Miracle-dz/UniFAR-FTUs huggingface dataset:
Disentangled Planning and Control in Vision Based Robotics via Reward Machines

In this work we augment a Deep Q-Learning agent with a Reward Machine (DQRM) to increase speed of learning vision-based policies for robot tasks, and overcome some of the limitations of DQN that prevent it from converging to good-quality policies. A reward machine (RM) is a finite state machine that decomposes a task into a discrete planning graph and equips the agent with a reward function to guide it toward task completion. The reward machine can be used for both reward shaping, and informing the policy what abstract state it is currently at. An abstract state is a high level simplification of the current state, defined in terms of task relevant features. These two supervisory signals of reward shaping and knowledge of current abstract state coming from the reward machine complement each other and can both be used to improve policy performance as demonstrated on several vision based robotic pick and place tasks. Particularly for vision based robotics applications, it is often easier to build a reward machine than to try and get a policy to learn the task without this structure.
TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis

This technique report introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. Compared to most previous publicly available text understanding systems and tools, TexSmart holds some unique features. First, the NER function of TexSmart supports over 1,000 entity types, while most other public tools typically support several to (at most) dozens of entity types. Second, TexSmart introduces new semantic analysis functions like semantic expansion and deep semantic representation, that are absent in most previous systems. Third, a spectrum of algorithms (from very fast algorithms to those that are relatively slow but more accurate) are implemented for one function in TexSmart, to fulfill the requirements of different academic and industrial applications. The adoption of unsupervised or weakly-supervised algorithms is especially emphasized, with the goal of easily updating our models to include fresh data with less human annotation efforts. The main contents of this report include major functions of TexSmart, algorithms for achieving these functions, how to use the TexSmart toolkit and Web APIs, and evaluation results of some key algorithms.
Source Identification for Mixtures of Product Distributions

We give an algorithm for source identification of a mixture of k product distributions on n bits. This is a fundamental problem in machine learning with many applications. Our algorithm identifies the source parameters of an identifiable mixture, given, as input, approximate values of multilinear moments (derived, for instance, from a sufficiently large sample), using 2 2)nO(k) arithmetic operations. Our result is the first explicit bound on the computational complexity of source identification of such mixtures. The running time improves previous results by Feldman, O’Donnell, and Servedio (FOCS 2005) and Chen and Moitra (STOC 2019) that guaranteed only learning the mixture (without parametric identification of the source). Our analysis gives a quantitative version of a qualitative characterization of identifiable sources that is due to Tahmasebi, Motahari, and Maddah-Ali (ISIT 2018).
Relation between finite topological spaces and finitely presentable groups

In this paper it is shown how to construct a finite topological space X for a given finitely presentable group G such that π1(X) ∼= G. Our construction is not optimal in the sense that the cardinality of the space X might not be the smallest possible. Our main result applies to a large class of interesting groups, including all finite groups.
This paper is included in the Proceedings of the 30th USENIX Security Symposium.

In differential privacy (DP), a challenging problem is to generate synthetic datasets that efﬁciently capture the useful information in the private data. The synthetic dataset enables any task to be done without privacy concern and modiﬁcation to existing algorithms. In this paper, we present PrivSyn , the ﬁrst automatic synthetic data generation method that can handle general tabular datasets (with 100 attributes and domain size > 2 500 ). PrivSyn is composed of a new method to automatically and privately identify correlations in the data, and a novel method to generate sample data from a dense graphic model. We extensively evaluate different methods on multiple datasets to demonstrate the performance of our method.
LISPR: An Options Framework for Policy Reuse with Reinforcement Learning

We propose a framework for transferring any existing policy from a potentially unknown source MDP to a target MDP. This framework (1) enables reuse in the target domain of any form of source policy, including classical controllers, heuristic policies, or deep neural network-based policies, (2) attains optimality under suitable theoretical conditions, and (3) guarantees improvement over the source policy in the target MDP. These are achieved by packaging the source policy as a black-box option in the target MDP and providing a theoretically grounded way to learn the option's initiation set through general value functions. Our approach facilitates the learning of new policies by (1) maximizing the target MDP reward with the help of the black-box option, and (2) returning the agent to states in the learned initiation set of the black-box option where it is already optimal. We show that these two variants are equivalent in performance under some conditions. Through a series of experiments in simulated environments, we demonstrate that our framework performs excellently in sparse reward problems given (sub-)optimal source policies and improves upon prior art in transfer methods such as continual learning and progressive networks, which lack our framework's desirable theoretical properties.
Mixed versus Ordinary Micelles in the Dilute Solution of AB and BC Diblock Copolymers

