Miracle-dz/UniFAR-FTUs huggingface dataset:
Ju l 2 02 1 Error Processing of Sparse Identification of Nonlinear Dynamical Systems via L ∞ Approximation

Sparse identification of nonlinear dynamical systems(SINDy) is a recently presented framework in the reverse engineering field. It soon gains general interests due to its interpretability and efficiency. Error processing, as an important issue in the SINDy framework, yet remains to be an open problem. To date, literature about error processing focuses on data processing methods which aim to improve the accuracy of data. However, the relationship between data and the identification framework is largely ignored. In this paper, error processing is studied from an optimization perspective. In detail, L∞ approximation is introduced to the objective function in SINDy framework in place of the former L2 approximation. This is especially appropriate for dealing with the derivative approximation error in SINDy because the derivative approximation error has no exact distribution. To verify the effectiveness of L∞ approximation, identification scenarios with different types of derivative approximation error are tested. The results indicate that L∞ approximation could become an alternative of L2 approximation especially when lacking prior knowledge of derivative approximation error. The performances of L∞ approximation and L2 approximation are evaluated in the cases where the measurement noise of system state is considered. Experimental results show that L∞ approximation has equal performance compared to L2 approximation under the assumption of Gaussian measurement noise, which is promising in applications.
Rating Facts under Coarse-to-fine Regimes

The rise of manipulating fake news as a political weapon has become a global concern and highlighted the incapability of manually fact checking against rapidly produced fake news. Thus, statistical approaches are required if we are to address this problem efficiently. The shortage of publicly available datasets is one major bottleneck of automated fact checking. To remedy this, we collected 24K manually rated statements from PolitiFact. The class values exhibit a natural order with respect to truthfulness as shown in Table 1. Thus, our task represents a twist from standard classification, due to the various degrees of similarity between classes. To investigate this, we defined coarse-to-fine classification regimes, which presents new challenge for classification. To address this, we propose BERT-based models. After training, class similarity is sensible over the multi-class datasets, especially in the fine-grained one. Under all the regimes, BERT achieves state of the art, while the additional layers provide insignificant improvement.
Primitive Rateless Codes

In this paper, we propose primitive rateless (PR) codes. A PR code is characterized by the message length and a primitive polynomial over <inline-formula> <tex-math notation="LaTeX">$\mathbf {GF}(2)$ </tex-math></inline-formula>, which can generate a potentially limitless number of coded symbols. We show that codewords of a PR code truncated at any arbitrary length can be represented as subsequences of a maximum-length sequence (<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-sequence). We characterize the Hamming weight distribution of PR codes and their duals and show that for a properly chosen primitive polynomial, the Hamming weight distribution of the PR code can be well approximated by the truncated binomial distribution. We further find a lower bound on the minimum Hamming weight of PR codes and show that there always exists a PR code that can meet this bound for any desired codeword length. We provide a list of primitive polynomials for message lengths up to 40 and show that the respective PR codes closely meet the Gilbert-Varshamov bound at various rates. Simulation results show that PR codes can achieve similar block error rates as their BCH counterparts at various signal-to-noise ratios (SNRs) and code rates. PR codes are rate-compatible and can generate as many coded symbols as required; thus, demonstrating a truly rateless performance.
Encoding Compositionality in Classical Planning Solutions

Classical AI planners provide solutions to planning problems in the form of long and opaque text outputs. To aid in the understanding transferability of planning solutions, it is necessary to have a rich and comprehensible representation for both human and computers beyond the current line-by-line text notation. In particular, it is desirable to encode the trace of literals throughout the plan to capture the dependencies between actions selected. The approach of this paper is to view the actions as maps between literals and the selected plan as a composition of those maps. The mathematical theory, called category theory, provides the relevant structures for capturing maps, their compositions, and maps between compositions. We employ this theory to propose an algorithm agnostic, model-based representation for domains, problems, and plans expressed in the commonly used planning description language, PDDL. This category theoretic representation is accompanied by a graphical syntax in addition to a linear notation, similar to algebraic expressions, that can be used to infer literals used at every step of the plan. This provides the appropriate constructive abstraction and facilitates comprehension for human operators. In this paper, we demonstrate this on a plan within the Blocksworld domain.
Secondary Ion Emission from Water Ice at 10–130 K Induced by MeV N2+ Ions

