Miracle-dz/UniFAR-FTUs huggingface dataset:
REpeating Pattern Extraction Technique (REPET), a Novel and Simple Approach for Separating the Repeating “Background” from the Non-repeating “Foreground” in a Mixture

As far as music is concerned the repetition forms the core. The repeating structure forms the characteristic of the musical piece and superimposed by various elements especially in the case of pop music. Proposed work concentrates on the Indian folk music and music voice separation from audio. The current available separation techniques are well suited for some typical music like REPET goes well with pop music as it has a constant repeating. Thus the study tries to present REPET for separating the repeating background from non repeating forebackground. The process starts with identifying the segments that repeats periodically, further comparison of this with the repeating segment derived model and then extraction of the repeating patterns using the time frequency masking. The Linear Predictive Coding (LPC) technique is used by most of the voice recognition devices in order to get input speech signal spectral information. LPC is used to generate observation vectors that are used by voice recognizers. On the basis of the parameters like Signal to Noise Ratio (SNR) the two techniques LPC and REPET are compared in the present work.
Determination of Poly-Î2-Hydroxybutyrate and Poly-Î2-Hydroxyvalerate in Activated Sludge by Gas-Liquid Chromatography

Poly-p-hydroxybutyrate (PHB) is a common bacterial lipid polymer involved in energy storage (5). It has been found in activated sludge samples from conventional wastewater treatment plants (13, 16) and in samples from biological phosphate removal plants (4, 7, 9, 10). It is considered to play an important role in phosphate removal in those plants (2, 11). However, other poly-3-hydroxyalkanoates (PHAs), such as poly-,-hydroxyvalerate (PHV), are also accumulated by the sludge and might play a similar role (3). In order to test this idea we have developed a simple routine assay for separating and accurately quantifying PHB and PHV by gas chromatography (GC), since the gravimetric (5), turbidimetric (19), spectrophotometric (8, 14, 17), and density gradient separation (12) methods did not distinguish the various forms of PHAs. Two published sensitive GC methods required too many purification steps and were unsuitable for the analysis of a large number of samples (6, 13), while one simpler method (1) was inefficient in the presence of water, caused premature column degradation due to acids and particulate debris in the extract, and could not adequately resolve PHV from neighboring peaks. The improved method presented in this paper eliminated these problems by including sludge lyophilization, purification of the chloroform extract by reextraction with water, and capillary gas-liquid chromatography. Activated sludge samples (10 to 15 ml) for the development of the technique were taken from a pilot plant treating domestic wastewater at the University of British Columbia (3), but the technique has also been applied successfully to samples from the full-scale treatment plant at Kelowna, British Columbia, Canada. After centrifugation the sludge pellets were frozen and lyophilized. A weighed amount (about 20 mg) of lyophilized sludge was combined with 2 ml of acidified methanol (3% H2SO4), containing benzoic acid as the internal standard, and 2 ml of chloroform. DL-3Hydroxybutyric acid (HB) sodium salt dissolved in acidified methanol was used as the standard. Samples and standards were heated for 3.5 h at 100°C in Pyrex test tubes (volume, 15 ml) with Teflon-lined caps. Further purification was achieved by transferring 1.9 ml of the denser chloroform phase to another Pyrex tube (volume, 10 ml) containing 0.5 ml of distilled water. This reextraction step greatly improved the reliability of the method by removing acids and particu-
No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets

Out-of-distribution detection is an important component of reliable ML systems. Prior literature has proposed various methods (e.g., MSP (Hendrycks&Gimpel, 2017), ODIN (Liang et al., 2018), Mahalanobis (Lee et al., 2018)), claiming they are state-of-the-art by showing they outperform previous methods on a selected set of in-distribution (ID) and out-of-distribution (OOD) datasets. In this work, we show that none of these methods are inherently better at OOD detection than others on a standardized set of 16 (ID, OOD) pairs. We give possible explanations for these inconsistencies with simple toy datasets where whether one method outperforms another depends on the structure of the ID and OOD datasets in question. Finally, we show that a method outperforming another on a certain (ID, OOD) pair may not do so in a low-data regime. In the low-data regime, we propose a distance-based method, Pairwise OOD detection (POD), which is based on Siamese networks and improves over Mahalanobis by sidestepping the expensive covariance estimation step. Our results suggest that the OOD detection problem may be too broad, and we should consider more specific structures for leverage.
Public-key Authenticated Encryption with Keyword Search: Cryptanalysis, Enhanced Security, and Quantum-resistant Instantiation

