Reviewerly/exHarmony huggingface dataset:
Simple Multi-Resolution Representation Learning for Human Pose Estimation

Human pose estimation - the process of recognizing human keypoints in a given image - is one of the most important tasks in computer vision and has a wide range of applications including movement diagnostics, surveillance, or self-driving vehicle. The accuracy of human keypoint prediction is increasingly improved thanks to the burgeoning development of deep learning. Most existing methods solved human pose estimation by generating heatmaps in which the ith heatmap indicates the location confidence of the ith keypoint. In this paper, we introduce novel network structures referred to as multi-resolution representation learning for human keypoint prediction. At different resolutions in the learning process, our networks branch off and use extra layers to learn heatmap generation. We firstly consider the architectures for generating the multi-resolution heatmaps after obtaining the lowest-resolution feature maps. Our second approach allows learning during the process of feature extraction in which the heatmaps are generated at each resolution of the feature extractor. The first and second approaches are referred to as multi-resolution heatmap learning and multi-resolution feature map learning respectively. Our architectures are simple yet effective, achieving good performance. We conducted experiments on two common benchmarks for human pose estimation: MSCOCO and MPII dataset. The code is made publicly available at https://github.com/tqtrunghnvn/SimMRPose.
Divergent Search for Few-Shot Image Classification

When data is unlabelled and the target task is not known a priori, divergent search offers a strategy for learning a wide range of skills. Having such a repertoire allows a system to adapt to new, unforeseen tasks. Unlabelled image data is plentiful, but it is not always known which features will be required for downstream tasks. We propose a method for divergent search in the few-shot image classification setting and evaluate with Omniglot and Mini-ImageNet. This high-dimensional behavior space includes all possible ways of partitioning the data. To manage divergent search in this space, we rely on a meta-learning framework to integrate useful features from diverse tasks into a single model. The final layer of this model is used as an index into the `archive' of all past behaviors. We search for regions in the behavior space that the current archive cannot reach. As expected, divergent search is outperformed by models with a strong bias toward the evaluation tasks. But it is able to match and sometimes exceed the performance of models that have a weak bias toward the target task or none at all. This demonstrates that divergent search is a viable approach, even in high-dimensional behavior spaces.
Short Paper: Design and Evaluation of Privacy-preserved Supply Chain System based on Public Blockchain

Securing the traceability of products in the supply chain is an urgent issue. Recently, supply chain systems that use public blockchain (PBC) have been proposed. In these systems, PBC is used as a common database shared between supply chain parties to secure the integrity and reliability of distribution information such as ownership transfer records. Thus, these systems secure a high level of traceability in the supply chain. However, the distribution information, which can be private information, is made public since the information recorded in PBC can be read by anyone. In this paper, we propose a method for preserving privacy while securing traceability in a supply chain system using PBC. The proposed method preserves privacy by concealing the distribution information via encryption. In addition, the proposed method ensures distribution among legitimate supply chain parties while concealing their blockchain address by using a zero-knowledge proof to prove their authenticity. We implement the proposed method on Ethereum smart contracts and evaluate cost performance based on transaction fees. The results show that the fee per party is at most 2.6 USD.
REVISED EARLY MIOCENE STRATIGRAPHY AND YOUNGER STRUCTURES IN THE LANE MOUNTAIN-NORTH CALICO MOUNTAINS AREA, CENTRAL MOJAVE DESERT, CALIFORNIA