Secondary ion emission from vapor-deposited water ice has been investigated in exploring the influence of phase transitions on the desorption yield of H2O ions, particularly, the hydronium H3O+ and the cluster ions (H2O)nH3O+. For 1.5 MeV N2+ ion bombardment on water ice at a temperature of ∼80 K, the sum of the yields of H3O+ and of the cluster ions represents 80% of the positive ion yield (i.e., 0.23 ions/impact) and 0.33% of the total H2O molecular sputtering yield. When the beam energy of N2+ is varied between 0.5 and 1.7 MeV, the shape of the cluster mass distributions does not change and the total cluster ion yield scales with the third power of the electronic energy loss. Time-of-flight mass spectra were taken during the warming up of the ice from 10 to 216 K. Up to 30 K, the cluster ion yields are approximately constant, whereas between 30 and 75 K, they decrease gradually by a factor 6.5 and remain constant until 130 K. This is in analogy with modifications in the ice structure: in the 38–78 K re.
On Local Minimizers of Quadratically Constrained Nonconvex Homogeneous Quadratic Optimization with at Most Two Constraints

We study nonconvex homogeneous quadratically constrained quadratic optimization with one or two constraints, denoted by (QQ1) and (QQ2), respectively. (QQ2) contains (QQ1), trust region subproblem (TRS) and ellipsoid regularized total least squares problem as special cases. It is known that there is a necessary and sufficient optimality condition for the global minimizer of (QQ2). In this paper, we first show that any local minimizer of (QQ1) is globally optimal. Unlike its special case (TRS) with at most one local non-global minimizer, (QQ2) may have infinitely many local non-global minimizers. At any local non-global minimizer of (QQ2), both linearly independent constraint qualification and strict complementary condition hold, and the Hessian of the Lagrangian has exactly one negative eigenvalue. As a main contribution, we prove that the standard second-order sufficient optimality condition for any strict local non-global minimizer of (QQ2) remains necessary. Applications and the impossibility of further extension are discussed.
Linking Health News to Research Literature

Accurately linking news articles to scientific research works is a critical component in a number of applications, such as measuring the social impact of a research work and detecting inaccuracies or distortions in science news. Although the lack of links between news and literature has been a challenge in these applications, it is a relatively unexplored research problem. In this paper we designed and evaluated a new approach that consists of (1) augmenting latest named-entity recognition techniques to extract various metadata, and (2) designing a new elastic search engine that can facilitate the use of enriched metadata queries. To evaluate our approach, we constructed two datasets of paired news articles and research papers: one is used for training models to extract metadata, and the other for evaluation. Our experiments showed that the new approach performed significantly better than a baseline approach used by altmetric.com (0.89 vs 0.32 in terms of top-1 accuracy). To further demonstrate the effectiveness of the approach, we also conducted a study on 37,600 health-related press releases published on EurekAlert! , which showed that our approach was able to identify the corresponding research papers with a top-1 accuracy of at least 0.97.
Heterogeneous Effects in the Built Environment

We present an approach to estimate distance-dependent heterogeneous associations between point-referenced exposures to built environment characteristics and health outcomes. By estimating associations that depend non-linearly on distance between subjects and point-referenced exposures, this method addresses the modifiable area-unit problem that is pervasive in the built environment literature. Additionally, by estimating heterogeneous effects, the method also addresses the uncertain geographic context problem. The key innovation of our method is to combine ideas from the non-parametric function estimation literature and the Bayesian Dirichlet process literature. The former is used to estimate nonlinear associations between subject's outcomes and proximate built environment features, and the latter identifies clusters within the population that have different effects. We study this method in simulations and apply our model to study heterogeneity in the association between fast food restaurant availability and weight status of children attending schools in Los Angeles, California.
The Quantum Condition Space

The fundamental properties of quantum physics are exploited to evaluate event probabilities with projection measurements. Next, to study what events can be specified by quantum methods, the concept of the condition space is introduced, which is found to be the dual space of the classical outcome space of bit strings. Like the classical outcome space generates the quantum state space, the condition space generates the quantum condition space being the central idea of this work. The quantum condition space permits the existence of entangled conditions having no classical equivalent. In addition, the quantum condition space is related to the quantum state space by a Fourier transform guaranteed by the Pontryagin duality, and therefore an entropic uncertainty principle can be defined. The quantum condition space offers a novel perspective of understanding quantum states with the duality picture. Furthermore, the quantum conditions have physical meanings and realizations of their own and thus may be studied for purposes beyond the original motivation of characterizing events for probability evaluation. Finally, the relation between the condition space and quantum circuits provides insights into how quantum states are collectively modified by quantum gates, which may lead to deeper understanding of the complexity of quantum circuits.
Self-Determined Reciprocal Recommender Systemwith Strong Privacy Guarantees

Recommender systems are widely used. Usually, recommender systems are based on a centralized client-server architecture. However, this approach implies drawbacks regarding the privacy of users. In this paper, we propose a distributed reciprocal recommender system with strong, self-determined privacy guarantees, i.e., local differential privacy. More precisely, users randomize their profiles locally and exchange them via a peer-to-peer network. Recommendations are then computed and ranked locally by estimating similarities between profiles. We evaluate recommendation accuracy of a job recommender system and demonstrate that our method provides acceptable utility under strong privacy requirements.
Regional heterogeneity of adventitial elastin in the arteriolar wall reflects differences in the mechanical environment