With the rapid development of cloud computing, an increasing number of companies are adopting cloud storage technology to reduce overhead. However, to ensure the privacy of sensitive data, the uploaded data need to be encrypted before being outsourced to the cloud. The concept of public-key encryption with keyword search (PEKS) was introduced by Boneh et al. to provide flexible usage of the encrypted data. Unfortunately, most of the PEKS schemes are not secure against inside keyword guessing attacks (IKGA), so the keyword information of the trapdoor may be leaked to the adversary. To solve this issue, Huang and Li presented public key authenticated encryption with keyword search (PAEKS) in which the trapdoor generated by the receiver is only valid for authenticated ciphertexts. With their seminal work, many PAEKS schemes have been introduced for the enhanced security of PAEKS. Some of them further consider the upcoming quantum attacks. However, our cryptanalysis indicated that in fact, these schemes could not withstand IKGA. To fight against the attacks from quantum adversaries and support the privacy-preserving search functionality, we first introduce a novel generic PAEKS construction in this work. Then, we further present the first quantum-resistant PAEKS instantiation based on lattices. The security proofs show that our instantiation not only satisfies the basic requirements but also achieves enhanced security models, namely the multi-ciphertext indistinguishability and multi-trapdoor privacy. Furthermore, the comparative results indicate that with only some additional expenditure, the proposed instantiation provides more secure properties, making it suitable for more diverse application environments.
LOSSY IMAGE COMPRESSION WITH DEEP NEURAL NETWORKS Diogo

Automatic colorization is a process which has the objective of inferring the color components of grayscale images. Frequently used for artistic purposes or to restore damaged images, this process can be, however, employed has a tool in the field of image compression, since it allows information associated with color not to be coded and transmitted if an efficient colorization is carried after decoding. Recently, several methods leveraging on deep learning (i
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

Therapeutics machine learning is an emerging field with incredible opportunities for innovatiaon and impact. However, advancement in this field requires formulation of meaningful learning tasks and careful curation of datasets. Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics. To date, TDC includes 66 AI-ready datasets spread across 22 learning tasks and spanning the discovery and development of safe and effective medicines. TDC also provides an ecosystem of tools and community resources, including 33 data functions and types of meaningful data splits, 23 strategies for systematic model evaluation, 17 molecule generation oracles, and 29 public leaderboards. All resources are integrated and accessible via an open Python library. We carry out extensive experiments on selected datasets, demonstrating that even the strongest algorithms fall short of solving key therapeutics challenges, including real dataset distributional shifts, multi-scale modeling of heterogeneous data, and robust generalization to novel data points. We envision that TDC can facilitate algorithmic and scientific advances and considerably accelerate machine-learning model development, validation and transition into biomedical and clinical implementation. TDC is an open-science initiative available at https://tdcommons.ai.
PoGO-Net: Pose Graph Optimization with Graph Neural Networks

Accurate camera pose estimation or global camera re-localization is a core component in Structure-from-Motion (SfM) and SLAM systems. Given pair-wise relative camera poses, pose-graph optimization (PGO) involves solving for an optimized set of globally-consistent absolute camera poses. In this work, we propose a novel PGO scheme fueled by graph neural networks (GNN), namely PoGO-Net, to conduct the absolute camera pose regression leveraging multiple rotation averaging (MRA). Specifically, PoGO-Net takes a noisy view-graph as the input, where the nodes and edges are designed to encode the geometric constraints and local graph consistency. Besides, we address the outlier edge removal by exploiting an implicit edge-dropping scheme where the noisy or corrupted edges are effectively filtered out with parameterized networks. Furthermore, we introduce a joint loss function embedding MRA formulation such that the robust inference is capable of achieving real-time performances even for large-scale scenes. Our proposed network is trained end-to-end on public benchmarks, outperforming state-of-the-art approaches in extensive experiments that demonstrate the efficiency and robustness of our proposed network.
An extension of the Reformulation-Linearization Technique to nonlinear optimization

We introduce a novel Reformulation-Perspectification Technique (RPT) to obtain convex approximations of nonconvex continuous optimization problems. RPT consists of two steps, those are, a reformulation step and a perspectification step. The reformulation step generates redundant nonconvex constraints from pairwise multiplication of the existing constraints. The perspectification step then convexifies the nonconvex components by using perspective functions. The proposed RPT extends the existing Reformulation-Linearization Technique (RLT) in two ways. First, it can multiply constraints that are not linear or not quadratic, and thereby obtain tighter approximations than RLT. Second, it can also handle more types of nonconvexity than RLT. We demonstrate the applicability of RPT by extensively analyzing all 15 possibilities of pairwise multiplication of the five basic cone constraints (linear cone, second-order cone, power cone, exponential cone, semi-definite cone). We show that many well-known RLT based results can also be obtained and extended by applying RPT. Numerical experiments on dike height optimization and convex maximization problems demonstrate the effectiveness of the proposed approach.
Assessing the protective effects of cryptotanshinone on CoCl2-induced hypoxia in RPE cells

