Miracle-dz/UniFAR-FTUs huggingface dataset:
Massive warming-induced carbon loss from subalpine grassland soils in an altitudinal transplantation experiment

Abstract. Climate change is associated with a change in soil organic carbon (SOC) stocks, implying a feedback mechanism on global warming. Grassland soils represent 28 % of the global soil C sink and are therefore important for the atmospheric greenhouse gas concentration. In a field experiment in the Swiss Alps we recorded changes in the ecosystem organic carbon stock under climate change conditions, while quantifying the ecosystem C fluxes at the same time (ecosystem respiration, gross primary productivity, C export in plant material and leachate water). We exposed 216 grassland monoliths to six different climate scenarios (CSs) in an altitudinal transplantation experiment. In addition, we applied an irrigation treatment (+12 % to 21 % annual precipitation) and an N deposition treatment (+3 and +15 kg N ha−1 yr−1) in a factorial design, simulating summer-drought mitigation and atmospheric N pollution. In 5 years the ecosystem C stock, consisting of plant C and SOC, dropped dramatically by about −14 % (-1034±610 g C m−2) with the CS treatment representing a +3.0 ∘C seasonal (April–October) warming. N deposition and the irrigation treatment caused no significant effects. Measurements of C fluxes revealed that ecosystem respiration increased by 10 % at the +1.5 ∘C warmer CS site and by 38 % at the +3 ∘C warmer CS site (P≤0.001 each), compared to the CS reference site with no warming. However, gross primary productivity was unaffected by warming, as were the amounts of exported C in harvested plant material and leachate water (dissolved organic C). As a result, the 5-year C flux balance resulted in a climate scenario effect of -936±138 g C m−2 at the +3.0 ∘C CS, similar to the C stock climate scenario effect. It is likely that this dramatic C loss of the grassland is a transient effect before a new, climate-adjusted steady state is reached.
Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning

Abstract. This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets, while also leveraging recent advances in machine-learning algorithms. The framework integrates high-resolution building inventory data with earthquake ground shaking intensity maps and surface-level changes detected by comparing pre- and post-event InSAR (interferometric synthetic aperture radar) images. We demonstrate the use of ensemble models in a machine-learning approach to classify the damage state of buildings in the area affected by an earthquake. Both multi-class and binary damage classification are attempted for four recent earthquakes, and we compare the predicted damage labels with ground truth damage grade labels reported in field surveys. For three out of the four earthquakes studied, the model is able to identify over 50 % or nearly half of the damaged buildings successfully when using binary classification. Multi-class damage grade classification using InSAR data has rarely been attempted previously, and the case studies presented in this report represent one of the first such attempts using InSAR data.
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG

Large pretrained models enable transfer learning to low-resource domains for language generation tasks. However, previous end-to-end approaches do not account for the fact that some generation sub-tasks, specifically aggregation and lexicalisation, can benefit from transfer learning in different extents. To exploit these varying potentials for transfer learning, we propose a new hierarchical approach for few-shot and zero-shot generation. Our approach consists of a three-moduled jointly trained architecture: the first module independently lexicalises the distinct units of information in the input as sentence sub-units (e.g. phrases), the second module recurrently aggregates these sub-units to generate a unified intermediate output, while the third module subsequently post-edits it to generate a coherent and fluent final text. We perform extensive empirical analysis and ablation studies on few-shot and zero-shot settings across 4 datasets. Automatic and human evaluation shows that the proposed hierarchical approach is consistently capable of achieving state-of-the-art results when compared to previous work.
Identidad en la era digital: construcción de perfiles en redes sociales en adolescentes chilenos/as

Este artículo tiene como objetivo caracterizar las lógicas que moldean la construcción de perfiles e identidades digitales utilizadas por los y las adolescentes como parte de sus interacciones en redes sociales en Chile. Utilizando una aproximación cuantitativa que incluyó la adaptación y validación de instrumentos estandarizados, se indagó por las prácticas y estrategias utilizadas en la construcción de dichos perfiles en una muestra estudiantes secundarios chilenos/as (N=892). Los resultados muestran que los y las adolescentes reportan un grado significativo de autenticidad en la presentación del “yo” en las redes, con importantes diferencias según sexo, que muestran como los códigos y normas offline son reapropiadas en el espacio digital
Network Packet Management Optimisation for Business Forensic Readiness