The mechanical properties of arteries are determined, in large part, by the extracellular matrix (ECM) composition and organization. Vessel stiffness and elasticity, vascular smooth muscle cell (SMCs) location, and stability are all influenced by the ECM proteins. The proteins that make up the ECM include elastin, collagen, and a number of glycoproteins: fibronectin, tenascin, vitronectin, and laminin. All of these proteins provide the vessel with mechanical support and functional strength. Elastic layers have been observed in throughout the vessel wall, specifically in the adventitia, external elastic lamina (EEL), and internal elastic lamina (IEL). The two elastic laminas act to constrain and presumably protect vascular SMCs from excessive mechanical forces. Each section of elastin has a distinct arrangement which hypothetically provide the vessel with a specific type of support. The ECM protein elastin is the most hydrophobic of all known ECM proteins, which stems from its composition. This composition also provides a high degree of cross-linking, which leads to the most important property elastin confers on the vessel, elasticity. In the ECM, elastic fibers are assembled on microfibrils and these longitudinal fibers are especially common in tissues that undergo repetitive distention or passive lengthening movements. For example, skeletal muscle and mesenteric arteries are known
Safer Reinforcement Learning through Transferable Instinct Networks

Random exploration is one of the main mechanisms through which reinforcement learning (RL) finds well-performing policies. However, it can lead to undesirable or catastrophic outcomes when learning online in safety-critical environments. In fact, safe learning is one of the major obstacles towards real-world agents that can learn during deployment. One way of ensuring that agents respect hard limitations is to explicitly configure boundaries in which they can operate. While this might work in some cases, we do not always have clear a-priori information which states and actions can lead dangerously close to hazardous states. Here, we present an approach where an additional policy can override the main policy and offer a safer alternative action. In our instinct-regulated RL (IR^2L) approach, an"instinctual"network is trained to recognize undesirable situations, while guarding the learning policy against entering them. The instinct network is pre-trained on a single task where it is safe to make mistakes, and transferred to environments in which learning a new task safely is critical. We demonstrate IR^2L in the OpenAI Safety gym domain, in which it receives a significantly lower number of safety violations during training than a baseline RL approach while reaching similar task performance.
Noisy searching: simple, fast and correct

This work considers the problem of the noisy binary search in a sorted array. The noise is modeled by a parameter $p$ that dictates that a comparison can be incorrect with probability $p$, independently of other queries. We state two types of upper bounds on the number of queries: the worst-case and expected query complexity scenarios. The bounds improve the ones known to date, i.e., our algorithms require fewer queries. Additionally, they have simpler statements, and work for the full range of parameters. All query complexities for the expected query scenarios are tight up to lower order terms. For the problem where target prior is uniform over all possible inputs, we provide algorithm with expected complexity upperbounded by $(\log_2 n + \log_2 \delta^{-1} + 3)/I(p)$, where $n$ is the domain size, $0\le p<1/2$ is the noise ratio, and $\delta>0$ is the failure probability, and $I(p)$ is the information gain function. As a side-effect, we close some correctness issues regarding previous work. Also, en route, we obtain new and improved query complexities for the search generalized to arbitrary graphs. This paper continues and improves upon the lines of research of Burnashev-Zigangirov [Prob. Per. Informatsii, 1974], Ben-Or and Hassidim [FOCS 2008], Gu and Xu [STOC 2023], and Emamjomeh-Zadeh et al. [STOC 2016], Dereniowski et al. [SOSA@SODA 2019].
A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization

Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. Reinforcement learning (RL) is a trending data-driven approach for adaptive traffic signal control in complex urban traffic networks. Although the development of deep neural networks (DNN) further enhances its learning capability, there are still some challenges in applying deep RLs to transportation networks with multiple signalized intersections, including non-stationarity environment, exploration-exploitation dilemma, multi-agent training schemes, continuous action spaces, etc. In order to address these issues, this paper first proposes a multi-agent deep deterministic policy gradient (MADDPG) method by extending the actor-critic policy gradient algorithms. MADDPG has a centralized learning and decentralized execution paradigm in which critics use additional information to streamline the training process, while actors act on their own local observations. The model is evaluated via simulation on the Simulation of Urban MObility (SUMO) platform. Model comparison results show the efficiency of the proposed algorithm in controlling traffic lights.
The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain periodicity estimation

