Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting knowledge distillation for light-weight visual object detection

An essential element for intelligent perception in mechatronic and robotic systems (M&RS) is the visual object detection algorithm. With the ever-increasing advance of artificial neural networks (ANN), researchers have proposed numerous ANN-based visual object detection methods that have proven to be effective. However, networks with cumbersome structures do not befit the real-time scenarios in M&RS, necessitating the techniques of model compression. In the paper, a novel approach to training light-weight visual object detection networks is developed by revisiting knowledge distillation. Traditional knowledge distillation methods are oriented towards image classification is not compatible with object detection. Therefore, a variant of knowledge distillation is developed and adapted to a state-of-the-art keypoint-based visual detection method. Two strategies named as positive sample retaining and early distribution softening are employed to yield a natural adaption. The mutual consistency between teacher model and student model is further promoted through a hint-based distillation. By extensive controlled experiments, the proposed method is testified to be effective in enhancing the light-weight network’s performance by a large margin.
Intello Labs: Non-Destructive Digital Commodity Grading

This case illustrates Intello Labs, a leading agri-tech company, operating out of Gurgaon. As an agri-tech company, Intello Labs is trying to create an artificial intelligence (AI)-based solution model for its clients. The case dives deep into the issues of degrading farm productivity being faced by Kerala Cardamom Processing and Marketing Company (KCPMC), a client of Intello Labs. The case stands out as a means for understanding the application of the AI-based solutions being offered by Intello Labs to solve the degrading farm productivity issue of KCPMC. It addresses the concerns of the current agricultural-based businesses which mostly depend on manual commodity grading.
Fault detection and isolation for discrete-time Markovian jump systems with generally bounded transition probabilities: A zonotope-based method

This paper addresses the fault detection and isolation for a class of discrete-time Markovian jump system with generally bounded transition probabilities. The proposed method is mainly based on a bank of L ∞ robust observers, which can detect the actuator fault occurrence and, even more, can locate those subjected to the fault. Furthermore, the performance and the robustness of the designed L ∞ robust observer are disseminated respect to disturbances and measurement noises. In particular, we propose the zonotop-based dynamic threshold to predict the ranges of the residual, which shows less conservatism in comparison to traditional methods. Finally, pertinent simulations are performed to corroborate the potential and superiority of the proposed method. In general, the proposed method offers effective fault detection and isolation scheme for Markovian jump systems, which can detect the fault timely and accurately with lowering the rate of fault missing report to a great extent, thus having practical value.
Real-time monitoring of granule properties during high shear wet granulation by near-infrared spectroscopy with a chemometrics approach

We developed an in-line near-infrared spectroscopy (NIRS) monitoring method enabling a rapid and non-invasive analysis of granule properties during the high shear wet granulation (HSWG) process. Eleven batches were manufactured and used as the calibration dataset and an additional batch was manufactured and used as the validation dataset. The HSWG process was directly monitored using an acousto-optical tunable filter (AOTF)-NIR spectrometer and the NIR spectra related to the physical and chemical changes of the granules were acquired. The particle size, tapped density, bulk density, and powder flowability of the granules were qualitatively and quantitatively evaluated by a chemometrics approach. Principal component analysis (PCA) and partial least square regression (PLSR) were applied for the qualitative and quantitative modelling of the granule properties. The PCA score plots showed a clear relationship between the granule properties and the granulation progress and provided an effective means for the endpoint determination of the manufacturing process. The PLSR models constructed for the quantitative evaluation of the granule properties were demonstrated to be predictive with high accuracy. These findings allow HSWG to be monitored in-line and controlled effectively. A better understanding of the process by NIRS with chemometrics will contribute to developing high-quality drug products using the required quality-by-design approach.
All-optical simultaneous XOR-AND operation using 1-D periodic nonlinear material

