Miracle-dz/UniFAR-FTUs huggingface dataset:
I am pleased to submit to you the following manuscript: “The secrets of Oriental panacea: Panax ginseng”

The Panax ginseng root proteome has been investigated via capture with combinatorial peptide ligand libraries (CPLL) at three different pH values. Proteomic characterization by SDS-PAGE and nLC-MS/MS analysis, via LTQ-Orbitrap XL, led to the identification of a total of 207 expressed proteins. This quite large number of identifications was achieved by consulting two different plant databases: Panax ginseng and Arabidopsis thaliana. The major groups of identified proteins were associated to structural species (19.2%), oxidoreductase (19,5%), dehydrogenases (7.6%) and synthases (9.0%). For the first time, an exploration of protein-protein interactions was performed by merging all recognized proteins and building an interactomic map, characterized by 196 nodes and 1554 interactions. Finally a peptidomic analysis was developed combining different in-silico enzymatic digestions to simulate the human gastrointestinal process: from 661 generated peptides, 95 were identified as possible bioactives and in particular 6 of them were characterized by antimicrobial activity. The present report offers new insight for future invesigations focused on elucidation of biological properties of Panax ginseng proteome and peptidome. Milano, July 31-2015 To Prof. Jesùs Jorrìn-Novo, Executive editor, Journal of Proteomics Dear Editor, I am pleased to submit to you the following manuscript: “The secrets of Oriental panacea: Panax ginseng” by M. Colzani, A. Altomare, M. Caliendo, G. Aldini, P. G. Righetti and myself for consideration for publication in the Journal of Proteomics. The manuscript describes the identification of Panax ginseng proteome captured by combinatorial peptide ligand libraries from dried root extracts, focusing attention on biological functions of proteins. Additionally an interactomic map was constructed by exploiting the protein-protein interactions and finally a peptidomic analysis was performed by in-silico human gastrointestinal digestion in order to evaluate the peptides bioactivity. In particular, from a list of 95 bioactive peptides, we have focused our attention on 6 peptides characterized by antimicrobial activity. Our study represents the first extensive proteomic and peptidomic investigation of Panax ginseng root and we hope that it might be of interest to the readers of Journal of Proteomics. Since Journal of Proteomics focuses on proteomics and peptidomics, we have considered this journal as the most appropriate to submit our manuscript. We will be happy to answer any questions the referees might have concerning this manuscript. Looking forward to hearing from you soon, Sincerely yours, Elisa Fasoli Ph.D Politecnico di Milano, Dep. of Chemistry, Materials and Chemical Engineering Via Mancinelli, 7 20133 Milan, Italy e-mail: elisa.fasoli@polimi.it Office Phone: +39-02-23994749 Cover Letter
PointNest: Learning Deep Multiscale Nested Feature Propagation for Semantic Segmentation of 3-D Point Clouds

3-D point cloud semantic segmentation is a fundamental task for scene understanding, but this task remains challenging due to the diverse scene classes, data defects, and occlusions. Most existing deep learning-based methods focus on new designs of feature extraction operators but neglect the importance of exploiting multiscale point information in the network, which is crucial for identifying objects under complex scenes. To tackle this limitation, we propose an innovative network called PointNest that efficiently learns multiscale point feature propagation for accurate point segmentation. PointNest employs a deep nested U-shape encoder–decoder architecture, where the encoder learns multiscale point features through nested feature aggregation units at different network depths and propagates local geometric contextual information with skip connections along horizontal and vertical directions. The decoder then receives multiscale nested features from the encoder to progressively recover geometric details of the abstracted decoding point features for pointwise semantic prediction. In addition, we introduce a deep supervision strategy to further promote multiscale information propagation in the network for efficient training and performance improvement. Experiments on three public benchmarks demonstrate that PointNest outperforms existing mainstream methods with the mean intersection over union scores of 68.8%, 74.7%, and 62.7% in S3DIS, Toronto-3D, and WHU-MLS datasets, respectively.
SCAResNet: A ResNet Variant Optimized for Tiny Object Detection in Transmission and Distribution Towers