The acceptability of evidence in court is dependent upon stringent criteria for its admissibility. One of the leading guidelines is the Daubert criteria which asserts five requirements for admissibility. These criteria assert scientific principles that must be complied. The focus on the scientific testing of Digital Forensics tools is often glossed over by accepting commonly used proprietary tools without delving into their performance. In particular, Daubert states that the known error rates of a scientific procedure must be published, and that the scientific procedures must be independently tested. In this thesis, I am concerned about the satisfaction of Daubert’s key points when collecting digital forensic information that is evidentially sound:  The scientific procedure must be independently tested.  The scientific procedure should be published and subjected to peer review.  Are there standards and protocols for the execution of the methodology of the scientific procedure?  Is the scientific procedure generally accepted by the relevant scientific communities  Is there a known error rate or potential to know the error rate associated with the use of the scientific procedure (Daubert v. Merrell Dow Pharmaceuticals, 1993) Without known error rates and falsifiability criterion, digital evidence should not be in courtrooms. In this thesis the relevant literature and conceptual scope of the problem is explored theoretically, and then network tools tested empirically. The thesis is an initial investigation into whether there are error rates above zero when a network tool is used for evidence collection. The contribution is to both the digital forensic investigation community as well as to the law practitioners and judicial profession by demonstrating a potential problem with network forensic data. The performance of two tools was tested by subjecting them to increasing packet loads and measuring their performance. The results were then measured against the baseline of expected outputs and the differences noted. These differences were then used to generate an error rate in the form of a percentage. As part of the methodology the assertion was that, the tools would perform without error. Therefore, the research question is: Research Question: Can the Network Management System and the Network Packet Capture tool, achieve zero errors for digital forensic purposes?
An Effective Parameter-free Comparison of NGS Short Reads for Phylogeny Reconstruction

: With the growth of the amount of genomic data generated from high-throughput sequencing, next-generation sequencing (NGS) has become the mainstream format for genome sequence data. NGS presents new challenges for many applications for genome sequence analysis. In sequence comparison applications, traditional multiple-sequence alignment approaches do not provide a solution for analyzing NGS data because of the short-read assembly and computational resource problems. Thus, alignment-free methods are more suitable for NGS data comparisons. Most of the alignment-free methods are based on the k-mer algorithm. However, the characteristics of NGS data make such k-mer-based methods suboptimal because the k parameter is a crucial factor in distance measurement and for the construction of phylogenetic trees. We propose an e ﬀ ective parameter-free comparison of NGS short reads, with the aim of eliminating the dependency on the k parameters. We compared the proposed method with existing methods, and the results show that the proposed method can measure accurate distances for the dataset without requiring any parameter.
Cardiac Magnetic Resonance Phase Detection Using Neural Networks

The precision of cardiac magnetic resonance segmentation is an important area to investigate clinically and has received a lot of attention from the research community for its impact on the evaluation of cardiac functions. However, the correct identification of key time frames of cardiac sequences has received significantly less attention, especially in the MR domain, despite its great importance in the correct measurement of the Ejection Fraction, a key metric in diagnostics. In this paper, we present two deep learning regression methods to automate the otherwise time-consuming annotation process, with performance within the 1–2 frame distance error and almost instant calculation over short-axis images from a public dataset. Results are presented using publicly available data.
Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN

Biologically inspired mechanisms such as foveation and multiple ﬁxation points have previously been shown to help alleviate adversarial examples (Reddy et al., 2020). By mimicking the effects of visual crowding present in human vision, foveated, texture-based computations may provide another route for increasing adversarial robustness. Previous statistical models of texture rendering (Portilla & Simoncelli, 2000; Gatys et al., 2015) paved the way for the development of a Foveated Texture Transform (FTT) module which utilizes localized texture synthesis in foveated receptive ﬁelds (Deza et al., 2019). The FTT module was added to a VGG-11 CNN architecture and ten randomly initialized networks were trained on 20 class subsets of the Places and EcoSet datasets for scene and object classiﬁcation respectively. The trained networks were attacked using Projected Gradient Descent (PGD) and the adversarial accuracies were calculated at multiple epochs to evaluate changes in robustness as the networks trained. The results indicate that the FTT module signiﬁcantly improved adversarial robustness for scene classiﬁcation, especially when the validation loss was at a minimum. However, the FTT module did not provide a statistically signiﬁcant increase in adversarial robustness for object classiﬁcation. Furthermore, we do not ﬁnd a trade-off between accuracy and robustness (Tsipras et al., 2018) for the FTT module suggesting a beneﬁt of using foveated, texture-based distortions in the latent space during learning compared to non-perturbed latent space representations. Finally, we investigate the nature of latent space distortions with additional controls that probe other directions in the latent space that are not texture-based. A link to our code is available at https://github.com/JonGant/FoveatedTextureTransform
25.9(cid:3027)nW, 91.86(cid:3027)dB dynamic range second-order lowpass filter tunable in the range 4–100(cid:3027)Hz

