Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-lingual Entity Alignment with Adversarial Kernel Embedding and Adversarial Knowledge Translation

Cross-lingual entity alignment, which aims to precisely connect the same entities in different monolingual knowledge bases (KBs) together, often suffers challenges from feature inconsistency to sequence context unawareness. This paper presents a dual adversarial learning framework for cross-lingual entity alignment, DAEA, with two original contributions. First, in order to address the structural and attribute feature inconsistency between entities in two knowledge graphs (KGs), an adversarial kernel embedding technique is proposed to extract graph-invariant information in an unsupervised manner, and project two KGs into the common embedding space. Second, in order to further improve successful rate of entity alignment, we propose to produce multiple random walks through each entity to be aligned and mask these entities in random walks. With the guidance of known aligned entities in the context of multiple random walks, an adversarial knowledge translation model is developed to fill and translate masked entities in pairwise random walks from two KGs. Extensive experiments performed on real-world datasets show that DAEA can well solve the feature inconsistency and sequence context unawareness issues and significantly outperforms thirteen state-of-the-art entity alignment methods.
An L2 Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation

Reinforcement learning (RL) algorithms based on high-dimensional function approximation have achieved tremendous empirical success in large-scale problems with an enormous number of states. However, most analysis of such algorithms gives rise to error bounds that involve either the number of states or the number of features. This paper considers the situation where the function approximation is made either using the kernel method or the two-layer neural network model, in the context of a fitted Q-iteration algorithm with explicit regularization. We establish an $\tilde{O}(H^3|\mathcal {A}|^{\frac14}n^{-\frac14})$ bound for the optimal policy with $Hn$ samples, where $H$ is the length of each episode and $|\mathcal {A}|$ is the size of action space. Our analysis hinges on analyzing the $L^2$ error of the approximated Q-function using $n$ data points. Even though this result still requires a finite-sized action space, the error bound is independent of the dimensionality of the state space.
The Need for Holistic Technical Debt Management across the Value Stream: Lessons Learnt and Open Challenges

The long lifetime and the evolving nature of industrial products make them subject to technical debt at different levels. Despite multiple years of research on technical debt management, our industrial experience shows that introducing systematic technical debt management in a large-scale company is very challenging. To identify the challenges, we provide a conceptual framework for holistic debt management across the product development value stream, which takes multiple categories of debt and their interplays into account. We use this framework to identify multiple challenges that are still open to be explored by the research community. Due to the practical nature of technical debt management, we believe this paper can guide the research community on the needs of industry for the effective application of technical debt management in practice.
Improving Hyper-Relational Knowledge Graph Completion

Different from traditional knowledge graphs (KGs) where facts are represented as entity-relation-entity triplets, hyper-relational KGs (HKGs) allow triplets to be associated with additional relation-entity pairs (a.k.a qualifiers) to convey more complex information. How to effectively and efficiently model the triplet-qualifier relationship for prediction tasks such as HKG completion is an open challenge for research. This paper proposes to improve the best-performing method in HKG completion, namely STARE, by introducing two novel revisions: (1) Replacing the computation-heavy graph neural network module with light-weight entity/relation embedding processing techniques for efficiency improvement without sacrificing effectiveness; (2) Adding a qualifier-oriented auxiliary training task for boosting the prediction power of our approach on HKG completion. The proposed approach consistently outperforms STARE in our experiments on three benchmark datasets, with significantly improved computational efficiency.
Variable Instance-Level Explainability for Text Classification

