Miracle-dz/UniFAR-FTUs huggingface dataset:
An application of waste algae biochar in aquaculture water to remove co-existed cadmium and PAHs and the corresponding mechanism

ABSTRACT Water quality in aquaculture farms is highly related to the quality of aquaculture products and the connected environment. Cadmium (Cd2+) and polycyclic aromatic hydrocarbons (PAHs) are two of the most common pollutants in the aquaculture water, while biochar derived from waste algae (Enteromorpha prolifera), namely BE, was applied in farms ponds to improve water quality. Firstly, the adverse environmental impact of BE was minor, while the concentrations of the heavy metal (Cd2+ in the present study) and PAHs (FLU, PHE, FLT and PYR) were removed with efficiencies of 49%, 88%, 90%, 91% and 88%, respectively. The ecological risk values (RQs) were reduced subsequently with a rate of 58 ± 11%. After dosing BE, the ecological risk values in all the studied ponds were lower than 1, indicating no ecological risk in the corresponding aquaculture environment. The sorption capacities (qm ) of BE were 15, 12, 6.3, 0.41, 0.29 and 0.56 mg·g−1 for Cd2+, FLU, PHE, FLT, PYR and BaP, respectively. The sorption capacities were acceptable compared with those derived from other types of biomass. The removal mechanisms were partition (PAHs), complexation (Cd2+), π-π interaction (Cd2+ and PAHs), precipitation (Cd2+) and ion-exchange (Cd2+). Practically and theoretically, the algae biochar is applicable in the aquaculture environment, where Cd2+ and PAHs co-exist. GRAPHICAL ABSTRACT
Reachability Analysis of Neural Networks Using Mixed Monotonicity

This letter presents a new reachability analysis approach to compute interval over-approximations of the output set of feedforward neural networks with input uncertainty. We adapt to neural networks an existing mixed-monotonicity method for the reachability analysis of dynamical systems and apply it to each partial network within the main network. This ensures that the intersection of the obtained results is the tightest interval over-approximation of the output of each layer that can be obtained using mixed-monotonicity on any partial network decomposition. Unlike other tools in the literature focusing on small classes of piecewise-affine or monotone activation functions, the main strength of our approach is its generality: it can handle neural networks with any Lipschitz-continuous activation function. In addition, the simplicity of our framework allows users to very easily add unimplemented activation functions, by simply providing the function, its derivative and the global argmin and argmax of the derivative. Our algorithm is compared to five other interval-based tools (Interval Bound Propagation, RELUVAL, NEURIFY, VERINET, CROWN) on both existing benchmarks and two sets of small and large randomly generated networks for four activation functions (ReLU, TanH, ELU, SiLU).
Hierarchical Bayesian Bandits

Meta-, multi-task, and federated learning can be all viewed as solving similar tasks, drawn from a distribution that reflects task similarities. We provide a unified view of all these problems, as learning to act in a hierarchical Bayesian bandit. We propose and analyze a natural hierarchical Thompson sampling algorithm (HierTS) for this class of problems. Our regret bounds hold for many variants of the problems, including when the tasks are solved sequentially or in parallel; and show that the regret decreases with a more informative prior. Our proofs rely on a novel total variance decomposition that can be applied beyond our models. Our theory is complemented by experiments, which show that the hierarchy helps with knowledge sharing among the tasks. This confirms that hierarchical Bayesian bandits are a universal and statistically-efficient tool for learning to act with similar bandit tasks.
TAP approach for multispecies spherical spin glasses II: The free energy of the pure models

In a companion paper we developed the generalized TAP approach for general multi-species spherical mixed $p$-spin models. In this paper, we use it to compute the limit of the free energy at any temperature for all pure multi-species spherical $p$-spin models, assuming that certain free energies converge. Importantly, the pure multi-species models do not satisfy the convexity assumption on the mixture which was crucial in the recent proofs of the Parisi formula for the multi-species Sherrington-Kirkpatrick model by Barra et al. (2015) and Panchenko (2015) and for the multi-species spherical mixed p-spin models by Bates and Sohn (2021).
QK Iteration: A Self-Supervised Representation Learning Algorithm for Image Similarity