: A second-order lowpass Butterworth filter with tunable bandwidth capable of offering a dynamic range of 91.86 dB operating on a supply voltage of 1.8 V is presented. The proposed filter is based on a sub-threshold source follower. The transistor bias currents are switched to enable the bandwidth tuning in the range 4–100 Hz. A proportional to absolute temperature (PTAT) current reference circuit helps to keep the bandwidth intact across process, voltage and temperature variations. The filter, designed in 0.18 µm standard CMOS process, consumes 25.9 nW making it a potential candidate for portable biomedical applications.
Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations

Abstract. The atmospheric boundary layer (ABL) defines the volume of air adjacent to the Earth's surface for the dilution of heat, moisture, and trace substances. Quantitative knowledge on the temporal and spatial variations in the heights of the ABL and its sub-layers is still scarce, despite their importance for a series of applications (including, for example, air quality, numerical weather prediction, greenhouse gas assessment, and renewable energy production). Thanks to recent advances in ground-based remote-sensing measurement technology and algorithm development, continuous profiling of the entire ABL vertical extent at high temporal and vertical resolution is increasingly possible. Dense measurement networks of autonomous ground-based remote-sensing instruments, such as microwave radiometers, radar wind profilers, Doppler wind lidars or automatic lidars and ceilometers are hence emerging across Europe and other parts of the world. This review summarises the capabilities and limitations of various instrument types for ABL monitoring and provides an overview on the vast number of retrieval methods developed for the detection of ABL sub-layer heights from different atmospheric quantities (temperature, humidity, wind, turbulence, aerosol). It is outlined how the diurnal evolution of the ABL can be monitored effectively with a combination of methods, pointing out where instrumental or methodological synergy are considered particularly promising. The review highlights the fact that harmonised data acquisition across carefully designed sensor networks as well as tailored data processing are key to obtaining high-quality products that are again essential to capture the spatial and temporal complexity of the lowest part of the atmosphere in which we live and breathe.
FARMING FOR MINING: COMBINING DATA FARMING AND DATA MINING TO GAIN KNOWLEDGE IN SUPPLY CHAINS

Knowledge discovery in databases (KDD) is a frequently used method in the context of supply chains (SC). The core phase of KDD is known under the term data mining. To gain knowledge, e.g., to support decisions in supply chain management (SCM), input data for KDD are necessary. Typically, such data consist of observational data, which have to be preprocessed before the data mining phase. Besides relying on observational data, simulation can be used to generate data as an input for the process of knowledge discovery. The process of using a simulation model as a data generator is called data farming. To link both data farming and KDD, a Farming-for-Mining-Framework has been developed, where the data farming process generates data as an input for the KDD process to support decisions in SCM.
Adaptive recursive clustering and target tracking Frijters

Autonomous vehicles are nowadays a dominant topic in research and development in the automotive industry. To achieve automation, the vehicle has to be aware of its surroundings. Therefore, automated vehicles rely to a large extent on their sensors, such as radar sensors for environmental perception. These radar sensors are used to detect and track other road users, which are important to accomplish autonomous driving. The goal of this graduation project is to develop a multi-target tracking (MTT) system using a specific type of radar sensor and an autonomous driving development platform, the so-called BlueBox, both provided by NXP. These radar sensors communicate with the NXP BlueBox via the Robot Operating System (ROS). Detection, prediction and data association are required to track multiple targets over time. Since the radar sensors measure multiple detections for a single object, radar clustering is applied to reduce the number of measurements. In this research, an adaptive recursive clustering (ARC) algorithm is presented as an extension of the DBSCAN method to mitigate the downsides of the regular implementation. The ARC method uses a dynamic cluster radius, which makes initial parameter selection less critical and gives better results when targets are close to each other. An extended Kalman filter with a constant acceleration model is used to track detected targets based on the clustered radar measurement. To correlate radar measurements to estimated tracks, the global nearest neighbour (GNN) algorithm is used as data association method. The MTT system includes the presented ARC method and is tested is subjected to multiple tests. The target tracking system with the regular DBSCAN is tested first using a simulation, where a single target performs a longitudinal movement. Then, the ARC is tested using a synthetic example to show the dynamic behaviour of the cluster radius over time. Subsequently, the complete target tracking system, including the introduced ARC method, is tested in both simulation and an experimental set-up. The results of these tests show that the target tracking system is able to track a single object in a longitudinal movement. A multi-target simulation is performed using two motorcycles. This simulation shows the benefit of the ARC method, which can distinguish closely spaced targets better than the regular DBSCAN. Adaptive recursive clustering and target tracking v
Utilisation of cervical cancer screening among women living with HIV at Kenya’s national referral hospital