Detailed geologic mapping and geochronology provide new insights into Early Miocene deposition and subsequent structural history in the Lane Mountain - North Calico Mountains area north of Barstow, California. The recent work revises the early Miocene stratigraphy, depositional setting and adds structural data. McCulloh (1960) defined and named the early Miocene Jackhammer and Pickhandle Formations that unconformably overlie eroded Paleozoic and Mesozoic rocks. The Jackhammer and Pickhandle Formations are volcanic clastic sequences and hypabyssal intrusives and domes thought to have accumulated in a northwest trending hangingwall basin of the Central Mojave Metamorphic Core Complex (CMMCC) (Fillmore and Walker 1996). McCulloh interpreted the Lane Mountain Rhyolite as the youngest (Pliocene?) of the sequences. Radiometric dating by Burke et.al. (1982) determined the Lane Mountain rhyolite was early Miocene (23.1 Ma) and thought to be the oldest Miocene rocks. Recent radiometric dating (22.3 ma) by Stone et.al. (2019) confirms the Early Miocene age. Detailed mapping identified >50 meters of Jackhammer Formation that underlies Lane Mountain Rhyolite, and the Lower Jackhammer is the oldest of the Miocene rocks. The Jackhammer has a very distinctive non volcanic basal conglomerate throughout the area. At Lane Mountain, tilted Jackhammer is unconformably overlain by Lane Mountain rhyolite (23 Ma). Younger Miocene rocks in the area include Upper Jackhammer, the Pickhandle Fm. and numerous 18-19.3 ma dacite flows, plugs, and sills that intrude the Pickhandle, and possible younger (17 ma) dacite domes (Burke et. al. 1982, Singleton and Gans 2008). Based on thermochronology CMMCC related extension started ~21 ma (Gans et. al. 2005). Thus, Lower Jackhammer and Lane Mountain rhyolite pre-date onset of CMMCC related extension and may be pre-extensional deposits related to local volcanic and non-volcanic sources. Deformation of the Miocene rocks includes tilting, angular unconformities, abundant syn- and post-depositional faulting, gravity slides, and localized intense folding of Miocene rocks adjacent to some faults, within roof pendants, and recumbent folds adjacent to dacite sills. Several potentially active north and northwest trending strike slip faults with displacement up to 2.0 km were also mapped.
Impact of lossy compression of nanopore raw signal data on basecalling and consensus accuracy

Abstract Motivation Nanopore sequencing provides a real-time and portable solution to genomic sequencing, enabling better assembly, structural variant discovery and modified base detection than second generation technologies. The sequencing process generates a huge amount of data in the form of raw signal contained in fast5 files, which must be compressed to enable efficient storage and transfer. Since the raw data is inherently noisy, lossy compression has potential to significantly reduce space requirements without adversely impacting performance of downstream applications. Results We explore the use of lossy compression for nanopore raw data using two state-of-the-art lossy time-series compressors, and evaluate the tradeoff between compressed size and basecalling/consensus accuracy. We test several basecallers and consensus tools on a variety of datasets at varying depths of coverage, and conclude that lossy compression can provide 35-50% further reduction in compressed size of raw data over the state-of-the-art lossless compressor with negligible impact on basecalling accuracy (≲0.2% reduction) and consensus accuracy (≲0.002% reduction). In addition, we evaluate the impact of lossy compression on methylation calling accuracy and observe that this impact is minimal for similar reductions in compressed size, although further evaluation with improved benchmark datasets is required for reaching a definite conclusion. The results suggest the possibility of using lossy compression, potentially on the nanopore sequencing device itself, to achieve significant reductions in storage and transmission costs while preserving the accuracy of downstream applications. Availability The code is available at https://github.com/shubhamchandak94/lossy_compression_evaluation . Supplementary information Supplementary data are available at Bioinformatics online. Contact schandak@stanford.edu
LibGuides: Children & Young Adult Book Awards: Getting Started

A guide for librarians, educators, parents, students, young people and others interested in award-winning and honor books for children and young adults.
Classical Coding Approaches to Quantum Applications

Quantum information science strives to leverage the quantum-mechanical nature of our universe in order to achieve large improvements in certain information processing tasks. In deep-space optical communications, current receivers for the pure-state classical-quantum channel first measure each qubit channel output and then classically post-process the measurements. This approach is sub-optimal. In this dissertation we investigate a recently proposed quantum algorithm for this task, which is inspired by classical belief-propagation algorithms, and analyze its performance on a simple $5$-bit code. We show that the algorithm is optimal for each bit and it appears to achieve optimal performance when deciding the full transmitted message. We also provide explicit circuits for the algorithm in terms of standard gates. This suggests a near-term quantum communication advantage over the aforementioned sub-optimal scheme. Quantum error correction is vital to building a universal fault-tolerant quantum computer. We propose an efficient algorithm that can translate a given logical Clifford operation on a stabilizer code into all (equivalence classes of) physical Clifford circuits that realize that operation. In order to achieve universality, one also needs to implement at least one non-Clifford logical operation. So, we develop a mathematical framework for a large subset of diagonal operations in the Clifford hierarchy, which we call Quadratic Form Diagonal (QFD) gates. Then we use the QFD formalism to characterize all stabilizer codes whose code spaces are preserved under the transversal action of the non-Clifford $T$ gates on the physical qubits. We also discuss a few purely-classical coding problems motivated by transversal $T$ gates. A conscious effort has been made to keep this dissertation self-contained, by including necessary background material on quantum information and computation.
The Security of Lazy Users in Out-of-Band Authentication

