Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly Phosphonated Polypentafluorostyrene

The synthesis and conductivity of highly phosphonated polypentafluorostyrene are demonstrated. Efficient postphosphonation (90%) was achieved via the classical nucleophilic aromatic substitution (SNAr) Michaelis–Arbuzov reaction of polypentafluorostyrene (PFS) with tris(trimethylsilyl) phosphite. The contrivance is in the cumulative electron-withdrawing effect of the fluorine functions. This simultaneously facilitates the SNAr reaction and enhances acidity of the resulting phosphonic acid. The most important consequence is a substantial increase of the H+ conductivity being the highest one measured on phosphonated polymer: σ = 0.1 S cm–1 at 108 °C, p = 105 Pa water vapor pressure. This value is 4 times higher than the one for poly(vinylphosphonic acid) (σ = 0.025 S cm–1) and higher than Nafion 117 (σ = 0.075 S cm–1) under the same conditions. Additionally, this polymer showed outstanding resistance to oxidative and thermal treatment (Tdecomp = 340 °C at 70% O2 atmosphere). All this makes the phosphonated .
Social Cost Optimization for Prosumer Community with Two Price-Package Incentives in Two-Settlement Based Electricity Market

In this paper, we consider a future electricity market consisting of aggregated energy prosumers, who are equipped with local wind power plants (WPPs) to support (part of) their energy demands and can also trade energy with day-ahead market (DAM) and energy balancing market (EBM). In addition, an energy aggregator (EA) is established, who can provide the trading gateways between prosumers and the markets. The EA is responsible for making pricing strategies on the prosumers to influence their trading behaviours such that the social benefit of the prosumer community is improved. Specifically, two price packages are provided by the EA: wholesale price (WP) package and lump-sum (LS) package, which can be flexibly selected by prosumers based on their own preferences. Analytical energy-trading strategies will be derived for WP prosumers and LS prosumers based on non-cooperative games and Nash resource allocation strategies, respectively. In this work, a social cost optimization problem will be formulated for the EA, where the detailed WP/LS selection plans are unknown in advance. Consequently, a stochastic Stackelberg game between prosumers and the EA is formulated, and a two-level stochastic convex programming algorithm is proposed to minimize the expectation of the social cost. The performance of the proposed algorithm is demonstrated with a two-settlement based market model in the simulation.
Robust Dynamic Network Embedding via Ensembles

Dynamic Network Embedding (DNE) has recently attracted considerable attention due to the advantage of network embedding in various fields and the dynamic nature of many real-world networks. An input dynamic network to DNE is often assumed to have smooth changes over snapshots, which however would not hold for all real-world scenarios. It is natural to ask if existing DNE methods can perform well for an input dynamic network without smooth changes. To quantify it, an index called Degree of Changes (DoCs) is suggested so that the smaller DoCs indicates the smoother changes. Our comparative study shows several DNE methods are not robust enough to different DoCs even if the corresponding input dynamic networks come from the same dataset, which would make these methods unreliable and hard to use for unknown real-world applications. To propose an effective and more robust DNE method, we follow the notion of ensembles where each base learner adopts an incremental Skip-Gram embedding model. To further boost the performance, a simple yet effective strategy is designed to enhance the diversity among base learners at each timestep by capturing different levels of local-global topology. Extensive experiments demonstrate the superior effectiveness and robustness of the proposed method compared to state-of-the-art DNE methods, as well as the benefits of special designs in the proposed method and its scalability.
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display, video, or social. Given a set of audiences or customers (seed users), the audience expansion technique (look-alike modeling) is a promising solution to identify more potential audiences, who are similar to the seed users and likely to finish the business goal of the target campaign. However, look-alike modeling faces two challenges: (1) In practice, a company could run hundreds of marketing campaigns to promote various contents within completely different categories every day, e.g., sports, politics, society. Thus, it is difficult to utilize a common method to expand audiences for all campaigns. (2) The seed set of a certain campaign could only cover limited users. Therefore, a customized approach based on such a seed set is likely to be overfitting. In this paper, to address these challenges, we propose a novel two-stage framework named Meta Hybrid Experts and Critics (MetaHeac) which has been deployed in WeChat Look-alike System. In the offline stage, a general model which can capture the relationships among various tasks is trained from a meta-learning perspective on all existing campaign tasks. In the online stage, for a new campaign, a customized model is learned with the given seed set based on the general model. According to both offline and online experiments, the proposed MetaHeac shows superior effectiveness for both content marketing campaigns in recommender systems and advertising campaigns in advertising platforms. Besides, MetaHeac has been successfully deployed in WeChat for the promotion of both contents and advertisements, leading to great improvement in the quality of marketing. The code has been available at https://github.com/easezyc/MetaHeac.
Exploiting Position Bias for Robust Aspect Sentiment Classification

