Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical Analysis of Architectural Primitives for NVRAM Consistency

Non-volatile memory (NVM) provides persistent memory semantics with access latencies comparable to volatile DRAM. The persistent nature of NVM requires the application developers to design data consistency mechanisms for failure recovery, without which application may end up with inconsistent memory state after a power failure or a system crash. Most commonly employed methods use architectural support for cache line flushing and memory fencing to enforce ordering of writes to NVM. In this paper, we study the performance overhead of different hardware primitives used to achieve NVM consistency on Intel x86-64 and Arm64 systems using micro-benchmarks. Further, we also empirically analyze the impact of working set size and memory access characteristics (read-to-write ratio) of applications on different data consistency techniques. Logging based mechanisms (e.g., redo and undo logging), commonly used for NVM consistency, also use underlying architectural primitives like cache flushing. We comparatively study the overheads of redo and undo logging with different architectural primitives. The analysis presented in this paper can be useful to improve the software/hardware architecture, develop efficient applications and perform better capacity planning in NVM systems.
Building Highly-Available Geo-Distributed Datastores for Continuous Learning

Data generation is becoming increasingly decentralized. As a consequence, techniques for building and continuously updating machine learning models, typically conﬁned to a data center, need to evolve to encompass geo-distributed data sources if they are to remain relevant and cost-effective. Timely access to distributed data for the purposes of analytics and machine learning is extremely valuable but challenging to provide [25, 17, 29, 20]. Scaling up these data and compute intensive tasks mandates distribution [20]. However, key algorithms for learning, such as stochastic gradient descent, are not trivially parallelizable; distributed implementations often require carefully re-architecting the underlying computational model [11]. For these analyses, if the global view of data can be maintained at a single node, extracting parallelism can become signiﬁcantly simpler. Data decentralization is pushed further with edge computing. Applications that span edge devices such as smartphones, home appliances, and sensors, exchange signiﬁcant amounts of data with cloud-based services. Autonomous vehicles alone are expected to generate about a Gigabyte of data every second [28]; edge services can include real-time driving decisions, monitoring and guidance, and ﬂeet-wide situational awareness [30]. These applications require the edge to continuously consolidate data from a large number of clients and maintain uptodate copies in multiple sites, often geographically distant, for both availability and performance. The cost of data movement over wide-area networks is thus a signiﬁcant hurdle in learning and a driving motivation behind systems speciﬁcally optimized for geo-distributed machine learning [17, 5]. Techniques to accommodate geo-distributed learning largely follow two approaches: 1) a centralized approach wherein data from remote locations is migrated, continuously if needed, to a single data center which relies on conventional machine learning approaches. 2) a decentralized approach wherein the machine learning computation itself is spread across the remote sites; the different sites communicate with each other to exchange intermediate state and parameters. The centralized learning approach has the beneﬁt of leveraging existing algorithms without the need to create custom
Information display by dragged haptic bumps

The utility of haptic interfaces is widely established now. Although many interfaces has been designed, few tests about immediate usability have been made. The purpose of this paper is a study of direction and amplitude discrimination when a user feels a bump while moving the hand along an axis with an haptic pointing device. The results show that users can discriminate two and four directions with few errors while using one amplitude. But in the case of two or three amplitudes, some users have difficulties. The amplitude discrimination is more problematic. With only two amplitudes some users don’t manage to discriminate the bumps, and with three amplitudes most of them make a lot of errors.
A Toolbox for Construction and Analysis of Speech Datasets

Automatic Speech Recognition and Text-to-Speech systems are primarily trained in a supervised fashion and require high-quality, accurately labeled speech datasets. In this work, we examine common problems with speech data and introduce a toolbox for the construction and interactive error analysis of speech datasets. The construction tool is based on K\"urzinger et al. work, and, to the best of our knowledge, the dataset exploration tool is the world's first open-source tool of this kind. We demonstrate how to apply these tools to create a Russian speech dataset and analyze existing speech datasets (Multilingual LibriSpeech, Mozilla Common Voice). The tools are open sourced as a part of the NeMo framework.
Design and Fabrication of Compact Microstrip Low-pass Filter with Thin Film Technique for Wideband Applications

This paper introduces a compact microstrip low-pass filter (LPF) with a very sharp rejection. The proposed LPF is designed to be used for electronic warfare in defense industry. It has 62 dB/GHz attenuation rate, 10 GHz of stopband width and 48.6 dB of stopband attenuation. Moreover, it also has a wide passband and has a high cut-off frequency (fc) of 6 GHz. The designed filter has been fabricated using alumina (Al2O3) substrate and titanium-gold (Ti-Au) coating on top of that with thin film technique in a clean room. The experimental measurements of the proposed LPF show great agreement with the simulation results, which validates that the presented filter is convenient to be used for applications up to C-band in electronic warfare systems. Received: 17 Nov 2020 Accepted: 10 May 2021
Ultra-wideband radar facility and measured results at the Naval Ocean Systems Center