The development of several retinal diseases is closely related to hypoxia. As a component of the Traditional Chinese medicine Salvia miltiorrhiza, the effects of cryptotanshinone (CT) on retinal cells under hypoxic conditions are not well understood. The aim of the present study was to explore how CT exerted its protective effects on retinal pigment epithelium (RPE) cells under hypoxic conditions induced by cobalt chloride (CoCl2). The effects of CT were investigated using a Cell Counting Kit-8 assay, Annexin V-FITC/PI staining, reverse transcription-quantitative PCR and western blotting in ARPE-19 cells. CT (10 and 20 µM) reduced the CoCl2-induced increase in vascular endothelial growth factor expression and hypoxia-inducible transcription factor-1α expression in ARPE-19 cells. Additionally, CT alleviated hypoxia-induced apoptosis by regulating Bcl-2 and Bax protein expression. CT treatment also reduced the increase in the mRNA levels of IL-6, IL-1β and TNF-α induced by CoCl2. In summary, CT may protect RPE cells against apoptosis and inflammation in CoCl2-induced hypoxia, and these results warrant further in vivo study into its value as a drug for treating hypoxic eye diseases.
Guiding Multi-Step Rearrangement Tasks with Natural Language Instructions

: Enabling human operators to interact with robotic agents using natural language would allow non-experts to intuitively instruct these agents. Towards this goal, we propose a novel Transformer-based model which enables a user to guide a robot arm through a 3D multi-step manipulation task with natural language commands. Our system maps images and commands to masks over grasp or place locations, grounding the language directly in perceptual space. In a suite of block rearrangement tasks, we show that these masks can be combined with an existing manipulation framework without re-training, greatly improving learning efﬁciency. Our masking model is several orders of magnitude more sample efﬁ-cient than typical Transformer models, operating with hundreds, not millions, of examples. Our modular design allows us to leverage supervised and reinforcement learning, providing an easy interface for experimentation with different architectures 2 . Our model completes block manipulation tasks with synthetic commands 530% more often than a UNet-based baseline, and learns to localize actions correctly while creating a mapping of symbols to perceptual input that supports compositional reasoning. We provide a valuable resource for 3D manipulation instruction following research by porting an existing 3D block dataset with crowdsourced language to a simulated environment. Our method’s 25 . 3% absolute improvement in identifying the correct block on the ported dataset demonstrates its ability to handle syntactic and lexical variation.
Leveraging Theoretical Tradeoﬀs in Hyperparameter Selection for Improved Empirical Performance

The tradeoﬀs in the excess risk incurred from data-driven learning of a single model has been studied by decomposing the excess risk into approximation, estimation and optimization errors. In this paper, we focus on the excess risk incurred in data-driven hyperparameter optimization (HPO) and its interaction with approximate empirical risk minimization (ERM) necessitated by large data. We present novel bounds for the excess risk in various common scenarios in HPO. Based on these results, we propose practical heuristics that allow us to improve performance or reduce computational overhead of data-driven HPO, demonstrating over 2 × speedup with no loss in predictive performance in our preliminary results
Conditionally Tractable Density Estimation using Neural Networks

Tractable models such as cutset networks and sum-product networks (SPNs) have become increasingly popular because they have superior predictive performance. Among them, cutset networks, which model the mechanics of Pearl’s cutset conditioning algorithm, demonstrate great scalability and prediction accuracy. Existing research on cutset networks has mainly focused on discrete domains, and the best mechanism to extend cutset networks to continuous domains is unclear. We propose one possible alternative to cutset networks that models the full joint distribution as the product of a local, complex distribution over a small subset of variables and a fully tractable conditional distribution whose parameters are controlled using a neural network. This model admits exact inference when all variables in the local distribution are observed, and although the model is not fully tractable in general, we show that “cutset” sampling can be employed to efficiently generate accurate predictions in practice. We show that our model performs comparably or better than existing competitors through a variety of prediction tasks on real datasets.
ANovelResearchTechnology toExplore theMysteryofTraditional Chinese Medicine : Optogenetics

