nils-herrmann/clean_scirepeval huggingface dataset:
Discussions of gas power cycle performance analysis method in the course of Engineering Thermodynamics

Engineering Thermodynamics has been the core course of many science and engineering majors at home and abroad, including energy and power, mechanical engineering, civil engineering, aerospace, cryogenic refrigeration, food engineering, chemical engineering, and environmental engineering, among which gas power cycle is one of the important contents. However, many Engineering Thermodynamics textbooks at home and abroad focus only on evaluating the thermal efficiency of gas power cycle, while the important concept of specific cycle net work is ignored. Taking an ideal Otto cycle and an ideal Brayton as examples, the optimum compression ratio (or the pressure ratio) and the maximum specific cycle net work are analyzed and determined. The ideal Otto and the ideal Brayton cycles, and also other gas power cycles, are concluded that the operation under the optimum compression/pressure ratio of the engine, instead of under the higher efficiency, is more economic and more reasonable. We concluded that the two very important concepts, i.e., the maximum specific cycle net work and the optimum compression (or pressure) ratio for the gas power cycles, should be emphasized in the Engineering Thermodynamics teaching process and the latter revised or the newly edited textbooks, in order to better guide the engineering applications. In the end, general T-s diagram is proposed for the gas power cycles.
Multi-Modal Three-Stream Network for Action Recognition

Human action recognition in video is an active yet challenging research topic due to high variation and complexity of data. In this paper, a novel video based action recognition framework utilizing complementary cues is proposed to handle this complex problem. Inspired by the successful two stream networks for action classification, additional pose features are studied and fused to enhance understanding of human action in a more abstract and semantic way. Towards practices, not only ground truth poses but also noisy estimated poses are incorporated in the framework with our proposed pre-processing module. The whole framework and each cue are evaluated on varied benchmarking datasets as JHMDB, sub-JHMDB and Penn Action. Our results outperform state-of-the-art performance on these datasets and show the strength of complementary cues.
The Adams–Hilton model and the group of self-homotopy equivalences of a simply connected CW-complex

Let $R$ be a principal ideal domain (PID). For a simply connected CW-complex $X$ of dimension $n$, let $Y$ be a space obtained by attaching cells of dimension $q$ to $X$, $q>n$, and let $A(Y)$ denote an Adams-Hilton model of $Y$. If $\mathcal E(A(Y))$ denotes the group of homotopy self-equivalences of $A(Y)$ and $\mathcal E_{*}(A(Y))$ its subgroup of the elements inducing the identity on $H_{*}( Y,R)$, then we construct two short exact sequences: $$\underset{i}{\oplus}\,H_{q}(\Omega X,R)\rightarrowtail \mathcal{E}(A(Y))\overset{}{ 
\twoheadrightarrow}\Gamma^{q}_{n}\,\,\,\,\,\,\,\,\,\,\,\,,\,\,\,\,\,\,\,\,\,\,\,\,\underset{i}{\oplus}\,H_{q}(\Omega X,R) \rightarrowtail \E_{*}(A(Y))\overset{}{ 
\twoheadrightarrow}\Pi^{q}_{n} $$ where $i=\mathrm{rank} \,H_{q}(Y,X;R)$, $\Gamma^{q}_{n}$ is a subgroup of $\aut(\mathrm{Hom}_{}(H_{q}( Y,X;R))\times \E(A(X))$ and $\Pi^{q}_{n}$ is a subgroup of $\mathcal E_{*}(A(X))$.
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks

Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification. However, these models lack a mechanism to account for relevant syntactical constraints and long-range word dependencies, and hence may mistakenly recognize syntactically irrelevant contextual words as clues for judging aspect sentiment. To tackle this problem, we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspect-specific sentiment classification framework is raised. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness to a range of state-of-the-art models, and further demonstrate that both syntactical information and long-range word dependencies are properly captured by the graph convolution structure.
An Experimental Study of Structural Diversity in Social Networks

Several recent studies of online social networking platforms have found that adoption rates and engagement levels are positively correlated with structural diversity, the degree of heterogeneity among an individual's contacts as measured by network ties. One common theory for this observation is that structural diversity increases utility, in part because there is value to interacting with people from different network components on the same platform. While compelling, evidence for this causal theory comes from observational studies, making it difficult to rule out non-causal explanations. We investigate the role of structural diversity on retention by conducting a large-scale randomized controlled study on the Twitter platform. We first show that structural diversity correlates with user retention on Twitter, corroborating results from past observational studies. We then exogenously vary structural diversity by altering the set of network recommendations new users see when joining the platform; we confirm that this design induces the desired changes to network topology. We find, however, that low, medium, and high structural diversity treatment groups in our experiment have comparable retention rates. Thus, at least in this case, the observed correlation between structural diversity and retention does not appear to result from a causal relationship, challenging theories based on past observational studies.
Almost all orbits of the Collatz map attain almost bounded values