Aspect sentiment classification (ASC) aims at determining sentiments expressed towards different aspects in a sentence. While state-of-the-art ASC models have achieved remarkable performance, they are recently shown to suffer from the issue of robustness. Particularly in two common scenarios: when domains of test and training data are different (out-of-domain scenario) or test data is adversarially perturbed (adversarial scenario), ASC models may attend to irrelevant words and neglect opinion expressions that truly describe diverse aspects. To tackle the challenge, in this paper, we hypothesize that position bias (i.e., the words closer to a concerning aspect would carry a higher degree of importance) is crucial for building more robust ASC models by reducing the probability of mis-attending. Accordingly, we propose two mechanisms for capturing position bias, namely position-biased weight and position-biased dropout, which can be flexibly injected into existing models to enhance representations for classification. Experiments conducted on out-of-domain and adversarial datasets demonstrate that our proposed approaches largely improve the robustness and effectiveness of current models.
Estimating air quality co-benefits of energy transition using machine learning

Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. Simulating pollution concentration is a crucial step of the estimation, but traditional approaches often rely on complicated chemical transport models that require extensive expertise and computational resources. In this study, we develop a novel and succinct machine learning framework that is able to provide precise and robust annual average fine particle (PM2.5) concentration estimations directly from a high-resolution fossil energy use data set. The accessibility and applicability of this framework show great potentials of machine learning approaches for integrated assessment studies. Applications of the framework with Chinese data reveal highly heterogeneous health benefits of reducing fossil fuel use in different sectors and regions in China with a mean of \$34/tCO2 and a standard deviation of \$84/tCO2. Reducing rural and residential coal use offers the highest co-benefits with a mean of \$360/tCO2. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition towards a carbon-neutral energy system.
Contact Mode Guided Motion Planning for Dexterous Manipulation

—Within the ﬁeld of robotic manipulation, a central goal is to replicate the human ability to manipulate any object in any situation using a sequence of manipulation primitives such as grasping, pushing, inserting, sliding, etc. Conceptually, each manipulation primitive restricts the object and robot to move on a lower-dimensional manifold deﬁned by the primitive’s dynamic equations of motion. Likewise, a manipulation sequence represents a dynamically feasible trajectory that traverses multiple manifolds. To manipulate any object in any situation, robotic systems must include the ability to automatically synthesize manipulation primitives (manifolds) and sequence those primitives into a coherent plan (ﬁnd a path across the manifolds). This paper investigates a principled approach for solving dexterous manipulation planning. This approach is based on rapidly-exploring random trees which use contact modes to guide tree expansion along primitive manifolds [10]. This paper extends this algorithm from 2D domains to 3D domains. We validated our algorithm on a large collection of simulated 3D manipulation tasks 1 . These tasks required our algorithm to sequence between 6-42 manipulation primitives (i.e. distinct contact modes). We believe this work represents an important step towards robotic manipulation capabilities which generalize across objects and environments.
Bangla Natural Language Processing: A Comprehensive Review of Classical, Machine Learning, and Deep Learning Based Methods

The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the speciﬁc domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive study of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough review of 71 BNLP research papers and categorize them into 11 categories, namely Information Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing and Recognition. We study articles published between 1999 to 2021, and 50% of the papers were published after 2015. We discuss Classical, Machine Learning and Deep Learning approaches with different datasets while addressing the limitations and current and future trends of the BNLP.
The CARMENES search for exoplanets around M dwarfs. Spectroscopic orbits of nine M-dwarf multiple systems, including two triples, two brown dwarf candidates, and one close M-dwarf-white dwarf binary