Traditional Chinese medicine (TCM) is gaining increasing popularity worldwide for the function of health promotion and adjuvant therapy. However, the world’s understanding of TCM is far from enough, which seriously limits the modernization and internationalization of TCM. /erefore, modern and efficient analytical methods are urgently needed to understand the mechanism of TCM. Optogenetics is one of the most prevalent technologies in the 21 century and has been used to explore life science, especially neuroscience. It already has had great influences in the study of neural circuits and animal models of mental diseases and was named “Method of the Year” by the Nature Methods journal in 2010. Increased interests occurred in the applications of optogenetics to explore amyriad of medical andmental health disorders. However, it has not so far been noticed by TCM researchers. We elaborated on an idea that introducing this technique into the field of TCM research to improve diagnosis, treatments, and evaluating the therapeutic effects. In this review, we made a systematic prospect in the theory, feasibility, and application of TCM optogenetics. We mainly focused on applying optogenetic methodologies to make a more comprehensive understanding of TCM.
IJPDLM-04-2020-0129_proof 656..682

Purpose – This paper aims to examine how the extant publication has related big data analytics (BDA) to supply chain planning (SCP). The paper presents a conceptual model based on the reviewed articles and the dominant research gaps and outlines the research directions for future advancement. Design/methodology/approach – Based on a systematic literature review, this study analysed 72 journal articles and reported the descriptive and thematic analysis in assessing the established body of knowledge. Findings – This study reveals the fact that literature on relating BDA to SCP has an ambiguous use of BDArelated terminologies and a siloed view on SCP processes that primarily focuses on the short-term. Looking at the big data sources, the objective of adopting BDA and changes to SCP, we identified three roles of big data and BDA for SCP: supportive facilitator, source of empowerment and game-changer. It bridges the conversation between BDA technology for SCP and its management issues in organisations and supply chains according to the technology-organisation-environmental framework. Research limitations/implications – This paper presents a comprehensive examination of existing literature on relating BDA to SCP. The resulted themes and research opportunities will help to advance the understanding of how BDA will reshape the future of SCP and how to manage BDA adoption towards a big data-driven SCP. Originality/value – This study is unique in its discussion on how BDA will reshape SCP integrating the technical and managerial perspectives, which have not been discussed to date.
Surface-atmosphere and Gas-particle Partitioning of Ammonia: Measurements, Modelling, and Trend Analysis

Atmospheric ammonia is of interest because of its impacts on ecosystem and human health, as well as climate. Processes of surface-atmosphere and gas-particle exchange were investigated through measurements, modelling, and long-term trend analysis. A Quantum Cascade-Tuneable Infrared Laser Differential Absorption Spectrometer (QC-TILDAS) was characterised for the purposes of making direct eddy covariance (EC) flux measurements of bi-directional surfaceatmosphere ammonia exchange. While this instrument has great potential, improvements are needed in both sensitivity and time response of the instrument if it is to be used to make EC flux measurements. This instrument was further used to measure vertical profiles of ammonia mixing ratios, investigating the influence of the surface on column distribution, and the variation in diurnal trends with altitude. Ammonia mixing ratios were found to increase towards the surface, with greater variability during the daytime than at night. Diurnal trends in ammonia were found to change with altitude: all heights showed a daytime maximum, and the lowest measurement height showed an additional nighttime maximum. Atmospheric ammonia is closely tied to aerosol particle composition, especially in terms of particle acidity, which can alter the health and climate impacts of such particles. A 21 year dataset of particle composition was used to determine spatial and temporal trends in particle acidity. It was found that particle acidity iii decreased significantly over this time period, concurrent with decreases in emissions of SO2 and NOx, with implications for air quality and atmospheric chemistry. Additionally, there is a need for greater consistency in metrics used to describe particle acidity. Finally, thermodynamic and kinetic models were used to explore the potential impacts of this particle acidity on gas-particle, and consequently surface-atmosphere exchange of ammonia, demonstrating the connection between these two equilibrium processes.
Learning to Acquire and Adapt Contact-Rich Manipulation Skills with Motion Primitives

: 1 Learning generalizable robotic skills in a data-efﬁcient manner has long been a 2 challenge in the robotics community. While the current state-of-the-art methods 3 using reinforcement learning (RL) show promising performance in acquiring ma-4 nipulation skills, the algorithms are often data-hungry and hard to generalize. In 5 this paper, we propose a simple yet effective framework to learn and adapt manip-6 ulation skills using motion primitives. The core of the method is using blackbox 7 function optimization, optionally leveraging prior experiences similar to the task 8 of interest, to learn/adapt the motion primitive parameters. Human demonstra-9 tions are modeled to serve as dense rewards guiding the parameter learning. We 10 validate the effectiveness of the proposed method on 8 peg-in-hole-like tasks. The 11 experimental results show that our proposed framework takes less than 1 hour to 12 acquire the insertion skills and as few as 15 minutes to adapt to an unseen insertion 13 task on a physical robot. 14