Traditional deep-learning-based object detection networks often resize images during the data preprocessing stage to achieve a uniform size and scale in the feature map. Resizing is done to facilitate model propagation and fully connected classification. However, resizing inevitably leads to object deformation and loss of valuable information in the images. This drawback becomes particularly pronounced for tiny objects such as distribution towers (DTs) with linear shapes and few pixels. To address this issue, we propose abandoning the resizing operation. Instead, we introduce positional-encoding multihead criss-cross attention (CCA). This allows the model to capture contextual information and learn from multiple representation subspaces, effectively enriching the semantics of DTs. In addition, we enhance spatial pyramid pooling by reshaping three pooled feature maps into a new unified one while also reducing the computational burden. This approach allows images of different sizes and scales to generate feature maps with uniform dimensions and can be used in feature map propagation. Our SCAResNet incorporates these aforementioned improvements into the backbone network ResNet. We evaluated our SCAResNet using the electric transmission and distribution infrastructure imagery (ETDII) dataset from Duke University. Without any additional tricks, we used various object detection models with Gaussian receptive-field-based label assignment (RFLA) as the baseline. When incorporating the SCAResNet into the baseline model, we achieved a 2.1% improvement in mAPs. This demonstrates the advantages of our SCAResNet in detecting transmission and DTs and its value in tiny object detection (TOD). The source code is available at https://github.com/LisavilaLee/SCAResNet_mmdet.
360 versus 3D Environments in VR Headsets for an Exploration Task

For entertainment, pedagogical or cultural purposes, there is a need for fast and easy setup of virtual environments that represent real ones. The use of 360 ◦ video in Virtual Reality Headsets seems like a powerful tool for producing fun and engaging content in a fast manner. This applies even more when we need to set up realistic views of actual environments. However, in terms of user experience in virtual reality headsets, can 360 ◦ shots of a real environment be an interesting alternative to a full 3D model? In this work, we have conducted a user study during a ﬁlm festival comparing the reaction of a wide public to two versions of a Virtual Reality cultural heritage visit of a Paleolithic cave, the "Grotte de Commarque" located in the south of France. The ﬁrst version is a full 3D textured model of the cave, the second is a series of 360 ◦ pictures, presented in a VR Headset. We have set up a scenario of observation and exploration. The users were able to navigate with the same teleportation metaphor in both conditions. We focused on evaluating the sense of presence during the visit. We have also sought for trends in perceived fun, sickness and easiness of navigation. Our results suggest that the full 3D environment is where the participants feel more present. However, the difference in rating the measures between the two conditions were not strongly marked. Moreover, a relevant result that we retain is that this rating is correlated to the degree of familiarity of the user with virtual reality.
Increasing digital investigator availability through efficient workflow management and automation

The growth of digital storage capacities and diversity devices has had a significant time impact on digital forensic laboratories in law enforcement. Backlogs have become commonplace and increasingly more time is spent in the acquisition and preparation steps of an investigation as opposed to detailed evidence analysis and reporting. There is generally little room for increasing digital investigation capacity in law enforcement digital forensic units and the allocated budgets for these units are often decreasing. In the context of developing an efficient investigation process, one of the key challenges amounts to how to achieve more with less. This paper proposes a workflow management automation framework for handling common digital forensic tools. The objective is to streamline the digital investigation workflow - enabling more efficient use of limited hardware and software. The proposed automation framework reduces the time digital forensic experts waste conducting time-consuming, though necessary, tasks. The evidence processing time is decreased through server-side automation resulting in 24/7 evidence preparation. The proposed framework increases efficiency of use of forensic software and hardware, reduces the infrastructure costs and license fees, and simplifies the preparation steps for the digital investigator. The proposed approach is evaluated in a real-world scenario to evaluate its robustness and highlight its benefits.
Selecting and Characterizing Tree-Ring Chronologies for Dendroclimatic Analysis

A widely spaced grid of tree -ring chronologies most suitable for dendroclimatic analysis of western North America is selected objectively on the basis of 1) numbers in the sample, length in years, and site locations, 2) statistical characteristics of the chronologies, and 3) correlation of chronologies with those on neighboring sites. The chronology statistics are then analyzed to characterize the quality of the selected set. The procedures used in this study are recommended for future climatic analysis to assure objectivity in the selection of quality tree -ring data and to allow comparisons of the statistics for new chronologies to the established data sets.
Java/A - Taking Components into Java

Component-based software-engineering and software architecture gain importance in software engineering. It is a well known fact that high level architectures and modular composition help constructing large-scale software systems. Today, programming languages support software architecture insufficiently. This leads to a number of problems, e.g. reduced reusability and architectural erosion. In this paper, the Java/A programming language is introduced. Java/A integrates notions like components and connectors into Java. Furthermore, Java/A provides mechanisms to verify component configurations.
Insights into planetesimal evolution: Petrological investigations of regolithic howardites and carbonaceous chondrite impact melts