Many speech and music analysis and processing schemes rely on an estimate of the fundamental frequency f0 of periodic signal components. Most established schemes apply rather unspecific signal models such as sinusoidal models to the estimation problem, which may limit time resolution and estimation accuracy. This study proposes a novel time-domain locked-loop algorithm with low computational effort and low memory footprint for f0 estimation. The loop control signal is directly derived from the input time signal, using a harmonic signal model. Theoretically, this allows for a noise-robust and rapid f0 estimation for periodic signals of arbitrary waveform, and without the requirement of a prior frequency analysis. Several simulations with short signals employing different types of periodicity and with added wide-band noise were performed to demonstrate and evaluate the basic properties of the proposed algorithm. Depending on the Signal-to-Noise Ratio (SNR), the estimator was found to converge within 3–4 signal repetitions, even at SNR close to or below 0 dB. Furthermore, it was found to follow fundamental frequency sweeps with a delay of less than one period and to track all tones of a three-tone musical chord signal simultaneously. Quasi-periodic sounds with shifted harmonics as well as signals with stochastic periodicity were robustly tracked. Mean and standard deviation of the estimation error, i.e., the difference between true and estimated f0, were at or below 1 Hz in most cases. The results suggest that the proposed algorithm may be applicable to low-delay speech and music analysis and processing.
Compound Sequential Change-point Detection in Parallel Data Streams

We consider sequential change-point detection in parallel data streams, where each stream has its own change point. Once a change is detected in a data stream, this stream is deactivated permanently. The goal is to maximize the normal operation of the pre-change streams, while controlling the proportion of post-change streams among the active streams at all time points. Taking a Bayesian formulation, we develop a compound decision framework for this problem. A procedure is proposed that is uniformly optimal among all sequential procedures which control the expected proportion of postchange streams at all time points. We also investigate the asymptotic behavior of the proposed method when the number of data streams grows large. Numerical examples are provided to illustrate the use and performance of the proposed method.
Exogenous melatonin treatment in the postharvest storage of pitaya fruits delays senescence and regulates reactive oxygen species metabolism

Abstract Pitaya fruits have high senescence rates throughout their postharvest storage period. Additionally, studies have confirmed that melatonin plays a regulatory role in plant senescence. However, the involvement of melatonin in the postharvest senescence of fruits remains unclear. In this study, two cultivars of pitaya fruit, ‘Zihonglong’ and ‘Jinghonglong’, were treated with melatonin and then evaluated for characteristics of senescence while in storage for 10 days. The results showed that melatonin treatment delayed fruit senescence in both pitaya cultivars, as indicated by the inhibition of weight loss, decay incidence, relative membrane permeability, and malondialdehyde (MDA) content, as well as the maintenance of the total soluble solids and ascorbic acid contents and the reduced respiration intensity. In addition, melatonin treatment reduced the O2·- production rates, H2O2 contents, and lipoxygenase activities but enhanced the activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX) in both pitaya cultivars. These results indicate that melatonin may contribute to delaying senescence in pitaya fruits. This study shows the potential of the use of melatonin in the postharvest storage of pitaya fruits, as well as other horticultural fruits.
Essential minimal volume of Einstein 4-manifolds

The minimal volume of a closed manifold $M$ is the infimum of the volume of $(M,g)$ over all metrics $g$ with sectional curvature between $-1$ and $1$. We introduce a variant called the essential minimal volume, $\mathrm{ess-Minvol}(M)$, which is the limit, as $\delta>0$ goes to $0$, of the infimum of the volume of the $\delta$-thick part of $(M,g)$ over all metrics $g$ with sectional curvature between $-1$ and $1$. We show that, for some universal constant $C>0$, any closed Einstein 4-manifold $M$ with Euler characteristic $e(M)$ satisfies $$C^{-1}e(M) \leq \mathrm{ess-Minvol}(M) \leq Ce(M). $$ As a corollary, these inequalities are true for the essential minimal volume of closed complex surfaces of nonnegative Kodaira dimension. We conjecture that those linear bounds in fact hold for the minimal volume.
Learning Stable Representations with Full Encoder

While the beta-VAE family is aiming to find disentangled representations and acquire human-interpretable generative factors, like what an ICA (from the linear domain) does, we propose Full Encoder, a novel unified autoencoder framework as a correspondence to PCA in the non-linear domain. The idea is to train an autoencoder with one latent variable first, then involve more latent variables progressively to refine the reconstruction results. The Full Encoder is also a latent variable predictive model that the latent variables acquired are stable and robust, as they always learn the same representation regardless of the network initial states. Full Encoder can be used to determine the degrees of freedom in a simple non-linear system and can be useful for data compression or anomaly detection. Full Encoder can also be combined with the beta-VAE framework to sort out the importance of the generative factors, providing more insights for non-linear system analysis. These qualities will make FE useful for analyzing real-life industrial non-linear systems. To validate, we created a toy dataset with a custom-made non-linear system to test it and compare its properties to those of VAE and beta-VAE's.