We predict the thermodynamic stability of mixed micelles of various morphologies (spheres, cylinders, lamellae) in the solution of AB and BC diblock copolymers in a selective solvent. The B blocks are assumed to be insoluble and form the core of the micelle, whereas soluble, incompatible (with each other) A and C blocks form the corona. A homogeneous mixing of the A and C blocks in the corona (mixed micelles) is possible if their incompatibility is relatively weak. Otherwise, a mixture of “pure” (ordinary) micelles consisting of either AB or BC copolymers is stable. Phase behavior of the solution, which depends on the relative and overall lengths of the blocks, and on the surface tension coefficients, is analyzed.
Photoionization dynamics of excited molecular states. Photoelectron angular distributions and rotational and vibrational branching ratios for H2 C 1Πu, v=0–4

Photoelectron angular distributions following three photon resonant, four photon (3+1) ionization of H2 via the C 1Πu, v′=0–4 ← X 1Σ+g, v″=0 Q(1) transitions are reported. The observed angular distributions are generally more isotropic for v+≠v′ than for v+=v′. Photoelectron spectra obtained along the polarization axis of the laser following (3+1) ionization via the C 1Πu, v′=4 ← X 1Σ+g, v″=0 R(0) and R(1) transitions are also reported. These spectra are rotationally resolved and exhibit strongly v+‐dependent rotational branching ratios. The comparison of the angular distribution data with available theoretical calculations indicates good agreement for some transitions and poor agreement for others, suggesting the need for substantial progress in understanding the photoionization dynamics of even the simplest excited molecular states.
Efficient Solvers for Nonstandard Models for Flow and Transport in Unsaturated Porous Media

We study several iterative methods for fully coupled flow and reactive transport in porous media. The resulting mathematical model is a coupled, nonlinear evolution system. The flow model component builds on the Richards equation, modified to incorporate nonstandard effects like dynamic capillarity and hysteresis, and a reactive transport equation for the solute. The two model components are strongly coupled. On one hand, the flow affects the concentration of the solute; on the other hand, the surface tension is a function of the solute, which impacts the capillary pressure and, consequently, the flow. After applying an Euler implicit scheme, we consider a set of iterative linearization schemes to solve the resulting nonlinear equations, including both monolithic and two splitting strategies. The latter include a canonical nonlinear splitting and an alternate linearized splitting, which appears to be overall faster in terms of numbers of iterations, based on our numerical studies. The (time discrete) system being nonlinear, we investigate different linearization methods. We consider the linearly convergent L-scheme, which converges unconditionally, and the Newton method, converging quadratically but subject to restrictions on the initial guess. Whenever hysteresis effects are included, the Newton method fails to converge. The L-scheme converges; nevertheless, it may require many iterations. This aspect is improved by using the Anderson acceleration. A thorough comparison of the different solving strategies is presented in five numerical examples, implemented in MRST, a toolbox based on MATLAB.
Development of viscometric methods for studying the interaction of various porphyrins with DNA. Part I: meso-tetra-(4N-hydroxyethylpyridyl) porphyrin and its Ni-, Cu-, Co- and Zn- containing derivatives

The influence of water soluble cationic meso-tetra(4N-hydroxyethyl)pyridyl porphyrin (H2THOEtPyP4) and it's metal complexes with Ni(II), Cu(II), Co(II) and Zn(II) on hydrodynamic and spectral behavior of DNA solutions has been studied by viscometry and UV-vis absorption methods. It was shown that the presence of planar porphyrins, such as H2THOEtPyP4, NiTHOEtPyP4 and CuTHOEtPyP4 leads to an increase in viscosity at relatively small concentrations, and then decreases to stable values. Such behavior corresponds to intercalation of these porphyrins in DNA structure, which results in a decrease of helical twist and lengthening of the DNA molecule. In the case of porphyrins with axial ligands, such as ZnTHOEtPyP4 and CoTHOEtPyP4, the relative viscosity decreases, which is explained by self-stacking of these porphyrins on DNA surface. Calculation and interpretation of binding parameters (Kb and n) demonstrated good agreement of viscometric and spectrophotometric measurements.
DynaSent: A Dynamic Benchmark for Sentiment Analysis

We introduce DynaSent (‘Dynamic Sentiment’), a new English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. DynaSent combines naturally occurring sentences with sentences created using the open-source Dynabench Platform, which facilities human-and-model-in-the-loop dataset creation. DynaSent has a total of 121,634 sentences, each validated by five crowdworkers, and its development and test splits are designed to produce chance performance for even the best models we have been able to develop; when future models solve this task, we will use them to create DynaSent version 2, continuing the dynamic evolution of this benchmark. Here, we report on the dataset creation effort, focusing on the steps we took to increase quality and reduce artifacts. We also present evidence that DynaSent’s Neutral category is more coherent than the comparable category in other benchmarks, and we motivate training models from scratch for each round over successive fine-tuning.
Meta Adaptive Neural Ranking with Contrastive Synthetic Supervision