Regolithic howardites are analogs for the surface materials of asteroid 4 Vesta, recently mapped by the Dawn spacecraft. Rigorously evaluating pairing of howardites recovered in 1995 in the Grosvenor Mountains (GRO 95), Antarctica, enables an examination of a larger, more representative regolith sample. Previous work on two of the howardites studied here concluded that GRO 95602 and GRO 95535 are solar windrich surface regolith samples and that they are not paired with each other, leading to uncertainty regarding pairing relationships between the other GRO 95 howardites. Based on petrology, cosmic-ray exposure history, and terrestrial age, four GRO 95 howardites are paired. The paired howardites (GRO 95534, 95535, 95574, 95581) were from a meteoroid with radius of 10-15 cm, a pre-atmospheric size comparable to that of Kapoeta, the largest known regolithic howardite. The paired GRO 95 howardites contain clasts of at least 18 separate HED lithologies, providing evidence they were assembled from diverse source materials. The total eucrite:diogenite mixing ratio (ratio of all eucrite lithologies to all diogenite lithologies) in the paired GRO 95 howardites is ~2:1. Petrographically determined basaltic eucrite:cumulate eucrite ratios in regolithic howardites, studied here and previously, vary more widely than total eucrite:diogenite ratios. Relative to eucritic pyroxene, plagioclase is depleted in these howardites, which provides evidence that plagioclase is preferentially comminuted in the vestan regolith. The extent of plagioclase depletion could be an indicator of regolith maturity.
Edinburgh Research Explorer Don’t Say Yes, Say Yes: Interacting with Synthetic Speech Using Tonetable

This demo is not about what you say but how you say it. Using a tangible system, Tonetable, we explore the shades of meaning carried by the same word said in many different ways. The same word or phrase is synthesised using the Intel Edison with different expressive techniques. Tonetable allows participants to play these different tokens and select the manner they should be synthesised for different contexts. Adopting the visual language of mid-century mod-ernism, the system provokes participants to think deeply about how they might want to say yes, oh really , or I see . Designed with the very serious objective of supporting expressive personalisation of AAC devices
Research on Efficiency of China’s Inter-Pollution Constraints

This paper adds the Air Quality Index (AQI) as an undesired output to the green economy efficiency research framework. It uses the super-efficiency SBM-DEA model to analyze the green economy efficiency of provinces in China from 2014 to 2020. The study found that the average green economic efficiency of provinces in China under air pollution constraints showed a trend of falling first and then rising, which indicates that green performance under air pollution constraints in China is not stable. Furthermore, the overall synchronization of environmental and economic performance is the best in the east, followed by the central region, and the worst in the west. Moreover, restrictions on air pollution have led to a decrease in the green economy efficiency of most western provinces. In contrast, the green economy efficiency of most central and eastern provinces has increased.
University of Birmingham Latency as a region contrast

Methods for measuring latency contrasts are evaluated against a new method utilizing the Dynamic Time Warping algorithm. They are applied on simulated data, for different signal to noise ratios and two sizes of window (broad vs narrow). The results are subjected to statistical and ROC analysis. The analysis suggests that DTW performs better than the other methods, being less sensitive to noise as well as to placement and width of the window selected.
INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN

In this paper we consider a two-country New Open Economy Macroeconomics model, and analyze the optimal monetary policy when countries cooperate in the face of a "global liquidity trap" -i.e., a situation where the two countries are simultaneously caught in liquidity traps. The notable features of the optimal policy in the face of a global liquidity trap are history dependence and international dependence. The optimality of history dependent policy is confirmed as in local liquidity trap. A new feature of monetary policy in global liquidity trap is whether or not a country's nominal interest rate is hitting the zero bound affects the target inflation rate of the other country. The direction of the effect depends on whether goods produced in the two countries are Edgeworth complements or substitutes. We also compare several classes of simple interest-rate rules. Our finding is that targeting the price level yields higher welfare than targeting the inflation rate, and that it is desirable to let the policy rate of each country respond not only to its own price level and output gap, but also to those in the other country.
cloud computing, wireless communications and the internet of things

There is a growing amount of data generated by a variety of devices of the Internet of Things (IoT). Sharing economy applications can leverage such data to provide solutions of high societal impact. Several technologies together enable the collaborative use of data through software services. This chapter describes the key developments in these technological areas. In particular, it describes advances in cloud computing that have resulted in new software architectures and deployment practices. Such improvements enable the rapid creation and deployment of new services on the cloud. Next, it highlights recent developments in wireless networks that allow heterogeneous devices to connect and share information. Furthermore, this chapter describes how IoT platforms are becoming interoperable, thus fostering collaborative access to data from diverse devices. Finally, it elaborates on how the described technologies jointly enable new sharing economy solutions through a case study on car sharing.
Seventh IEEE International Working Conference on Source Code Analysis and Manipulation-Copyright