An ultrawideband (UWB) radar facility has been developed to experimentally and analytically explore the potential of technology based on such radar waveforms and to characterize the sea echo environment as sensed by an ultrawide fractional bandwidth radar waveform. Initial UWB results are presented for sea clutter, a small boat with and without a corner reflector, a helicopter-suspended calibration standard, the helicopter in question, a calibration tower standard, and ships of various sizes.
TI NN BRE : A TIMBRE - BASED MUSICAL AGENT

In this paper, we present tiNNbre, a generative music prototype-system that reacts to timbre gestures. By timbre gesture we mean a sonic (as opposed to body) gesture that mainly conveys artistic meaning through timbre rather than other sonic properties. The system is designed and developed to be used in free improvisation and composition. Our prototype is powered by a neural network trained using a supervised learning approach on a set of sonic gestures representing the stimulus (or input) and a correspondent set of sonic gestures representing the reactions (or output). This model is then used to explore and generate new musical material. Here, we present an informal evaluation of our system, based on two use cases. In the ﬁrst, the system is trained using MFCC analysis, while the second uses Constant-Q Transform spectrum. Participants were asked to rate our system’s generated audio, particularly in respect of timbre. Results showed that although tiNNbre has been tested ofﬂine, it fosters timbre-rich interaction with the sonic materials and the co-creation process. Broadly speaking, musicians preferred the results obtained using the second system other than for purely percussive gestures.
Multilayer MMIC using a 3 mu m*3-layer dielectric film structure

Very small multilayer MMICs (monolithic microwave integrated circuits) using miniature microstrip lines on a thin dielectric film are described. Their configuration, loss characteristic, and dielectric materials (silicon oxynitride and polyinide) are discussed. Other effective thin-film transmission lines, line crossovers, and vertical connections are also discussed. Multiport Wilkinson dividers, 90 degrees and 180 degrees hybrids, and distributed amplifiers are implemented in an area of less than 1 mm/sup 2/. <<ETX>>
RP-Mod&RP-Crowd:  Moderator- and Crowd-Annotated German News Comment Datasets

Abuse and hate are penetrating social media and many comment sections of news media companies. These platform providers invest considerable efforts to moderate user-generated contributions to prevent losing readers who get appalled by inappropriate texts. This is further enforced by legislative actions, which make non-clearance of these comments a punishable action. While (semi-)automated solutions using Natural Language Processing and advanced Machine Learning techniques are getting increasingly sophisticated, the domain of abusive language detection still struggles as large non-English and well-curated datasets are scarce or not publicly available. With this work, we publish and analyse the largest annotated German abusive language comment datasets to date. In contrast to existing datasets, we achieve a high labelling standard by conducting a thorough crowd-based annotation study that complements professional moderators’ decisions, which are also included in the dataset. We compare and cross-evaluate the performance of baseline algorithms and state-of-the-art transformer-based language models, which are ﬁne-tuned on our datasets and an existing alternative, showing the usefulness for the community.
A 3D Generative Model for Structure-Based Drug Design

We study a fundamental problem in structure-based drug design -- generating molecules that bind to specific protein binding sites. While we have witnessed the great success of deep generative models in drug design, the existing methods are mostly string-based or graph-based. They are limited by the lack of spatial information and thus unable to be applied to structure-based design tasks. Particularly, such models have no or little knowledge of how molecules interact with their target proteins exactly in 3D space. In this paper, we propose a 3D generative model that generates molecules given a designated 3D protein binding site. Specifically, given a binding site as the 3D context, our model estimates the probability density of atom's occurrences in 3D space -- positions that are more likely to have atoms will be assigned higher probability. To generate 3D molecules, we propose an auto-regressive sampling scheme -- atoms are sampled sequentially from the learned distribution until there is no room for new atoms. Combined with this sampling scheme, our model can generate valid and diverse molecules, which could be applicable to various structure-based molecular design tasks such as molecule sampling and linker design. Experimental results demonstrate that molecules sampled from our model exhibit high binding affinity to specific targets and good drug properties such as drug-likeness even if the model is not explicitly optimized for them.
Prevalence and risk factors associated with repeat positive SARS-CoV-2 nucleic acid test results among discharged COVID-19 patients A protocol for systematic review and meta-analysis