Faced with the threats posed by man-in-the-middle attacks, messaging platforms rely on “out-of-band” authentication, assuming that users have access to an external channel for authenticating one short value. For example, assuming that users recognizing each other’s voice can authenticate a short value, Telegram and WhatApp ask their users to compare 288-bit and 200-bit values, respectively. The existing protocols, however, do not take into account the plausible behavior of users who may be “lazy” and only compare parts of these values (rather than their entirety). Motivated by such a security-critical user behavior, we study the security of lazy users in out-of-band authentication. We start by showing that both the protocol implemented by WhatsApp and the statistically optimal protocol of Naor, Segev, and Smith (CRYPTO’06) are completely vulnerable to man-in-the-middle attacks when the users consider only a half of the out-of-band authenticated value. In this light, we put forward a framework that captures the behavior and security of lazy users. Our notions of security consider both statistical security and computational security, and for each flavor we derive a lower bound on the tradeoff between the number of positions that are considered by the lazy users and the adversary’s forgery probability. Within our framework, we then provide two authentication protocols. First, in the statistical setting, we present a transformation that converts any out-of-band authentication protocol into one that is secure even when executed by lazy users. Instantiating our transformation with a new refinement of the protocol of Naor et al. results in a protocol whose tradeoff essentially matches our lower bound in the statistical setting. Then, in the computational setting, we show that the computationally optimal protocol of Vaudenay (CRYPTO’05) is secure even when executed by lazy users—and its tradeoff matches our lower bound in the computational setting.
Sistem Penentuan Penginapan dengan Metode Promethee

Every person who travels to a place for a period of days requires lodging with certain criteria and different possibilities for each person. The problem lies in determining the desired lodging recommendations because there are too many choices and various criteria. This study conducted a search for lodging in Yogyakarta with criteria for distance, price, class of lodging, room facilities and supporting facilities. The Promethee method is used to get the rankings of each alternatef by calculating the weights for each criterion. Alternative values desired by visitors are processed to find the value of criteria and preference criteria to then be calculated leaving flow and entering flow to get net flow. This research produces a ranking of data that has been tested using a system with the highest net flow value is 0.5 ie alternative lodging 1 with this is prioritized according to the criteria desired by visitors.
An Investigation of Denoising Parameters Choice in two Perona-Malik Models

The paper addresses the problem of no-reference parameter choice for image denoising by Perona-Malik image diffusion algorithm using two models. The idea of the approach is to analyze the difference image between noisy input image and the outcome of the denoising algorithm for the presence of structured data from the input image. The analysis consists of the calculation of the mutual information - a value that shows the ratio between the structured data and the noise. We apply the proposed method to photographic images, vector graphics images and to retinal images with modeled Gaussian noise with different parameters.
Boer, Adolph Oswald - Biographical entry - Index to the AMA archive

Boer, Adolph Oswald - Biographical entry - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
End-to-End Learning for Video Frame Compression with Self-Attention

One of the core components of conventional (i.e., non-learned) video codecs consists of predicting a frame from a previously-decoded frame, by leveraging temporal correlations. In this paper, we propose an end-to-end learned system for compressing video frames. Instead of relying on pixel-space motion (as with optical flow), our system learns deep embeddings of frames and encodes their difference in latent space. At decoder-side, an attention mechanism is designed to attend to the latent space of frames to decide how different parts of the previous and current frame are combined to form the final predicted current frame. Spatially-varying channel allocation is achieved by using importance masks acting on the feature-channels. The model is trained to reduce the bitrate by minimizing a loss on importance maps and a loss on the probability output by a context model for arithmetic coding. In our experiments, we show that the proposed system achieves high compression rates and high objective visual quality as measured by MS-SSIM and PSNR. Furthermore, we provide ablation studies where we highlight the contribution of different components.
Supervised Machine Learning Approach for Subjectivity/Objectivity Classification of Social Data

With the exponential growth of critiques available on social media, the distinction between subjective (i.e., emotional terms) and objective (i.e., factual terms) information is a non-trivial natural language processing task. Therefore, we focus in this paper on the subjectivity/objectivity classification of social data precisely Facebook which is the most popular social network. The proposed approach is based on supervised machine learning techniques. Similar to any supervised application, our approach is composed of three main steps which are features extraction, training and prediction. Our main contribution in this work is to introduce new features such as ontology-based N-grams feature which represent good a indicator for predicting subjective text. Multiple classifiers like Naive Bayes, Support Vector Machine, Random Forest and Multi Layer Perceptron are applied on our data set. The experimental results show that the Random Forest model achieved the highest performance metrics 0.676 of accuracy, 0.697 of precision, 0,677 of recall and 0.683 off-score.
On the Explanatory Power of the Hypothesis of Semantic Determination to Causative Alternation