Program slicing is a program-reduction technique for extracting statements that may influence other statements. While there exist efficient algorithms to slice sequential programs precisely, there are only two algorithms for precise slicing of concurrent interprocedural programs with recursive procedures. We implemented both algorithms for Java, applied several new optimizations and examined their precision and runtime behavior. We compared these results with two further algorithms which trade precision for speed. We show that one algorithm may produce incorrect slices and that precise slicing of concurrent programs is very expensive in terms of computation time.
Fluxes of Energy and Water Vapour from Grazed Pasture on a Mineral Soil in the Waikato

The eddy covariance (EC) technique was used to measure half hourly fluxes of energy and evaporation from 15 December 2007 to 30 November 2008 at the Scott Research Farm, located 7 km east of Hamilton. Many other supporting measurements of climate and soil variables were also made. The research addressed three objectives: 1. To examine the accuracy of the eddy covariance measurement technique. 2. Understand the surface partitioning of energy and water vapour on a diurnal to annual timescale. 3. Compare measurements of evaporation to methods of estimation. Average energy balance closure at Scott Farm was deficient by 24%, comparable to published studies of up to 30%. Three lysimeter studies were carried out to help verify eddy covariance data. These resulted in the conclusions that; 1) lysimeter pots needed to be deeper to allow for vegetation rooting depths to be encompassed adequately; 2) forcing energy balance closure was not supported by two of the studies (summer and winter); 3) latent heat flux (λE) gap filling of night time EC data during winter over estimated values by about 10 W m -2 ; and 4) the spring lysimeter study verified eddy covariance measurements including the closure forcing method. Some uncertainty still exists as to the accuracy of both lysimeter and EC methods of evaporation measurement because both methods still have potential biases, however for the purpose of this study, it would appear data are sufficiently accurate to have confidence in results. Energy and water vapour fluxes varied on both a diurnal and seasonal timescale. Diurnally, fluxes were small or negative at night and were highest during the day, usually at solar noon. Seasonally, spring and summer had the highest energy and evaporation fluxes and winter rates were small but tended to exceed available energy supply. Evaporation was constrained by soil moisture availability during summer and by energy availability during winter. Estimated annual evaporation at Scott Farm was 755 mm, 72% of precipitation. Two evaporation models were compared to eddy covariance evaporation (EEC) measurements; the FAO56 Penman-Monteith model (Eo) and the Priestley-Taylor model (EPT). Both models over estimated evaporation during dry conditions and slightly under estimated during winter. The α coefficient that is applied to EPT was not constant and a seasonally adjusted value would be most appropriate. A crop coefficient of 1.13 is needed for Eo measurements during moist conditions. Eo began over estimating evaporation when soil moisture contents dropped below ~44%. A water stress adjustment was applied to both models which improved evaporation estimates, however early onset of drying was not able to be adjusted for. The adjusted Eo model is the most accurate overall, when compared to EEC.
Analysing empowerment-oriented email consultation for parents

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Unlocking Synergy in Bimetallic Catalysts by Core-Shell Design

Extending the toolbox from mono- to bimetallic catalysts is key in realizing efficient chemical processes. Traditionally, the performance of bimetallic catalysts featuring one active and one selective metal is optimized by varying the metal composition, often resulting in a compromise between the catalytic properties of the two metals. Here we show that by designing the atomic distribution of bimetallic Au-Pd nanocatalysts, we obtain a synergistic catalytic performance in the industrially relevant selective hydrogenation of butadiene. Our single crystalline Au-core Pd-shell nanorods were up to 50 times more active than their alloyed and monometallic counterparts, while retaining high selectivity. We find a shell thickness dependent catalytic activity, indicating that not only the nature of the surface but also several sub-surface layers play a crucial role in the catalytic performance, and rationalize this finding using density-functional-theory calculations. Our results open up a novel avenue for the structural design of bimetallic catalysts.
Beta bursts question the ruling power for brain-computer interfaces

Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience. In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in “left vs. right hand” motor imagery tasks. Current decoding approaches for such tasks take advantage of the fact that motor imagery generates time-locked changes in induced power in the sensorimotor cortex, and rely on band-pass filtered power changes or covariance matrices which also describe co-varying power changes in signals recorded from different channels. Although little is known about the dynamics of beta burst activity during motor imagery, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements. We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.