Despite the high accuracy of pretrained trans-former networks in text classiﬁcation, a persisting issue is their signiﬁcant complexity that makes them hard to interpret. Recent research has focused on developing feature scoring methods for identifying which parts of the input are most important for the model to make a particular prediction and use it as an explanation (i.e. rationale). A limitation of these approaches is that they assume that a particular feature scoring method should be used across all instances in a dataset using a prede-ﬁned ﬁxed length, which might not be optimal across all instances. To address this, we pro-pose a method for extracting variable-length explanations using a set of different feature scoring methods at instance-level. Our method is inspired by word erasure approaches which assume that the most faithful rationale for a prediction should be the one with the highest divergence between the model’s output distribution using the full text and the text after removing the rationale for a particular instance. Evaluation on four standard text classiﬁcation datasets shows that our method consistently provides more faithful explanations compared to previous ﬁxed-length and ﬁxed-feature scoring methods for rationale extraction. 1
Data-driven Optimization Model for Global Covid-19 Intervention Plans

In the wake of COVID-19, every government huddles to find the best interventions that will reduce the number of infection cases while minimizing the economic impact. However, with many intervention policies available, how should one decide which policy is the best course of action? In this work, we describe an integer programming approach to prescribe intervention plans that optimizes for both the minimal number of daily new cases and economic impact. We present a method to estimate the impact of intervention plans on the number of cases based on historical data. Finally, we demonstrate visualizations and summaries of our empirical analyses on the performance of our model with varying parameters compared to two sets of heuristics.
Tiny-scale Structure Discovered toward PSR B1557–50

Optical depth variations in the Galactic neutral interstellar medium (ISM) with spatial scales from hundreds to thousands of astronomical units have been observed through H i absorption against pulsars and continuum sources, while extremely small structures with spatial scales of tens of astronomical units remain largely unexplored. The nature and formation of such tiny-scale atomic structures (TSAS) need to be better understood. We report a tentative detection of TSAS with a signal-to-noise ratio of 3.2 toward PSR B1557−50 in the second epoch of two Parkes sessions just 0.36 yr apart, which are the closest-spaced spectral observations toward this pulsar. One absorption component showing marginal variations has been identified. Based on the pulsar’s proper motion of 14 mas yr−1 and the component’s kinematic distance of 3.3 kpc, the corresponding characteristic spatial scale is 17 au, which is among the smallest sizes of known TSAS. Assuming a similar line-of-sight (LOS) depth, the tentative TSAS cloud detected here is overdense and overpressured relative to the cold neutral medium (CNM), and can radiatively cool fast enough to be in thermal equilibrium with the ambient environment. We find that turbulence is not sufficient to confine the overpressured TSAS. We explore the LOS elongation that would be required for the tentative TSAS to be at the canonical CNM pressure, and find that it is ∼5000—much larger than filaments observed in the ISM. We see some evidence of line width and temperature variations in the CNM components observed at the two epochs, as predicted by models of TSAS-like cloud formation colliding warm neutral medium flows.
Optimal Size-Performance Tradeoffs: Weighing PoS Tagger Models

Improvement in machine learning-based NLP performance are often presented with bigger models and more complex code. This presents a trade-off: better scores come at the cost of larger tools; bigger models tend to require more during training and inference time. We present multiple methods for measuring the size of a model, and for comparing this with the model's performance.In a case study over part-of-speech tagging, we then apply these techniques to taggers for eight languages and present a novel analysis identifying which taggers are size-performance optimal. Results indicate that some classical taggers place on the size-performance skyline across languages. Further, although the deep models have highest performance for multiple scores, it is often not the most complex of these that reach peak performance.
Circuit-aware Device Modeling of Energy-efficient Monolayer WS$_2$ Trench-FinFETs

The continuous scaling of semiconductor technology has pushed the footprint of logic devices below 50 nm. Currently, logic standard cells with one single fin are being investigated to increase the integration density, although such options could severely limit the performance of individual devices. In this letter, we present a novel Trench (T-) FinFET device, composed of a monolayer twodimensional (2D) channel material. The device characteristics of a monolayer WS2-based T-FinFET are studied by combining the first-principles calculations and quantum transport (QT) simulations. These results serve as inputs to a predictive analytical model. The latter allows to benchmark the T-FinFET with strained (s)-Si FinFETs in both quasi-ballistic and diffusive transport regimes. The circuitlevel evaluation highlights that WS2 T-FinFETs exhibit a competitive energy-delay performance compared to s-Si FinFET and WS2 double-gate transistors, assuming the same mobility and contact resistivity at small footprints.
LAI Estimation of Cucumber Crop Based on Improved Fully Convolutional Network