According to the hypothesis of semantic determination, meaning is a key to verb behavior; verb fall into classes on the basis of shared components of meaning, and members in the same semantic class may share patterns of behavior. This paper aims to verify the validity of component analysis in predicting syntactic behavior. It first makes a brief introduction to causative alternation, then compares the meaning components and usage of three verbs, i.e., break, cut and bake. Finally, two contradictions are found in the alternation analysis of these verbs. Therefore, the component analysis cannot completely predict their alternation type. Consequently, the explanatory power of this hypothesis is very limited as far as causative alternation is concerned.
Late Cretaceous-Middle Eocene olistrostromal pelagic units in the Biga Peninsula (NW Anatolia); Balıkkaya formation

Balıkkaya formation consists of olistostromal units with a burgundy coloured mudstone, siltstone and pelagic limestone matrix containing various sizes of UpperJurassic-Lower Cretaceous limestone (Bilecik Limestone) blocks and Triassic Karakaya Complex blocks, which crop out in the west, south and southwest of Biga Town in Biga Peninsula (NW Anatolia). The matrix of Balıkkaya formation, of which age and sedimentary environment are controversial, contains Late Cretaceous (Maastrichtian) Abathomphalus mayaroensis (Bolli), Abathomphalus sp., Rosita fornicata (Plummer), Globotruncanidae, early Paleocene (Danian) Morozovella pseudobulloides (Plummer), late Paleocene (Thanetian) Morozovella velascoensis (Bolli), early Eocene Acarinina pentacamarata (Subbotina), middle Eocene Turborotalia frontosa (Subbotina), Turborotalia cerroazulensis (Cole), Orbulinoides beckmanni (Saito), Hantkenina sp. pelagic foraminifera and Radiolariafossils representing the deep marine environment. These paleontological, lithological and sedimentological data obtained from Balıkkaya formation show that Balıkkaya formation developed in a deep marine environment under tectonic control starting from Late Cretaceous and ending in early-middle Eocene. This pelagic unit indicates the presence of a extensional tectonic regime on the Biga Peninsula and the fault activity along the southeastern edge of the Thrace Basin in the Late Cretaceous-Middle Eocene time interval.
Schultze, Alex - Biographical entry - Index to the AMA archive

Schultze, Alex - Biographical entry - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
Research Guides: The Open Science Framework (OSF) at Virginia Tech: Center for Open Science

This libguide introduces you to the free research management platform, the Open Science Framework. To access OSF, visit https://osf.io/institutions/vt/
Extending Text Informativeness Measures to Passage Interestingness Evaluation (Language Model vs. Word Embedding)

Standard informativeness measures used to evaluate Automatic Text Summarization mostly rely on n-gram overlapping between the automatic summary and the reference summaries. These measures differ from the metric they use (cosine, ROUGE, Kullback-Leibler, Logarithm Similarity, etc.) and the bag of terms they consider (single words, word n-grams, entities, nuggets, etc.). Recent word embedding approaches offer a continuous alternative to discrete approaches based on the presence/absence of a text unit. Informativeness measures have been extended to Focus Information Retrieval evaluation involving a user's information need represented by short queries. In particular for the task of CLEF-INEX Tweet Contextualization, tweet contents have been considered as queries. In this paper we define the concept of Interestingness as a generalization of Informativeness, whereby the information need is diverse and formalized as an unknown set of implicit queries. We then study the ability of state of the art Informativeness measures to cope with this generalization. Lately we show that with this new framework, standard word embeddings outperforms discrete measures only on uni-grams, however bi-grams seems to be a key point of interestingness evaluation. Lastly we prove that the CLEF-INEX Tweet Contextualization 2012 Logarithm Similarity measure provides best results.
Apakah perceived usefulness dapat meningkatkan continuance intention?

The purpose of this study is to find out the perceived usefulness and continuance intention. The design of this study was a cross-sectional method. This study uses a descriptive approach with explanatory survey method. A total of 145 respondents were selected for using probability sampling. A questionnaire was used as a research instrument to collect data from respondents. The analysis technique used is descriptive technique using frequency distribution. Based on the results of the study using descriptive analysis, obtained perceived usefulness results in a good category, and good enough category for continuance intentions. The difference in this study lies in the object of the study, the time of the study, the measuring instrument, the literature used, the theory used and the results of the study.