Background In 2009, Kenyatta National Hospital (KNH) integrated cervical cancer screening within HIV care using visual inspection with acetic acid (VIA) and Pap smear cytology. Objectives We evaluated utilisation of cervical cancer screening and human papillomavirus (HPV) vaccination among women living with HIV (WLHIV) receiving HIV care at KNH. Method From November 2019 to February 2020, WLHIV aged ≥ 14 years were invited to participate in a survey following receipt of routine HIV services. We assessed awareness of cervical cancer, uptake of cervical cancer screening, uptake of the HPV vaccine, and barriers to utilisation of these services. In a subset of survey participants, focus group discussions (FGDs) were also conducted to identify screening barriers. Results Overall, 305 WLHIV participated in the survey. Median age was 36 years (interquartile range [IQR]: 28–43), 41% were married, and 38% completed secondary education. Most (90%) had HIV RNA < 1000 copies/mL. Awareness of cervical cancer was high (84%), although only 45% of WLHIV had screened for cervical cancer at the referral hospital and only 13% knew how to prevent high-risk HPV. No participants had received an HPV vaccination. Older age, higher education, and knowledge of the HPV vaccine were associated with higher likelihood of cervical cancer screening (P < 0.05). In FGDs, barriers to utilising the services included user fees, fear of the procedure impacting fertility, age and gender of the provider, and long waiting times. Conclusion Despite integration with HIV services, the utilisation of cervical cancer screening was low among WLHIV and implementation barriers contributed to low utilisation.
Morphuzz: Bending (Input) Space to Fuzz Virtual Devices

The security of the entire cloud ecosystem crucially de-pends on the isolation guarantees that hypervisors provide between guest VMs and the host system. To allow VMs to communicate with their environment, hypervisors provide a slew of virtual-devices including network interface cards and performance-optimized VIRTIO-based SCSI adapters. As these devices sit directly on the hypervisor’s isolation boundary and accept potentially attacker controlled input (e.g., from a malicious cloud tenant), bugs and vulnerabilities in the devices’ implementations have the potential to render the hypervisor’s isolation guarantees moot. Prior works applied fuzzing to simple virtual-devices, focusing on a narrow subset of the vast input-space and the state-of-the-art virtual-device fuzzer, Nyx, requires precise, manually-written, speciﬁcations to exercise complex devices. In this paper we present M ORPHUZZ , a generic approach that leverages insights about hypervisor design combined with coverage-guided fuzzing to ﬁnd bugs in virtual device implementations. Crucially M ORPHUZZ does not rely on expert knowledge speciﬁc to each device. M ORPHUZZ is the ﬁrst approach that automatically elicits the complex I/O behaviors of the real-world virtual devices found in modern clouds. To demonstrate this capability, we implemented M ORPHUZZ in QEMU and bhyve and fuzzed 33 different virtual devices (a superset of the 16 devices analyzed by prior work). Additionally, we show that M ORPHUZZ is not tied to a speciﬁc CPU architecture, by fuzzing 3 additional ARM devices. M OR PHUZZ matches or exceeds coverage obtained by Nyx, for 13/16 virtual devices, and identiﬁed a superset
An integrated architecture for global interconnects in a gigascale system-on-a-chip (GSoC)

An integrated architecture for global interconnects in a gigascale system-on-a-chip (GSoC) is presented using the models for global signal, clock, and power supply wiring networks. Based on the models for wiring resource demand, noise limit, and bandwidth requirement, an interconnect design plane is proposed. The new design plane demonstrates the limits imposed on global on-chip interconnect physical dimensions for future technology generations.
Reconf-Pack: A Simulator for Reconfigurable Battery-Powered Real-Time Systems

This paper addresses the management of independent periodic OS tasks running on battery-powered real-time systems which can change their parameters at run-time for each reconfiguration scenario. A reconfiguration is assumed to be any run-time automatic addition, re- moval, or also update of software tasks according to external events or also user requirements. After any sce- nario, the system can become not feasible and can also lead to a shortage of energy before the next recharge. To resolve these problems, we propose a dynamic strat- egy to be applied at run-time based on grouping tasks in packs. This strategy o↵ers five di↵erent solutions to ensure the system remains up until the next battery recharge while remaining feasible. In this context, we present, in this paper, a new simulator Reconf-Pack for analyzing a reconfiguration and applying the proposed strategy for real-time systems. It is based upon another tool Task-Generator which generates random tasks. Ac- cording to the state of the system after a reconfiguration, Reconf-Pack calculates dynamically a deterministic so- lution. Moreover, it compares our pack-based solutions to related works.
Sample Space Truncation on Boltzmann Machines

We present a lightweight variant of Boltzmann machines via sample space truncation, called a truncated Boltzmann machine (TBM), which has not been investigated before while can be naturally introduced from the log-linear model view-point. TBMs can alleviate the massive computational cost of exact training of Boltzmann machines that requires exponential time evaluation of expected values and the partition function of the model distribution. To analyze the learnability of TBMs, we theoretically provide bias-variance decomposition of the log-linear model using dually ﬂat structure of statistical manifolds