LAI (Leaf Area Index) is of great importance for crop yield estimation in agronomy. It is directly related to plant growth status, net assimilation rate, plant photosynthesis, and carbon dioxide in the environment. How to measure LAI accurately and efficiently is the key to the crop yield estimation problem. Manual measurement consumes a lot of human resources and material resources. Remote sensing technology is not suitable for near-Earth LAI measurement. Besides, methods based on traditional digital image processing are greatly affected by environmental noise and image exposure. Nowadays, deep learning is widely used in many fields. The improved FCN (Fully Convolutional Network) is proposed in our study for LAI measure task. Eighty-two cucumber images collected from our greenhouse are labeled to fine-tuning the pre-trained model. The result shows that the improved FCN model performs well on our dataset. Our method's mean IoU can reach 0.908, which is 11% better than conventional methods and 4.7% better than the basic FCN model.
Capturing patterns of variation unique to a specific dataset

Capturing patterns of variation present in a dataset is important in exploratory data analysis and unsupervised learning. Contrastive dimension reduction methods, such as contrastive principal component analysis (cPCA), find patterns unique to a target dataset of interest by contrasting with a carefully chosen background dataset representing unwanted or uninteresting variation. However, such methods typically require a tuning parameter that governs the level of contrast, and it is unclear how to choose this parameter objectively. Furthermore, it is frequently of interest to contrast against multiple backgrounds, which is difficult to accomplish with existing methods. We propose unique component analysis (UCA), a tuning-free method that identifies low-dimensional representations of a target dataset relative to one or more comparison datasets. It is computationally efficient even with large numbers of features. We show in several experiments that UCA with a single background dataset achieves similar results compared to cPCA with various tuning parameters, and that UCA with multiple individual background datasets is superior to both cPCA with any single background data and cPCA with a pooled background dataset.
Task Space Planning with Complementarity Constraint-based Obstacle Avoidance

In this paper, we present a task space-based local motion planner that incorporates collision avoidance and constraints on end-effector motion during the execution of a task. Our key technical contribution is the development of a novel kinematic state evolution model of the robot where the collision avoidance is encoded as a complementarity constraint. We show that the kinematic state evolution with collision avoidance can be represented as a Linear Complementarity Problem (LCP). Using the LCP model along with Screw Linear Interpolation (ScLERP) in SE(3), we show that it may be possible to compute a path between two given task space poses by directly moving from the start to the goal pose, even if there are potential collisions with obstacles. Scalability of the planner is demonstrated with experiments using a physical robot. We present simulation and experimental results with both collision avoidance and task constraints to show the efficacy of our approach.
Detecting Polarized Topics in COVID-19 News Using Partisanship-aware Contextualized Topic Embeddings

Growing polarization of the news media has been blamed for fanning disagreement, controversy and even violence. Early identiﬁ-cation of polarized topics is thus an urgent matter that can help mitigate conﬂict. However, accurate measurement of polarization is still an open research challenge. To address this gap, we propose P artisanship-a ware C ontextualized T opic E mbeddings ( PaCTE ), a method to automatically detect polarized topics from partisan news sources. Speciﬁcally, we represent the ideology of a news source on a topic by corpus-contextualized topic embedding utilizing a langauge model that has been ﬁnetuned on recognizing partisanship of the news articles, and measure the polarization between sources using cosine similarity. We apply our method to a corpus of news about COVID-19 pandemic. Extensive experiments on different news sources and topics demonstrate the effectiveness of our method to precisely capture the topical polarization and alignment between different news sources. To help clarify and validate results, we explain the polarization using the Moral Foundation Theory.
Tracer-based source apportioning of atmospheric organic carbon and 
the influence of anthropogenic emissions on secondary organic aerosol 
formation in Hong Kong