Abstract In this paper, an all-optical XOR-AND gate operation has been proposed using one-dimensional periodic nonlinear material model. This structure consists of alternating layers of different nonlinear materials. In this design, we can obtain XOR and AND logical operation simultaneously at the reflected and transmitted port of the periodic structure. Numerical simulation has also been done using the finite-difference-time-domain (FDTD) method. The response time of this switching operation is picoseconds (ps) range order. We find low insertion loss (−3.01 dB), high contrast ratio (14.13 dB) and high extension ratio (10.93 dB) of this device. This design will be useful in future all-optical computing.
Identi cation and Allele Mining of New Candidate Genes Underlying Rice Grain Weight and Grain Shape by Genome- Wide Association Study

Yanan Niu Chinese Academy of Agricultural Sciences Institute of Crop Sciences Tianxiao Chen Chinese Academy of Agricultural Sciences Institute of Crop Sciences Kai Chen Chinese Academy of Agricultural Sciences Agricultural Genomes Institute at Shenzhen Congcong Shen Chinese Academy of Agricultural Sciences Agricultural Genomes Institute at Shenzhen Huizhen Chen Pngxiang Institute of Agricultural Sciences Zhichao Wu Chinese Academy of Agricultural Sciences Institute of Crop Sciences Chunchao Wang Chinese Academy of Agricultural Sciences Institute of Crop Sciences Tianqing Zheng Chinese Academy of Agricultural Sciences Institute of Crop Sciences Jianlong Xu (  xujlcaas@126.com ) Chinese Academy of Agricultural Sciences Institute of Crop Sciences https://orcid.org/0000-0001-6623-1256
Atmospheric photo-oxidation of myrcene: OH reaction rate constant, gas phase oxidation products and radical budgets

Abstract. The photo-oxidation of myrcene, a monoterpene species emitted by plants, was investigated at atmospheric conditions in the outdoor simulation chamber SAPHIR. The chemical structure of myrcene consists of one moiety that is a conjugated π-system (similar to isoprene) and another moiety that is a triple-substituted olefinic unit (similar to 2-methyl-2-butene). Hydrogen shift reactions of organic peroxy radicals RO2 formed in the reaction of isoprene with atmospheric OH radicals are known to be of importance for the regeneration of OH. Structure-activity relationships (SAR) suggest that similar hydrogen shift reactions like in isoprene may apply to the isoprenyl part of RO2 radicals formed during the OH oxidation of myrcene. In addition, SAR predicts further isomerization reactions that would be competitive with bi-molecular RO2 reactions for chemical conditions that are typical for forested environments with low concentrations of nitric oxide. Assuming that OH peroxy radicals can rapidly interconvert by addition and elimination of O2 like in isoprene, bulk isomerization rate constants of 0.21 s−1 and 0.097 s−1 (T = 298 K) for the 3 isomers resulting from the 3'-OH and 1-OH addition, respectively, can be derived from SAR. Measurements of radicals and trace gases in the experiments allowed to calculate radical production and destruction rates, which are expected to be balanced. Largest discrepancies between production and destruction rates were found for RO2. Additional loss of organic peroxy radicals due to isomerization reactions could explain the observed discrepancies. The uncertainty of the total radical (ROx = OH+HO2+RO2) production rates were high due to the uncertainty in the yield of radicals from myrcene ozonolysis. However, results indicate that radical production can only be balanced, if the reaction rate constant of the reaction between hydroperoxy (HO2) and RO2 radicals derived from myrcene is lower (0.9 to 1.6 × 10−11 cm3 s−1) than predicted by SAR. Another explanation of the discrepancies would be that a significant fraction of products (yield: 0.3 to 0.6) from these reactions include OH and HO2 radicals instead of radical terminating organic peroxides. Experiments also allowed to determine the yields of organic oxidation products acetone (yield: 0.45 ± 0.08) and formaldehyde (yield: 0.35 ± 0.08). Acetone and formaldehyde are produced from different oxidation pathways, so that yields of these compounds reflect the branching ratios of the initial OH addition to myrcene. Yields determined in the experiments are consistent with branching ratios expected from SAR. The yield of organic nitrate was determined from the gas-phase budget analysis of reactive oxidized nitrogen in the chamber giving a value of 0.13 ± 0.03. In addition, the reaction rate constant for myrcene + OH was determined from the measured myrcene concentration yielding a value of (2.3 ± 0.3) × 10−10 cm3 s−1.
The Smallest Extraction Problem