Background: The COVID-19 (coronavirus disease 2019) pandemic continues to have an immense impact on the world at large. COVID-19 patients who meet the discharge criteria, may subsequently exhibit positive viral RNA test results upon subsequent evaluation. This phenomenon has been a major source of research and public health interest, and poses a major challenge to COVID-19 prevention, treatment, and standardized patient management. Methods: We will search the PubMed, MEDLINE, Embase, Cochrane Clinical Trials Database, China National Knowledge Infrastructure, Wanfang Database, Chinese Science Journal Database, and China Biology Medicine databases for all studies published as of November 2021. Data will be extracted independently by two researchers according to the eligibility criteria. Finally, RevMan 5.3. 0 will be implemented for statistical analyses. Results:The results of this study will show the prevalence and risk factors associated with repeat positive SARS-CoV-2 nucleic acid test results among discharged COVID-19 patients. Conclusions: This study will provide a reliable evidence-based for the prevalence and risk factors associated with repeat positive SARS-CoV-2 nucleic acid test results among discharged COVID-19 patients. Trial registration number: CRD42021272447. Abbreviations: AHRQ = Agency for Healthcare Research and Quality, CIs = confidence intervals, COVID-19 = coronavirus disease 2019, NOS = Newcastle-Ottawa Scale, ORs = odds ratios, SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2.
Suggested location of the equivalent horizontal conductor when replacing catenary

Medium voltage overhead lines are usually routed over flat terrain. Spans are in the order of 100m long. Sometimes large spans are needed, in particular in steeped terrains. That implies large sags. In those cases the catenary of the conductors is important. For electrical calculations, in steady state or transient propagation, it is usual to replace the catenary by an "equivalent" horizontal line located at the "average height": H av = H −2/3 S where H is the maximum height and S the sag of the catenary. In this contribution we suggest that, for normal steady operation and also for the analysis of transients propagation along the line, the best location to place the equivalent horizontal line is near the minimum height of the catenary.
Appendix: Information-Aware Graph Contrastive Learning

We summarize the published approaches of graph contrastive learning in Table 1. Specifically, the tasks include graph-level and node-level tasks. The GNNs are used to encode the graph data and they are quite flexible. A typical augmented view is the graph itself, but it focuses on different structure-level of the graph. The two views adopted by mvgrl are the graph itself and the diffused graph, respectively. The four views used by GraphCL are discussed in the text. GCA applies edges removing and attribute masking to generate two views. In CSSLs, edge deletion/insertion and node deletion/insertion are used to generate different views. Similar to GCA, SelfTask also applies edges removing and attribute masking to generate views. The contrastive modes are discussed in the text, which include global-global, local-global, local-local, multi-scale mode, and hybrid modes. mvgrl studies the effect of various contrastive modes. SelfTask uses both local-local and global-global. The additional comparison of sampling strategies and objective functions between published graph contrastive learning approaches is shown in Table-2.
Synchronization of Group-labelled Multi-graphs

Synchronization refers to the problem of inferring the unknown values attached to vertices of a graph where edges are labelled with the ratio of the incident vertices, and labels belong to a group. This paper addresses the synchronization problem on multi-graphs, that are graphs with more than one edge connecting the same pair of nodes. The problem naturally arises when multiple measures are available to model the relationship between two vertices. This happens when different sensors measure the same quantity, or when the original graph is partitioned into sub-graphs that are solved independently. In this case, the relationships among sub-graphs give rise to multi-edges and the problem can be traced back to a multi-graph synchronization. The baseline solution reduces multi-graphs to simple ones by averaging their multi-edges, however this approach falls short because: i) averaging is well defined only for some groups and ii) the resulting estimator is less precise and accurate, as we prove empirically. Specifically, we present MULTISYNC, a synchronization algorithm for multi-graphs that is based on a principled constrained eigenvalue optimization. MULTISYNC is a general solution that can cope with any linear group and we show to be profitably usable both on synthetic and real problems.
Quantitative Investigation of Hand Grasp Functionality: Hand Joint Motion Correlation, Independence, and Grasping Behavior

Modeling and understanding human grasp functionality are fundamental in prosthetics, robotics, medicine, and rehabilitation, since they contribute to exploring motor control mechanism, evaluating grasp function, and designing and controlling prosthetic hands or exoskeletons. However, there are still limitations in providing a comprehensive and quantitative understanding of hand grasp functionality. After simultaneously considering three significant and essential influence factors in daily grasping contained relative position, object shape, and size, this paper presents the tolerance grasping to provide a more comprehensive understanding of human grasp functionality. The results of joint angle distribution and variance explained by PCs supported that tolerance grasping can represent hand grasp functionality more comprehensively. Four synergies are found and account for 93% ± 1.5% of the overall variance. The ANOVA confirmed that there was no significant individual difference in the first four postural synergies. The common patterns of grasping behavior were found and characterized by the mean value of postural synergy across 10 subjects. The independence analysis demonstrates that the tolerance grasping results highly correlate with unstructured natural grasping and more accurately correspond to cortical representation size of finger movement. The potential for exploring the neuromuscular control mechanism of human grasping is discussed. The analysis of hand grasp characteristics that contained joint angle distribution, correlation, independence, and postural synergies, presented here, should be more representative to provide a more comprehensive understanding of hand grasp functionality.
Production and Perception of a Tonal Neutralization Case in Taiwan Southern Min