Abstract. Here we conducted comprehensive chemical characterization and source apportionment of 49 PM2.5 samples collected in Hong Kong. Besides the major aerosol constituents, 39 polar organic species, including 14 secondary organic aerosol (SOA) tracers of isoprene, monoterpenes, β-caryophyllene, and naphthalene, were quantified using gas chromatography-mass spectrometry (GC-MS). Six factors, i.e., SOA, secondary sulfate (SS), biomass burning (BB)/SOA, sea salt, marine vessels, and vehicle emissions, were apportioned by positive matrix factorization (PMF) as the major sources of ambient organic carbon (OC) in Hong Kong. The secondary formation, including OC from SOA, SS, and aging of BB plume, was the leading contributor to OC (51.4 %, 2.15 ± 1.37 μgC m−3) throughout the year. We then applied a tracer-based method (TBM) to estimate the SOA formation from the photo-oxidation of four selected precursors, and monoterpenes SOA was the most abundant. A Kintecus kinetic model was used to examine the formation channels of isoprene SOA, and the aerosol-phase ring-opening reaction of isoprene epoxydiols (IEPOX) was found to be the dominant formation pathway. Consistently, IEPOX tracers contributed 94 % of total GC-MS quantified isoprene SOA tracers. The TBM-estimated SOC (SOCTBM) and PMF-resolved SOC (SOCPMF) showed similar temporal trends; however, SOCTBM only accounted for 26.5 % of SOCPMF, indicating a large fraction of ambient SOA was from other reaction pathways/precursors. Results of Pearson's R and multivariate linear regression analysis showed that NOx processing played a key role in both daytime and nighttime SOA production in the region. SOA formation through nighttime NO3 oxidation of biogenic VOCs, especially monoterpenes, may have made a considerable contribution to the SOA loading in Hong Kong. Moreover, sulfate had a significant positive linear relationship with SOCPMF and SS-related SOC, and particle acidity was significantly correlated with SOC from the aging of BB.
Comparison of Nitrogen Adsorption and Mercury Penetration Results

Mercury penetration pore volumes obtained for four materials (two silicas, alumina and zirconia) agreed closely with the “Gurvitsch Volume” obtained from nitrogen adsorption. The pore volume of the materials used in this study was found in a narrow range of pore sizes that are amenable to both mercury penetration and nitrogen adsorption measurements. The average crystallite sizes calculated from the BET surface area and from TEM measurements agreed closely except for the highest surface area (310 m2/g) material. The surface areas obtained from mercury penetration or the BET method agree closely for the two lower area materials but the mercury penetration is much higher than the nitrogen surface area for the two higher area materials.
Lattice dynamics and elasticity of SrCO3

The lattice dynamics and elasticity of synthetic SrCO3 have been investigated by a combination of ab initio lattice dynamics calculations, microcalorimetry, Raman spectroscopy, X-ray thermal diffuse scattering and high-resolution inelastic X-ray scattering. The results of density functional based calculations were in all cases in good agreement with experiment. For the spectroscopic investigations, peak positions and intensities are well reproduced by the density functional theory model. Experimentally determined intensity distributions in thermal diffuse scattering maps differ from the theoretical distribution only in the (HK0) plane, a fact that is attributed to stacking disorder. As the model is accurate and reliable, the complete elastic stiffness tensor is predicted and, on the basis of these results, the anisotropy of the sound velocities is discussed, also in relation to the anisotropy in other carbonate systems.
SERVICE DISTRIBUTION AND SATISFACTION TOWARD CUSTOMER LOYALTY