We introduce landmark grammars , a new family of context-free grammars aimed at describing the HTML source code of pages published by large and templated websites and therefore at effectively tackling Web data extraction problems. Indeed, they address the inherent ambiguity of HTML, one of the main challenges of Web data extraction, which, despite over twenty years of research, has been largely neglected by the approaches presented in literature. We then formalize the Smallest Extraction Problem (SEP), an optimization problem for finding the grammar of a family that best describes a set of pages and contextually extract their data. Finally, we present an unsupervised learning algorithm to induce a landmark grammar from a set of pages sharing a common HTML template, and we present an automatic Web data extraction system. The experiments on consolidated benchmarks show that the approach can substantially contribute to improve the state-of-the-art.
A Study of Failure Recovery and Logging of High-Performance Parallel File Systems

Large-scale parallel file systems (PFSs) play an essential role in high-performance computing (HPC). However, despite their importance, their reliability is much less studied or understood compared with that of local storage systems or cloud storage systems. Recent failure incidents at real HPC centers have exposed the latent defects in PFS clusters as well as the urgent need for a systematic analysis. To address the challenge, we perform a study of the failure recovery and logging mechanisms of PFSs in this article. First, to trigger the failure recovery and logging operations of the target PFS, we introduce a black-box fault injection tool called PFault, which is transparent to PFSs and easy to deploy in practice. PFault emulates the failure state of individual storage nodes in the PFS based on a set of pre-defined fault models and enables examining the PFS behavior under fault systematically. Next, we apply PFault to study two widely used PFSs: Lustre and BeeGFS. Our analysis reveals the unique failure recovery and logging patterns of the target PFSs and identifies multiple cases where the PFSs are imperfect in terms of failure handling. For example, Lustre includes a recovery component called LFSCK to detect and fix PFS-level inconsistencies, but we find that LFSCK itself may hang or trigger kernel panics when scanning a corrupted Lustre. Even after the recovery attempt of LFSCK, the subsequent workloads applied to Lustre may still behave abnormally (e.g., hang or report I/O errors). Similar issues have also been observed in BeeGFS and its recovery component BeeGFS-FSCK. We analyze the root causes of the abnormal symptoms observed in depth, which has led to a new patch set to be merged into the coming Lustre release. In addition, we characterize the extensive logs generated in the experiments in detail and identify the unique patterns and limitations of PFSs in terms of failure logging. We hope this study and the resulting tool and dataset can facilitate follow-up research in the communities and help improve PFSs for reliable high-performance computing.
AnyOLAP: Analytical Processing of Arbitrary Data-Intensive Applications without ETL

The volume of data that is processed and produced by modern data-intensive applications is constantly increasing. Of course, along with the volume, the interest in analyzing and interpreting this data increases as well. As a consequence, more and more DBMSs and processing frameworks are specialized towards the efficient execution of long-running, read-only analytical queries. Unfortunately, to enable analysis, the data first has to be moved from the source application to the analytics tool via a lengthy ETL process, which increases the runtime and complexity of the analysis pipeline. In this work
Exploring multi-view perspectives on deep reinforcement learning agents for embodied object navigation in virtual home environments

Recent years have brought the exploration of embodied reinforcement learning agents in a variety of domains. One of the advantages of artificial agents is that they can obtain visual inputs simultaneously using multiple input devices. This work explores multi-view reinforcement learning for object navigation tasks in 3D rendered virtual home environments using AI2-THOR. We trained CNN based Deep Q-learning embodied agents with egocentric, allocentric, and combined egocentric-allocentric perspectives to locate an object in an unknown environment. We compared the results of the three RL agents, and evaluated them by both reward improvement rate, and reward obtained. We demonstrate that the egocentric perspective allows for faster reward accumulation in the earlier episodes, whereas the allocentric agents obtained better long-term rewards. Interesting results arise from the combined allocentric and egocentric perspective, where we found that the agent had the best overall results by harnessing the benefits of each perspective. The results show that while single perspective embodied agents each have their own advantages, combining both inputs yield the best overall reward. Our findings provide a foundation and benchmark for building embodied RL agents with multi-view perspectives.
Popular Object detection algorithms in deep learning