Neural Information Retrieval (Neu-IR) models have shown their effectiveness and thrive from end-to-end training with massive high-quality relevance labels. Nevertheless, relevance labels at such quantity are luxury and unavailable in many ranking scenarios, for example, in biomedical search. This paper improves Neu-IR in such few-shot search scenarios by meta-adaptively training neural rankers with synthetic weak supervision. We ﬁrst leverage contrastive query generation (ContrastQG) to synthesize more informative queries as in-domain weak relevance labels, and then ﬁl-ter them with meta adaptive learning to rank (MetaLTR) to better generalize neural rankers to the target few-shot domain. Experiments on three different search domains: web, news, and biomedical, demonstrate signiﬁcantly improved few-shot accuracy of neural rankers with our weak supervision framework. The code of this paper will be open-sourced.
Dimensionality-dependent type-II Weyl semimetal state in 
Mo0.25W0.75Te2

Weyl nodes and Fermi arcs in type-II Weyl semimetals (WSMs) have led to lots of exotic transport phenomena. Recently, Mo0.25W0. 75Te2 has been established as a typeII WSM with Weyl points located near Fermi level, which offers an opportunity to study its intriguing band structure by electrical transport measurements. Here, by selecting a special sample with the thickness gradient across two(2D) and threedimensional (3D) regime, we show strong evidences that Mo0.25W0. 75Te2 is a type-II Weyl semimetal by observing the following two dimensionality-dependent transport features: 1) A chiral-anomaly-induced anisotropic magneto-conductivity enhancement, proportional to the square of in-plane magnetic field (  ); 2) An additional quantum oscillation with thickness-dependent phase shift. Our theoretical calculations show that the observed quantum oscillation originates from a Weyl-orbit-like scenario due to the unique band structure of Mo0.25W0. 75Te2. The in situ dimensionality-tuned transport experiment offers a new strategy to search for type-II WSMs.
Towards User Scheduling for 6G: A Fairness-Oriented Scheduler Using Multi-Agent Reinforcement Learning

—User scheduling is a classical problem and key technology in wireless communication, which will still plays an important role in the prospective 6G. There are many sophisticated schedulers that are widely deployed in the base stations, such as Proportional Fairness (PF) and Round-Robin Fashion (RRF). It is known that the Opportunistic (OP) scheduling is the optimal scheduler for maximizing the average user data rate (AUDR) considering the full buffer trafﬁc. But the optimal strategy achieving the highest fairness still remains largely unknown both in the full buffer trafﬁc and the bursty trafﬁc. In this work, we investigate the problem of fairness-oriented user scheduling, especially for the RBG allocation. We build a user scheduler using Multi-Agent Reinforcement Learning (MARL), which conducts distributional optimization to maximize the fairness of the communication system. The agents take the cross-layer information (e.g. RSRP, Buffer size) as state and the RBG allocation result as action , then explore the optimal solution following a well-deﬁned reward function designed for maximizing fairness. Furthermore, we take the 5%-tile user data rate (5TUDR) as the key performance indicator (KPI) of fairness, and compare the performance of MARL scheduling with PF scheduling and RRF scheduling by conducting extensive simulations. And the simulation results show that the proposed MARL scheduling outperforms the traditional schedulers.
Causal Perception in Question-Answering Systems

Root cause analysis is a common data analysis task. While question-answering systems enable people to easily articulate a why question (e.g., why students in Massachusetts have high ACT Math scores on average) and obtain an answer, these systems often produce questionable causal claims. To investigate how such claims might mislead users, we conducted two crowdsourced experiments to study the impact of showing different information on user perceptions of a question-answering system. We found that in a system that occasionally provided unreasonable responses, showing a scatterplot increased the plausibility of unreasonable causal claims. Also, simply warning participants that correlation is not causation seemed to lead participants to accept reasonable causal claims more cautiously. We observed a strong tendency among participants to associate correlation with causation. Yet, the warning appeared to reduce the tendency. Grounded in the findings, we propose ways to reduce the illusion of causality when using question-answering systems.
Multi-Principal Assistance Games: Definition and Collegial Mechanisms