Define the Collatz map 
${\operatorname {Col}} \colon \mathbb {N}+1 \to \mathbb {N}+1$
 on the positive integers 
$\mathbb {N}+1 = \{1,2,3,\dots \}$
 by setting 
${\operatorname {Col}}(N)$
 equal to 
$3N+1$
 when N is odd and 
$N/2$
 when N is even, and let 
${\operatorname {Col}}_{\min }(N) := \inf _{n \in \mathbb {N}} {\operatorname {Col}}^n(N)$
 denote the minimal element of the Collatz orbit 
$N, {\operatorname {Col}}(N), {\operatorname {Col}}^2(N), \dots $
 . The infamous Collatz conjecture asserts that 
${\operatorname {Col}}_{\min }(N)=1$
 for all 
$N \in \mathbb {N}+1$
 . Previously, it was shown by Korec that for any 
$\theta> \frac {\log 3}{\log 4} \approx 0.7924$
 , one has 
${\operatorname {Col}}_{\min }(N) \leq N^\theta $
 for almost all 
$N \in \mathbb {N}+1$
 (in the sense of natural density). In this paper, we show that for any function 
$f \colon \mathbb {N}+1 \to \mathbb {R}$
 with 
$\lim _{N \to \infty } f(N)=+\infty $
 , one has 
${\operatorname {Col}}_{\min }(N) \leq f(N)$
 for almost all 
$N \in \mathbb {N}+1$
 (in the sense of logarithmic density). Our proof proceeds by establishing a stabilisation property for a certain first passage random variable associated with the Collatz iteration (or more precisely, the closely related Syracuse iteration), which in turn follows from estimation of the characteristic function of a certain skew random walk on a 
$3$
 -adic cyclic group 
$\mathbb {Z}/3^n\mathbb {Z}$
 at high frequencies. This estimation is achieved by studying how a certain two-dimensional renewal process interacts with a union of triangles associated to a given frequency.
A New Analysis of Differential Privacy's Generalization Guarantees

We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and gives structural insights that we expect will be useful elsewhere. We show: 1) that differential privacy ensures that the expectation of any query on the posterior distribution on datasets induced by the transcript of the interaction is close to its true value on the data distribution, and 2) sample accuracy on its own ensures that any query answer produced by the mechanism is close to its posterior expectation with high probability. This second claim follows from a thought experiment in which we imagine that the dataset is resampled from the posterior distribution after the mechanism has committed to its answers. The transfer theorem then follows by summing these two bounds, and in particular, avoids the "monitor argument" used to derive high probability bounds in prior work. An upshot of our new proof technique is that the concrete bounds we obtain are substantially better than the best previously known bounds, even though the improvements are in the constants, rather than the asymptotics (which are known to be tight). As we show, our new bounds outperform the naive "sample-splitting" baseline at dramatically smaller dataset sizes compared to the previous state of the art, bringing techniques from this literature closer to practicality.
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning

Sensational headlines are headlines that capture people’s attention and generate reader interest. Conventional abstractive headline generation methods, unlike human writers, do not optimize for maximal reader attention. In this paper, we propose a model that generates sensational headlines without labeled data. We first train a sensationalism scorer by classifying online headlines with many comments (“clickbait”) against a baseline of headlines generated from a summarization model. The score from the sensationalism scorer is used as the reward for a reinforcement learner. However, maximizing the noisy sensationalism reward will generate unnatural phrases instead of sensational headlines. To effectively leverage this noisy reward, we propose a novel loss function, Auto-tuned Reinforcement Learning (ARL), to dynamically balance reinforcement learning (RL) with maximum likelihood estimation (MLE). Human evaluation shows that 60.8% of samples generated by our model are sensational, which is significantly better than the Pointer-Gen baseline and other RL models.
Variational control forces for enhanced sampling of nonequilibrium molecular dynamics simulations.