M dwarfs are ideal targets for the search of Earth-size planets in the habitable zone using the radial velocity method, attracting the attention of many ongoing surveys. As a by-product of these surveys, new multiple stellar systems are also found. This is the case also for the CARMENES survey, from which nine new SB2 systems have already been announced. Throughout the five years of the survey, the accumulation of new observations has resulted in the detection of several new multiple stellar systems with long periods and low radial-velocity amplitudes. Here, we newly characterise the spectroscopic orbits and constrain the masses of eight systems and update the properties of a system that we reported earlier. We derive the radial velocities of the stars using two-dimensional cross correlation techniques and template matching. The measurements are modelled to determine the orbital parameters of the systems. We combine CARMENES spectroscopic observations with archival high-resolution spectra from other instruments to increase the time-span of the observations and improve our analysis. When available, we also added archival photometric, astrometric, and adaptive optics imaging data to constrain the rotation periods and absolute masses of the components. We determine the spectroscopic orbits of nine multiple systems, eight of which are presented for the first time. The sample is composed of five SB1s, two SB2s, and two ST3s. The companions of two of the single-line binaries, GJ 3626 and GJ 912, have minimum masses below the stellar boundary and, thus, could be brown dwarfs. We find a new white dwarf in a close binary orbit around the M star GJ 207.1. From a global fit to radial velocities and astrometric measurements, we are able to determine the absolute masses of the components of GJ 282C, which is one of the youngest systems with measured dynamical masses.
Comparing two perfect bases

Comparing two perfect bases Anne Dranowski Doctor of Philosophy Graduate Department of Mathematics University of Toronto 2020 We study a class of varieties which generalize the classical orbital varieties of Joseph. We show that our generalized orbital varieties are the irreducible components of a Mirković–Vybornov slice to a nilpotent orbit, and can be labeled by semistandard Young tableaux. Furthermore, we prove that Mirković–Vilonen cycles are obtained by applying the Mirković– Vybornov isomorphism to generalized orbital varieties and taking a projective closure, refining Mirković and Vybornov’s result. As a consequence, we are able to use the Lusztig datum of a Mirković–Vilonen cycle to determine the tableau labeling the generalized orbital variety which maps to it, and, hence, the ideal of the generalized orbital variety itself. By homogenizing we obtain equations for the cycle we started with, which is useful for computing various equivariant invariants such as equivariant multiplicity and multidegree. As an application, we show that in type A5 the Mirković–Vilonen basis and Lusztig’s dual semicanonical basis are not equal as perfect bases of the coordinate ring of the unipotent subgroup. This is significant because it shows that perfect bases are not unique. Our comparison relies heavily on the theory of measures developed in [BKK19], so we include what we need. Finally, we state a conjectural combinatorial “formula” for the ideal of a generalized orbital variety in terms of its tableau.
System-aware dynamic partitioning for batch and streaming workloads

When processing data streams with highly skewed and nonstationary key distributions, we often observe overloaded partitions when the hash partitioning fails to balance data correctly. To avoid slow tasks that delay the completion of the whole stage of computation, it is necessary to apply adaptive, on-the-fly partitioning that continuously recomputes an optimal partitioner, given the observed key distribution. While such solutions exist for batch processing of static data sets and stateless stream processing, the task is difficult for long-running stateful streaming jobs where key distribution changes over time. Careful checkpointing and operator state migration is necessary to change the partitioning while the operation is running. Our key result is a lightweight on-the-fly Dynamic Repartitioning (DR) module for distributed data processing systems (DDPS), including Apache Spark and Flink, which improves the performance with negligible overhead. DR can adaptively repartition data during execution using our Key Isolator Partitioner (KIP). In our experiments with real workloads and power-law distributions, we reach a speedup of 1.5-6 for a variety of Spark and Flink jobs.
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising

Advertising expenditures have become the major source of revenue for e-commerce platforms. Providing good advertising experiences for advertisers by reducing their costs of trial and error in discovering the optimal advertising strategies is crucial for the long-term prosperity of online advertising. To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives. In this work, we first deploy a prototype of strategy recommender system on Taobao display advertising platform, which indeed increases the advertisers' performance and the platform's revenue, indicating the effectiveness of strategy recommendation for online advertising. We further augment this prototype system by explicitly learning the advertisers' preferences over various advertising performance indicators and then optimization objectives through their adoptions of different recommending advertising strategies. We use contextual bandit algorithms to efficiently learn the advertisers' preferences and maximize the recommendation adoption, simultaneously. Simulation experiments based on Taobao online bidding data show that the designed algorithms can effectively optimize the strategy adoption rate of advertisers.
Non-local Patch-based Low-rank Tensor Ring Completion for Visual Data