Automated Teller Machines (ATMs) are a medium for financial transactions. This study investigates the impact of service distribution and service satisfaction on customer loyalty on ATM usage of the BRI Syariah Sub-Branch Office (KCP) Pamekasan. Data obtained from primary data in ATM user respondents' Form, the data analyzed by SEM with PLS approach. The calculation is carried out in two phases, the first-order phase, and the second-order phase. A validity and reliability test revealed that all three variables were valid and reliable in the first-order phase. At the same time, the second-order phase was used, CFA, and Construct Reliability. Subsequently, an Inner Model test uncovered that the Service Distribution has no significant effect on Customer Loyalty. Contrastively, Service Satisfaction has a significant positive impact on customer loyalty. The goodness of Fit based on R-Square reveals that Service Distribution and Service Satisfaction can explain Service Loyalty at BRI Syariah KCP Pamekasan ATM very well.
Studi Analisis Metode Analisis Sentimen pada YouTube

— Abstract—It cannot be denied that YouTube is now the most popular video sharing website. Opinions written from viewers could be an important asset for the development of a company or YouTubers. In this report, the technique used to analyze those comments is called sentiment analysis or opinion mining techniques. Although this knowledge or technique is not new, it is still important to continue studying this, given the importance of the viewer comments. This paper is the result of a literature review of several studies that have been conducted using various methods in sentiment analysis. The purpose of this analytical study is to obtain a theoretical basis that can support further research by studying the working methods, advantages, and disadvantages of each method. For the reason of having no knowledge on how the method works will affect the results and will be a waste of time. The results of comparisons from research that have been done, showing that the Naïve bayes algorithm has a higher accuracy, then SVM then DT. But this is a preliminary result as no study has used all methods at once in a case.
PERFORMANCE EVALUATION OF A SOLAR POWERED SOLID ADSORPTION REFRIGERATOR UNDER A TROPICAL HUMID CLIMATE

A solar powered intermittent cycle solid adsorption refrigeration system was developed and tested in Akure, Nigeria (Latitude 7.25 o N, Longitude 5.08 o E). The system utilized granular 70% CaCl2 + 10% activated carbon + 20% CaSO4 / NH3 as an adsorbent/adsorbate pair. Initial and final condensate liquid volume, evaporator surface temperature, evaporator water temperature, adsorber plate surface temperature and ambient temperature were measured during adsorption. The adsorber plate surface temperature, condenser water temperature, condensate volume and ambient temperature were recorded during the generation of adsorbate. The data collected were reduced, using appropriate physical equations, to determine the Coefficient of Performance (C.O.P) of the Solar Refrigerator. Ambient temperatures during adsorption and generation ranged over 24 o – 29 o C. Performances of 1123.09 - 1186.2 kJ/m 2 per day of available cooling were obtained. The best cooling obtained was 438.57 kJ/m 2 per day of collector-exposed area. The refrigerator had an overall C.O. P of 0.021 - 0.033 whilst its daily ice production was 0.49 – 0.63 kg/m 2 . The C.O. P and the daily ice production were higher than those reported in the literature for systems with solar collector plates coated with black paint and which utilized CaCl2/NH3 as working pair.
Laser sintering and post-processing of a walnut shell/Co-PES composite

In order to recycle agricultural and forestry waste and reduce the cost of materials, energy consumption and CO2 emission of the laser sintering process, herein, a sustainable and low-cost walnut shell/Co-PES composite (WSPC) is developed as a feedstock for laser sintering technology. Laser sintering experiments are performed to study the formability of WSPC. Through single layer sintering, the optimal mixture ratio of walnut shell powder and Co-PES was determined, which is 1 : 4 by weight. Moreover, the microstructure and dispersity of walnut shell particles in the WSPC prototype were examined via scanning electron microscopy (SEM). In addition, it is shown that the WSPC parts have good forming accuracy and mechanical properties. The tensile strength, bending strength and impact strength of the WSPC parts are 6.0801 MPa, 9.6759 MPa and 0.8102 kJ m−2, respectively. In order to improve the strength of the WSPC parts, their internal pores were filled with infiltrating wax through post-processing. The result shows that the density of the WSPC parts considerably increases and their average tensile strength, bending strength and impact strength increase to 6.5879 MPa, 11.0822 MPa and 0.9504 kJ m−2, respectively.