We introduce a variational algorithm to estimate the likelihood of a rare event within a nonequilibrium molecular dynamics simulation through the evaluation of an optimal control force. Optimization of a control force within a chosen basis is made possible by explicit forms for the gradients of a cost function in terms of the susceptibility of driven trajectories to changes in variational parameters. We consider probabilities of time-integrated dynamical observables as characterized by their large deviation functions and find that in many cases, the variational estimate is quantitatively accurate. Additionally, we provide expressions to exactly correct the variational estimate that can be evaluated directly. We benchmark this algorithm against the numerically exact solution of a model of a driven particle in a periodic potential, where the control force can be represented with a complete basis. We then demonstrate the utility of the algorithm in a model of repulsive particles on a line, which undergo a dynamical phase transition, resulting in singular changes to the form of the optimal control force. In both systems, we find fast convergence and are able to evaluate large deviation functions with significant increases in statistical efficiency over alternative Monte Carlo approaches.
DublinCity: Annotated LiDAR Point Cloud and its Applications

Scene understanding of full-scale 3D models of an urban area remains a challenging task. While advanced computer vision techniques offer cost-effective approaches to analyse 3D urban elements, a precise and densely labelled dataset is quintessential. The paper presents the first-ever labelled dataset for a highly dense Aerial Laser Scanning (ALS) point cloud at city-scale. This work introduces a novel benchmark dataset that includes a manually annotated point cloud for over 260 million laser scanning points into 100'000 (approx.) assets from Dublin LiDAR point cloud  in 2015. Objects are labelled into 13 classes using hierarchical levels of detail from large (i.e., building, vegetation and ground) to refined (i.e., window, door and tree) elements. To validate the performance of our dataset, two different applications are showcased. Firstly, the labelled point cloud is employed for training Convolutional Neural Networks (CNNs) to classify urban elements. The dataset is tested on the well-known state-of-the-art CNNs (i.e., PointNet, PointNet++ and So-Net). Secondly, the complete ALS dataset is applied as detailed ground truth for city-scale image-based 3D reconstruction.
Transfer Reward Learning for Policy Gradient-Based Text Generation

Task-specific scores are often used to optimize for and evaluate the performance of conditional text generation systems. However, such scores are non-differentiable and cannot be used in the standard supervised learning paradigm. Hence, policy gradient methods are used since the gradient can be computed without requiring a differentiable objective. 
However, we argue that current n-gram overlap based measures that are used as rewards can be improved by using model-based rewards transferred from tasks that directly compare the similarity of sentence pairs. These reward models either output a score of sentence-level syntactic and semantic similarity between entire predicted and target sentences as the expected return, or for intermediate phrases as segmented accumulative rewards. 
We demonstrate that using a \textit{Transferable Reward Learner} leads to improved results on semantical evaluation measures in policy-gradient models for image captioning tasks. Our InferSent actor-critic model improves over a BLEU trained actor-critic model on MSCOCO when evaluated on a Word Mover's Distance similarity measure by 6.97 points, also improving on a Sliding Window Cosine Similarity measure by 10.48 points. Similar performance improvements are also obtained on the smaller Flickr-30k dataset, demonstrating the general applicability of the proposed transfer learning method.
Theory of Optimal Bayesian Feature Filtering

Optimal Bayesian feature filtering (OBF) is a supervised screening method designed for biomarker discovery. In this article, we prove two major theoretical properties of OBF. First, optimal Bayesian feature selection under a general family of Bayesian models reduces to filtering if and only if the underlying Bayesian model assumes all features are mutually independent. Therefore, OBF is optimal if and only if one assumes all features are mutually independent, and OBF is the only filter method that is optimal under at least one model in the general Bayesian framework. Second, OBF under independent Gaussian models is consistent under very mild conditions, including cases where the data is non-Gaussian with correlated features. This result provides conditions where OBF is guaranteed to identify the correct feature set given enough data, and it justifies the use of OBF in non-design settings where its assumptions are invalid.
Real-time and interactive tools for vocal training based on an analytic signal with a cosine series envelope

We introduce real-time and interactive tools for assisting vocal training. In this presentation, we demonstrate mainly a tool based on real-time visualizer of fundamental frequency candidates to provide information-rich feedback to learners. The visualizer uses an efficient algorithm using analytic signals for deriving phase-based attributes. We start using these tools in vocal training for assisting learners to acquire the awareness of appropriate vocalization. The first author made the MATLAB implementation of the tools open-source. The code and associated video materials are accessible in the first author's GitHub repository.
The Future of Misinformation Detection: New Perspectives and Trends

The massive spread of misinformation in social networks has become a global risk, implicitly influencing public opinion and threatening social/political development. Misinformation detection (MID) has thus become a surging research topic in recent years. As a promising and rapid developing research field, we find that many efforts have been paid to new research problems and approaches of MID. Therefore, it is necessary to give a comprehensive review of the new research trends of MID. We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection. We further investigate the extraction and usage of various crowd intelligence in MID, which paves a promising way to tackle MID challenges. Finally, we give our own views on the open issues and future research directions of MID, such as model adaptivity/generality to new events, embracing of novel machine learning models, explanatory detection models, and so on.
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing

Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable. A thorough grasp of the elusive shape requires sufficiently contextual semantic information, yet few works devote to this. Here we propose DensePoint, a general architecture to learn densely contextual representation for point cloud processing. Technically, it extends regular grid CNN to irregular point configuration by generalizing a convolution operator, which holds the permutation invariance of points, and achieves efficient inductive learning of local patterns. Architecturally, it finds inspiration from dense connection mode, to repeatedly aggregate multi-level and multi-scale semantics in a deep hierarchy. As a result, densely contextual information along with rich semantics, can be acquired by DensePoint in an organic manner, making it highly effective. Extensive experiments on challenging benchmarks across four tasks, as well as thorough model analysis, verify DensePoint achieves the state of the arts.
Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases

State-of-the-art models often make use of superficial patterns in the data that do not generalize well to out-of-domain or adversarial settings. For example, textual entailment models often learn that particular key words imply entailment, irrespective of context, and visual question answering models learn to predict prototypical answers, without considering evidence in the image. In this paper, we show that if we have prior knowledge of such biases, we can train a model to be more robust to domain shift. Our method has two stages: we (1) train a naive model that makes predictions exclusively based on dataset biases, and (2) train a robust model as part of an ensemble with the naive one in order to encourage it to focus on other patterns in the data that are more likely to generalize. Experiments on five datasets with out-of-domain test sets show significantly improved robustness in all settings, including a 12 point gain on a changing priors visual question answering dataset and a 9 point gain on an adversarial question answering test set.
Graphical Frobenius representations of non-abelian groups

A group $G$ has a Frobenius graphical representation (GFR) if there is a simple graph $\varGamma$ whose full automorphism group is isomorphic to $G$ and it acts on vertices as a Frobenius group. In particular, any group $G$ with GFR is a Frobenius group and $\varGamma$ is a Cayley graph. The existence of an infinite family of groups with GFR whose Frobenius kernel is a non-abelian $2$-group has been an open question. In this paper, we give a positive answer by showing that the Higman group $A(f,q_0)$ has a GFR for an infinite sequence of $f$ and $q_0$.
Physics without determinism: Alternative interpretations of classical physics

Classical physics is generally regarded as deterministic, as opposed to quantum mechanics that is considered the first theory to have introduced genuine indeterminism into physics. We challenge this view by arguing that the alleged determinism of classical physics relies on the tacit, metaphysical assumption that there exists an actual value of every physical quantity, with its infinite predetermined digits (which we name principle of infinite precision). Building on recent information-theoretic arguments showing that the principle of infinite precision (which translates into the attribution of a physical meaning to mathematical real numbers) leads to unphysical consequences, we consider possible alternative indeterministic interpretations of classical physics. We also link those to well-known interpretations of quantum mechanics. In particular, we propose a model of classical indeterminism based on finite information quantities (FIQs).Moreover, we discuss the perspectives that an indeterministic physics could open (such as strong emergence), as well as some potential problematic issues. Finally, we make evident that any indeterministic interpretation of physics would have to deal with the problem of explaining how the indeterminate values become determinate, a problem known in the context of quantum mechanics as (part of) the “quantum measurement problem.” We discuss some similarities between the classical and the quantum measurement problems, and propose ideas for possible solutions (e.g., “collapse models” and “top-down causation”).
Accurate Biomolecular Simulations Account for Electronic Polarization

In this perspective, we discuss where and how accounting for electronic many-body polarization affects the accuracy of classical molecular dynamics simulations of biomolecules. While the effects of electronic polarization are highly pronounced for molecules with an opposite total charge, they are also non-negligible for interactions with overall neutral molecules. For instance, neglecting these effects in important biomolecules like amino acids and phospholipids affects the structure of proteins and membranes having a large impact on interpreting experimental data as well as building coarse grained models. With the combined advances in theory, algorithms and computational power it is currently realistic to perform simulations with explicit polarizable dipoles on systems with relevant sizes and complexity. Alternatively, the effects of electronic polarization can also be included at zero additional computational cost compared to standard fixed-charge force fields using the electronic continuum correction, as was recently demonstrated for several classes of biomolecules.
Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information

This paper presents an end-to-end radar odometry system which delivers robust, real-time pose estimates based on a learned embedding space free of sensing artefacts and distractor objects. The system deploys a fully differentiable, correlation-based radar matching approach. This provides the same level of interpretability as established scan-matching methods and allows for a principled derivation of uncertainty estimates. The system is trained in a (self-)supervised way using only previously obtained pose information as a training signal. Using 280km of urban driving data, we demonstrate that our approach outperforms the previous state-of-the-art in radar odometry by reducing errors by up 68% whilst running an order of magnitude faster.