Object detection is applied in various field. Autonomous driving, surveillance, OCR(optical character recognition) and aerial image etc. We will look at the algorithms that are using to object detect. These algorithms are divided into two methods. The one is R-CNN algorithms [2], [5] ,[6] which based on region proposal. The other is YOLO [7] and SSD [8] which are one stage object detector based on regression/classification.
QARTA: An ML-based System for Accurate Map Services

Maps services are ubiquitous in widely used applications including navigation systems, ride sharing, and items/food delivery. Though there are plenty of efforts to support such services through designing more efficient algorithms, we believe that efficiency is no longer a bottleneck to these services. Instead, it is the accuracy of the underlying road network and query result. This paper presents QARTA; an open-source full-fledged system for highly accurate and scalable map services. QARTA employs machine learning techniques to construct its own highly accurate map, not only in terms of map topology but more importantly, in terms of edge weights. QARTA also employs machine learning techniques to calibrate its query answers based on contextual information, including transportation modality, location, and time of day/week. QARTA is currently deployed in all Taxis and the third largest food delivery company in the State of Qatar, replacing the commercial map service that was in use, and responding in real-time to hundreds of thousands of daily API calls. Experimental evaluation of QARTA shows its comparable or higher accuracy than commercial services.
Recent Progress on Bio-mechanical Energy Harvesting System from the Human Body: Comprehensive Review

Energy harvesting is a powerful technique to produce clean and renewable energy with better infrastructure improvement. The exhaustive review of recent progress and development in bio-mechanical energy harvesting (BMEH) techniques from human body is discussed in this manuscript. The BMEH from the human body is categorized into three parts, namely, piezoelectric energy harvesting (PEEH), triboelectric energy harvesting (TEEH), and Electro-magnetic Energy harvesting (EMEH). Each energy harvesting system is discussed with working principles with mathematical equations; each energy harvesting progress is discussed with a few work demonstrations. The applications of each energy harvesting from the recent research work are addressed in detail. The summary of each energy harvester from the human body or motion with advantages, limitations, performance metrics, current methods, and implemented human body parts are highlighted with Tabulation. The critical challenges/issues with possible solutions are also discussed. Keywords—Bio-mechanical; energy harvesting; electromagnetic; human-body; piezoelectric; triboelectric
Bibliometric Analysis and Visualization of Online Consumer Behavior – Past, Present and Future

Understanding online consumers and their buying behavior had been the major focus for marketers and gained special attention among the marketing science researchers for over three decades. The purpose of this research is to investigate the past and current trends in online consumer behavior domain, by highlighting the research themes through bibliometric analysis and provide an agenda for future research. The study carried out a systematic literature review of online consumer behavior research with the help of bibliometric analysis from 1999 to 2020, and the bibliographic data of 1254 documents were obtained from the Scopus, one of the popular academic indexing databases. The study performed various bibliometric analysis using open-source software tools, including Biblioshiny, VOS Viewer, and Sci2 tool. This paper is unique in applying a variety of quantitative and scientific mapping tools for better understanding and visualization of the research field. The analysis of scientific research will be a reference guide for research scholars in the academic community for understanding the intellectual structure, theoretical foundations of online consumer behavior research and identify the knowledge gaps and recommend future research directions. It can also help the marketing practitioners to understand the dynamic characteristics of online consumers and their buying behavior, through various contributions made by the scientific literature.
Columnar Storage and List-based Processing for Graph Database Management Systems