This study explored the tonal neutralization of context tones 55 and 24 in Taiwan Southern Min, which are said to be realized both as sandhi tone 33 on surface when occurring in the context positions according to the Tone Circle. A production experiment with a reading task was conducted and the speech samples produced by speakers of two different age groups, old and young generations, were examined. The f0 contours of the two target context tones embedded in the minimal pair of sentences were compared using smoothing spline analysis of variance (SS ANOVA). The result showed an age-based acoustic variation, where the f0 contours of context tones 55 produced by the old speakers were significantly higher in pitch than those of context tones 24 throughout the entire contour, which was absent from the data of the young speakers, where the f0 contours of context tones 55 and 24 were indistinguishable from each other. The result of a preliminary perception experiment with an identification task was in line with the production data.
Supplementary Material for “ Generative Neurosymbolic Machines ” 1 Additional qualitative Results

In Figure 5 and 6 we show the generation results with different zs samples while the zg are fixed. In the arrow room dataset, we see the image variation is small for different zs samples though we can occasionally see some object color changes. And in the two MNIST datasets, we see some variation on the digit styles in different zs samples while the overall scene structure remains the same. Comparing the variation in Figure 5 and 6, we find the same level of certainty on the zs on both the posterior zg samples and the prior zg samples. This implies that the global representation zg is flexible enough to capture most of the information in the scene.
Partial power DC-DC converter to integrate PVs in DC grids

Partial power processing is a concept applied to power converters in order to reduce its losses, size and cost. This technique consists of designing a converter that only processes a fraction of the input power. This dissertation's objective is to analyze the performance of a partial power converter when operating in a DC microgrid. For that purpose, the partial power converter will be designed and sized to operate as both a step-up converter and a step-down converter to connect a PV system and a constant power load, respectively, to the DC microgrid. A Maximum Power Point Tracker (MPPT) algorithm is used to guarantee maximum power generation of the PV system. For the converters, non-linear controllers were designed based on Lyapunov Stability Theory and used the Backstepping technique and Sliding Mode Control methods. A DC microgrid was modelled and simulated and the results are analyzed to evaluate the performance of the DC microgrid components for different operation scenarios.
A Survey on Voice Conversion using Deep Learning

—Voice conversion covers several topics and there are many approaches available to partially solve the problem. This survey explains the required fundamentals, presents different methods to solve this task and contains more material about similar problems. It focuses on end-to-end deep learning methods. The objective of this work is to give an overview over the state-of-the-art of voice conversion methods that are based on deep learning.
Methane emissions from paddy cultivation and livestock farming in Sarawak, Malaysia

In this study, implementation of Tier 1 methodology of 2006 IPCC (Intergovernmental Panel on Climate Change) Guidelines in paddy cultivation and livestock farming has been applied to estimate methane emissions in Sarawak, Malaysia within the years from 1998 to 2009. Methane emission inventory has been developed in this study, based on volume 4, 2006 IPCC Guidelines. Based on cultivation area and livestock population data as input to Tier 1 methodology, variations in paddy cultivation area and amount of livestock has been identified as the main contributor to emissions of methane. Methane emissions increased from 1.61 to 1.72 Gg CH4/year during 1998 to 1999. Based on results obtained, the outcomes show that there would be a significant drop of methane emission from buffalo and sheep. Although there are gain and loss in emissions from enteric fermentation, drastic reduction is observed from 0.65 Gg CH4/year in 1998 to 0.44 Gg CH4/year in 2009 as well as 0.05 Gg CH4/year to 0.02 Gg CH4/year for buffalo and sheep respectively. Simultaneously, methane emissions from manure management of buffalo has decreased from 0.024 Gg CH4/year in 1998 to 0.016 Gg CH4/year in 2009 while for sheep, its emission from manure management dropped from 0.002 Gg CH4/year in 1998 to 0.0007 Gg CH4/year in 2009. Overall emission from paddy cultivation can be considered in upward trend due to gain from 1998 at 1.61 Gg CH4/year to 1.67 Gg CH4/year in 2009. As an addition, significant rise in methane emission by 0.24 Gg CH4/year from 2000 to 2006 as well as 0.1 Gg CH4/year from 2007 to 2009 show momentum gaining in enteric fermentation of cattle. It also indicates future increment in methane emission from cattle which coherently affects the state’s emission level. As for emissions from manure management, emissions from cattle, goat and deer are gaining momentum in Sarawak. ∗ Corresponding author: pengek@gmail.com DOI: 10.1051/ , 05007 (2017) 71030 MATEC Web of Conferences matecconf/201 103