We introduce the concept of a multi-principal assistance game (MPAG), and circumvent an obstacle in social choice theory, Gibbard's theorem, by using a sufficiently collegial preference inference mechanism. In an MPAG, a single agent assists N human principals who may have widely different preferences. MPAGs generalize assistance games, also known as cooperative inverse reinforcement learning games. We analyze in particular a generalization of apprenticeship learning in which the humans first perform some work to obtain utility and demonstrate their preferences, and then the robot acts to further maximize the sum of human payoffs. We show in this setting that if the game is sufficiently collegial, i.e. if the humans are responsible for obtaining a sufficient fraction of the rewards through their own actions, then their preferences are straightforwardly revealed through their work. This revelation mechanism is non-dictatorial, does not limit the possible outcomes to two alternatives, and is dominant-strategy incentive-compatible.
Continuity of Generalized Entropy and Statistical Learning

We study the continuity property of the generalized entropy as a function of the underlying probability distribution, defined with an action space and a loss function, and use this property to answer the basic questions in statistical learning theory: the excess risk analyses for various learning methods. We first derive upper and lower bounds for the entropy difference of two distributions in terms of several commonly used f-divergences, the Wasserstein distance, a distance that depends on the action space and the loss function, and the Bregman divergence generated by the entropy, which also induces bounds in terms of the Euclidean distance between the two distributions. Examples are given along with the discussion of each general result, comparisons are made with the existing entropy difference bounds, and new mutual information upper bounds are derived based on the new results. We then apply the entropy difference bounds to the theory of statistical learning. It is shown that the excess risks in the two popular learning paradigms, the frequentist learning and the Bayesian learning, both can be studied with the continuity property of different forms of the generalized entropy. The analysis is then extended to the continuity of generalized conditional entropy. The extension provides performance bounds for Bayes decision making with mismatched distributions. It also leads to excess risk bounds for a third paradigm of learning, where the decision rule is optimally designed under the projection of the empirical distribution to a predefined family of distributions. We thus establish a unified method of excess risk analysis for the three major paradigms of statistical learning, through the continuity of generalized entropy.
Algorithms for Learning Graphs in Financial Markets

In the past two decades, the field of applied finance has tremendously benefited from graph theory. As a result, novel methods ranging from asset network estimation to hierarchical asset selection and portfolio allocation are now part of practitioners’ toolboxes. In this paper, we investigate the fundamental problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market times series data. In particular, we present natural justifications, supported by empirical evidence, for the usage of the Laplacian matrix as a model for the precision matrix of financial assets, while also establishing a direct link that reveals how Laplacian constraints are coupled to meaningful physical interpretations related to the market index factor and to conditional correlations between stocks. Those interpretations lead to a set of guidelines that practitioners should be aware of when estimating graphs in financial markets. In addition, we design numerical algorithms based on the alternating direction method of multipliers to learn undirected, weighted graphs that take into account stylized facts that are intrinsic to financial data such as heavy tails and modularity. We illustrate how to leverage the learned graphs into practical scenarios such as stock time series clustering and foreign exchange network estimation. The proposed graph learning algorithms outperform the state-of-the-art, benchmark methods in an extensive set of practical experiments, evidencing the advantages of adopting more principled assumptions into the learning framework. Furthermore, we obtain theoretical and empirical convergence results for the proposed algorithms. Along with the developed methodologies for graph learning in financial markets, we release an R package, called fingraph , accommodating the code and data to obtain all the experimental results.
5.6 Cellulose acetate in separation technology

Cellulose acetate has been utilized in a variety of separation and purification processes. In membrane separation, cellulosics, in particular cellulose acetate, have played important roles. Hemodialysis and desalination are the established fields where acetate membranes have been applied. Recently, a new application, that is the purification of drinking water, is growing. The history of cellulosic membranes, preparation of acetate membranes, and applications will be reviewed. Cellulose acetate is also useful in chromatography and related separation technologies. These application will also be reviewed and the relationship between the superstructure of cellulose triacetate and its adsorptive behavior is briefly mentioned.
Cascaded Framework for Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI

Automatic evaluation of myocardium and pathology plays an important role in the quantitative analysis of patients suffering from myocardial infarction. In this paper, we present a cascaded convolutional neural network framework for myocardial infarction segmentation and classification in delayed-enhancement cardiac MRI. Specifically, we first use a 2D U-Net to segment the whole heart, including the left ventricle and the myocardium. Then, we crop the whole heart as a region of interest (ROI). Finally, a new 2D U-Net is used to segment the infraction and no-reflow areas in the whole heart ROI. The segmentation method can be applied to the classification task where the segmentation results with the infraction or no-reflow areas are classified as pathological cases. Our method took second place in the MICCAI 2020 EMIDEC segmentation task with Dice scores of 86.28%, 62.24%, and 77.76% for myocardium, infraction, and no-reflow areas, respectively, and first place in the classification task with an accuracy of 92%.