We revisit column-oriented storage and query processing techniques in the context of contemporary graph database management systems (GDBMSs). Similar to column-oriented RDBMSs, GDBMSs support read-heavy analytical workloads that however have fundamentally different data access patterns than traditional analytical workloads. We first derive a set of desiderata for optimizing storage and query processors of GDBMS based on their access patterns. We then present the design of columnar storage, compression, and query processing techniques based on these desiderata. In addition to showing direct integration of existing techniques from columnar RDBMSs, we also propose novel ones that are optimized for GDBMSs. These include a novel list-based query processor, which avoids expensive data copies of traditional block-based processors under many-to-many joins, a new data structure we call single-indexed edge property pages and an accompanying edge ID scheme, and a new application of Jacobson's bit vector index for compressing NULL values and empty lists. We integrated our techniques into the GraphflowDB in-memory GDBMS. Through extensive experiments, we demonstrate the scalability and query performance benefits of our techniques.
PSMA-Specific Peptide with Inhibitor Cystine Knot for Prostate Cancer Treatment

Purpose: A comparative study of the efficiency of binding in vitro and in vivo with prostate cancer cells of artificial peptides based on U5-scytotoxin-Sth1a toxin with an inserted peptide tropic to the prostate-specific membrane antigen and the radiopharmaceutical 177Lu-PSMA-617 is carried out. Objective and Methods: The stability of peptides, toxicity, efficiency of binding to LNCaP, PC3 and CHO-K cells, as well as the biodistribution in a BALB/c mice model with inoculated prostate adenocarcinoma cells are studied using radiometric methods (radio-HPLC, TLC, scintillator radiometer). Results: Our results show that the synthesized DOTA-Knot/C0-C1, DOTAKnot/C1-C2 and DOTA-Knot/C2-C3 peptides containing the GTIQPYPFSWGY sequence inserted into U5-Sth1a knottin are more stable in blood plasma and saline and also show a similar degree of binding compared with the 177LuPSMA-617 radiopharmaceutical. Biodistribution studies of 177Lu-PSMA-617, 177Lu-DOTA-Knot/C0-C1, 177Lu-DOTA-Knot/C1-C2 and 177Lu-DOTA-Knot/C2C3 in prostate adenocarcinoma-bearing BALB/c mice demonstrate that, for peptide 177Lu-DOTA-Knot/C1-C2, the tumor binding index is 20.4% ID/g, while for the radiopharmaceutical 177Lu-PSMA-617, it is 11.9% ID/g. Conclusion: Thus, the modified peptides with a peptide tropic to the PSMA antigen inserted into the structure of U5-Sth1a toxin demonstrate the greatest stability, both in saline and in blood plasma, and good binding both to cell cultures and to tumors in the body.
Drip Irrigation Detection for Power Outage-Prone Areas with Internet-of-Things Smart Fertigation Managemant System

In drip irrigation agriculture or fertigation technique, sufficient amount of water and nutrients are crucial for a plant's growth and development. An electronic timer is usually used to control the plant watering automatically and the scheduling is set according to different levels of plant growth. The timer has to be adjusted frequently since the required amount of water is different according to the growth stages. In power outage-prone regions, the problem with scheduled irrigation using timer becomes worsen since the watering schedule is disrupted by occasional black-outs, leading to an insufficient supply of water and nutrients, which leads to poor crop yields. Typical solution for such problems is by hiring field works to monitor the functionality of the automated system, plant health, and to re-adjust the timer once a power outage occurs. However, this solution is ineffective, time-consuming, and acquires high overhead costs. This paper proposes a systematic irrigation method using the internet-of-things (IoT) framework in order to improve the monitoring of plant growth and consequently improves the efficiency of the workflow. This systematic fertigation monitoring system consists of power outage alerts, and on-line notifications of plant irrigation, pesticide delivery, and polybag cleaning schedule. As a result, by using the proposed system, higher efficiency in farming management is achieved, with a 40% reduction in manpower, as compared to a typical fertigation-based farming system. This system demonstrates greater control over irrigation scheduling, plant growth, recording of pesticide scheduling automatically, and polybag cleaning, all of which will improve crop yields significantly. Keywords—Irrigation technique; water and nutrient; automatic drip irrigation; crop; power-outage