—Tensor completion is the problem of estimating the missing entries of a partially observed tensor with a certain low-rank structure. It improves on matrix completion for image and video data by capturing additional structural information intrinsic to such data. Traditional completion algorithms treat the entire visual data as a tensor, which may not always work well especially when camera or object motion exists. In this paper, we develop a novel non-local patch-based tensor ring completion algorithm. In the proposed approach, similar patches are extracted for each reference patch along both the spatial and temporal domains of the visual data. The collected patches are then formed into a high-order tensor and a tensor ring completion algorithm is proposed to recover the completed tensor. A novel interval sampling-based block matching (ISBM) strategy and a hybrid completion strategy are also proposed to improve efﬁciency and accuracy. Further, we develop an online patch-based completion algorithm to deal with streaming video data. An efﬁcient online tensor ring completion algorithm is proposed to reduce the time cost. Extensive experimental results demonstrate the superior performance of the proposed algorithms compared with state-of-the-art methods.
The expected degree distribution in transient duplication divergence models

We study the degree distribution of a randomly chosen vertex in a duplication–divergence graph, under a variety of different generalizations of the basic model of Bhan et al. (2002) and Vázquez et al. (2003). We pay particular attention to what happens when a non-trivial proportion of the vertices have large degrees, establishing a central limit theorem for the logarithm of the degree distribution. Our approach, as in Jordan (2018) and Hermann and Pfaffelhuber (2021), relies heavily on the analysis of related birth–catastrophe processes, and couplings are used to show that a number of different formulations of the process have asymptotically similar expected degree distributions.
Shadows and optical appearance of black bounces illuminated by a thin accretion disk

We study the light rings and shadows of an uniparametric family of spherically symmetric geometries interpolating between the Schwarzschild solution, a regular black hole, and a traversable wormhole, and dubbed as black bounces, all of them sharing the same critical impact parameter. We consider the ray-tracing method in order to study the impact parameter regions corresponding to the direct, lensed, and photon ring emissions, finding a broadening of all these regions for black bounce solutions as compared to the Schwarzschild one. Using this, we determine the optical appearance of black bounces when illuminated by three standard toy models of optically and geometrically thin accretion disks viewed in face-on orientation.
Bimanual Shelf Picking Planner Based on Collapse Prediction

In logistics warehouse, since many objects are randomly stacked on shelves, it becomes difficult for a robot to safely extract one of the objects without other objects falling from the shelf. In previous works, a robot needed to extract the target object after rearranging the neighboring objects. In contrast, humans extract an object from a shelf while supporting other neighboring objects. In this paper, we propose a bimanual manipulation planner based on collapse prediction trained with data generated from a physics simulator, which can safely extract a single object while supporting the other objects. We confirmed that the proposed method achieves more than 80% success rate for safe extraction by real-world experiments using a dual-arm manipulator.
SNIPS: Solving Noisy Inverse Problems Stochastically

In this work we introduce a novel stochastic algorithm dubbed SNIPS, which draws samples from the posterior distribution of any linear inverse problem, where the observation is assumed to be contaminated by additive white Gaussian noise. Our solution incorporates ideas from Langevin dynamics and Newton's method, and exploits a pre-trained minimum mean squared error (MMSE) Gaussian denoiser. The proposed approach relies on an intricate derivation of the posterior score function that includes a singular value decomposition (SVD) of the degradation operator, in order to obtain a tractable iterative algorithm for the desired sampling. Due to its stochasticity, the algorithm can produce multiple high perceptual quality samples for the same noisy observation. We demonstrate the abilities of the proposed paradigm for image deblurring, super-resolution, and compressive sensing. We show that the samples produced are sharp, detailed and consistent with the given measurements, and their diversity exposes the inherent uncertainty in the inverse problem being solved.
Three-dimensional multimodal medical imaging system based on freehand ultrasound and structured light

Abstract. We propose a three-dimensional (3D) multimodal medical imaging system that combines freehand ultrasound and structured light 3D reconstruction in a single coordinate system without requiring registration. To the best of our knowledge, these techniques have not been combined as a multimodal imaging technique. The system complements the internal 3D information acquired with ultrasound with the external surface measured with the structured light technique. Moreover, the ultrasound probe’s optical tracking for pose estimation was implemented based on a convolutional neural network. Experimental results show the system’s high accuracy and reproducibility, as well as its potential for preoperative and intraoperative applications. The experimental multimodal error, or the distance from two surfaces obtained with different modalities, was 0.12 mm. The code is available in a Github repository.