of this transformation with a low computational burden towards their implementation in real time. To this end, in this paper, a dual-mode control strategy based on two control approaches is proposed to minimize the energy consumption of manufacturing systems without a ﬀ ecting their productivity, even when scenarios of ﬂexible manufacturing are considered. The ﬁrst control model is based on Model Predictive Control to determine the optimal and constrained behaviour of the system. Then, the second mode starts from the assumption that the system exhibits a periodic behaviour to switch to an autonomous control mode that not solve an optimisation problem online. The proposed control strategy is tested in a manufacturing process line in which changes in the production programs are considered with the aim to test the performance in ﬂexible manufacturing scenarios. The obtained results show that the computational burden could be signiﬁcantly reduced while reducing global energy consumption without a ﬀ ecting the system productivity.
Indoor Localization and Building Occupancy Count Estimation using LTE-A Ultra-Dense Networks

Recent studies have shown that awareness of occupants’ presence, location, and count can be used for optimizing building operations and management. We present innovative ideas on how to improve such building sustainability reducing CO 2 emissions and energy consumption, through occupants’ localization and tracking, and building occupancy count estimation. We propose to use Long Term Evolution-Advanced Ultra-Dense-Networks to locate users and to estimate the occupancy count. Furthermore, we discuss how awareness of occupants’ location and count will be integrated into other parts of our project, namely, Building Information Modeling (BIM), building simulation, design, ret-rofitting, and studying occupant’s behavior.
Modelling Clock Synchronization in the Chess gMAC WSN Protocol

We present a detailled timed automata model of the clock synchronization algorithm that is currently being used in a wireless sensor network (WSN) that has been developed by the Dutch company Chess. Using the Uppaal model checker, we establish that in certain cases a static, fully synchronized network may eventually become unsynchronized if the current algorithm is used, even in a setting with infinitesimal clock drifts.
Shuni Virus in Wildlife and Nonequine Domestic Animals, South Africa

We screened nonequine animals with unexplained neurologic signs or death in South Africa during 2010–2018 for Shuni virus (SHUV). SHUV was detected in 3.3% of wildlife, 1.1% of domestic, and 2.0% of avian species. Seropositivity was also demonstrated in wildlife. These results suggest a range of possible SHUV hosts in Africa.
Object Detection for the Audi Autonomous Driving Cup

One of the challenges for autonomous driving in general is to detect objects in the car’s camera images. In the Audi Autonomous Driving Cup (AADC) 1 , among those objects are other cars, adult and child pedestrians and emergency vehicle lighting. We show that with recent deep learning networks we are able to detect these objects reliably on the limited Hardware of the model cars. Also, the same deep network is used to detect road features like mid lines, stop lines and even complete crossings. Best results are achieved using Faster R-CNN with Inception v2 showing an overall accuracy of 0.84 at 7 Hz.
Supplementary Material for DoveNet: Deep Image Harmonization via Domain Veriﬁcation

In this Supplementary file, we will introduce the attention block used in our proposed DoveNet in Section 1, analyze our constructed iHarmony4 dataset w.r. t. foreground ratio, color transfer method, and semantic category in Section 2, 3, 4. Besides, We will show samples of manually filtered images and final images in our dataset in Section 5, 6. Finally, we will exhibit the results of different methods on all 99 real composite images in Section 7.
Modelling of Elastic Wave Scattering in Polycrystalline Materials

Predicting ultrasonic wave propagation in large 3D structures is demanding in terms of computer memory and CPU time. Accounting for polycrystalline nature of materials makes the tas k significantly challenging. While elastic wave propagation and scattering characteristics of polycrystalline metals have been studied, the role of polycrystalline microstructure in ultrasonic non-destructive evaluation is not full y comprehended. We have been studying FEM and ray trace based models to understand how polycrystalline microstructure influences ultrasonic waves. We now report on the development of an FDTD model that is capable of reducing computation time significantly. Elastic wave propagation in 3D polycrystalline media is modelled usi ng the velocity-stress formulation employing the FDTD method. A controlled Voronoi tessellation r epresents the polycrystalline microstructure. The variation of elastic constants due to varying (grain to g rain) crystallographic orientations is also taken into account. The numerical scheme employs parallel computation using General Purpose Computing on Graphical Processing Units (GPGPU). The FDTD formulation with parallel proce ssing allows one to deal with larger structures with additional complexities in terms of microstructural features or as external appendages to a large structure.
ZEBRA plus ultracapacitors: A good match for energy efficient EVs

ZEBRA batteries have demonstrated mature development, having reached almost 120 Wh/kg specific energy. On the other hand, specific power has a good figure, but needs to be improved for fast acceleration and good regenerative braking. With this purpose in mind, an ultra capacitor power system is being included in an EV that uses a high temperature Sodium-Nickel Chloride battery (ZEBRA). The ultra capacitor system has been connected to the ZEBRA battery and to the traction inverter through a Buck-Boost type DC-DC converter, which manages the energy flow with the help of DSP controller. Special control algorithms are being programmed for such a purpose. The paper shows the implementation of the experimental vehicle with the location of all components, and some simulation of the vehicle operation. Previous experimental results with lead-acid batteries demonstrated excellent behavior and now, the tests with the ZEBRA battery are under way.
Automatic fish ageing from otolith images using statistical learning.

In this paper, we investigate the use of statistical learning techniques for ﬁsh age estimation from otolith images. The core of this study lies in the deﬁnition of relevant image-related features. We rely on the characterization of a 1 D signal summing up the image content within a predeﬁned area of interest. Fish age estimation is then viewed as a multi-class classiﬁcation issue using neural networks and SVMs. A procedure based on demodulation and remodulation of ﬁsh growth patterns is used to improve the generalization properties of the trained classiﬁers. We also investigate the combination of additional biological and shape features to the image-related ones. The performances are evaluated for a database of several hundred of plaice otoliths.