Self-supervised representation learning is a fundamental problem in computer vision with many useful applications (e.g., image search, instance level recognition, copy detection). In this paper we present a new contrastive self-supervised representation learning algorithm in the context of Copy Detection in the 2021 Image Similarity Challenge hosted by Facebook AI Research. Previous work in contrastive self-supervised learning has identified the importance of being able to optimize representations while ``pushing'' against a large number of negative examples. Representative previous solutions either use large batches enabled by modern distributed training systems or maintain queues or memory banks holding recently evaluated representations while relaxing some consistency properties. We approach this problem from a new angle: We directly learn a query model and a key model jointly and push representations against a very large number (e.g., 1 million) of negative representations in each SGD step. We achieve this by freezing the backbone on one side and by alternating between a Q-optimization step and a K-optimization step. During the competition timeframe, our algorithms achieved a micro-AP score of 0.3401 on the Phase 1 leaderboard, significantly improving over the baseline $\mu$AP of 0.1556. On the final Phase 2 leaderboard, our model scored 0.1919, while the baseline scored 0.0526. Continued training yielded further improvement. We conducted an empirical study to compare the proposed approach with a SimCLR style strategy where the negative examples are taken from the batch only. We found that our method ($\mu$AP of 0.3403) significantly outperforms this SimCLR-style baseline ($\mu$AP of 0.2001).
First-order transition between the plaquette valence bond solid and antiferromagnetic phases of the Shastry-Sutherland model

We study the ground state phase diagram of the Shastry-Sutherland model by using the variational optimization of the infinite tensor network states, and find a weakly first-order transition between the plaquette and the antiferromagnetic states. The full plaquette state strongly competes with the empty plaquette ground state, with an energy difference less than $10^{-4}J$. We show a staggered ring exchange interaction that preserves the Shastry-Sutherland lattice symmetry can stabilize the full plaquette ground state. In light of this, we propose the triple point where the full plaquette, empty plaquette, and antiferromagnetic phases meet as a deconfined quantum critical point.
Moduli Spaces of Unstable Objects: Sheaves of Harder-Narasimhan Length 2

Given a moduli problem posed using Geometric Invariant Theory, one can use Non-Reductive Geometric Invariant Theory to quotient unstable HKKN strata and construct 'moduli spaces of unstable objects', extending the usual moduli classifications. After giving a self-contained account of how to do this, we apply this method to construct moduli spaces for certain unstable coherent sheaves of HN length 2 on a projective scheme, which we call $\tau$-stable sheaves. This extends a previous result of Brambila-Paz and Mata-Guti\'{e}rrez for rank two vector bundles on a curve.
360° Polarization Control of Terahertz Spintronic Emitters Using Uniaxial FeCo/TbCo2/FeCo Trilayers

Polarization control of THz light is of paramount interest for the numerous applications offered in this frequency range. Recent developments in THz spintronic emitters allow for a very efficient broadband emission, and especially unique is their ability of THz polarization switching through magnetization control of the ferromagnetic layer. Here we present an improved scheme to achieve full 360 nearly coherent polarization rotation that does not require multipolar or rotating external magnetic bias nor complex cascaded emitters. By replacing the FM layer of the spintronic emitter with a carefully designed FeCo/TbCo2/FeCo anisotropic heterostructure, we experimentally demonstrate Stoner-Wohlfarth-like coherent rotation of the THz polarization over a full 2c azimuth only by a bipolar variation of the strength of the hard axis field, and with only a negligible decrease in the emission efficiency as compared to standard Pt/CoFeB/W inverse spin Hall emitters. THz measurements are in agreement with our model of the non-perfect Stoner-Wohlfarth behaviour. These emitters are well adapted for the implementation of polarimetric characterization not requiring any mechanically rotating polarizing elements. An example is given with the characterization of the birefringence in a quartz plate. © 2021 Optica Publishing Group
Delayed Feedback in Episodic Reinforcement Learning

There are many provably efﬁcient algorithms for episodic reinforcement learning. However, these algorithms are built under the assumption that the sequences of states, actions and rewards associated with each episode arrive immediately, allowing policy updates after every interaction with the environment. This assumption is often unrealistic in practice, particularly in areas such as healthcare and online recommendation. In this paper, we study the impact of delayed feedback on several provably efﬁcient algorithms for regret minimisation in episodic reinforcement learning. Firstly, we consider updating the policy as soon as new feedback becomes available. Using this updating scheme, we show that the regret increases by an additive term involving the number of states, actions, episode length and the expected delay. This additive term changes depending on the optimistic algorithm of choice. We also show that updating the policy less frequently can lead to an improved dependency of the regret on the delays.
Interquantile Shrinkage in Spatial Quantile Autoregressive Regression models

Spatial dependent data frequently occur in many fields such as spatial econometrics and epidemiology. To deal with the dependence of variables and estimate quantile-specific effects by covariates, spatial quantile autoregressive models (SQAR models) are introduced. Conventional quantile regression only focuses on the fitting models but ignores the examination of multiple conditional quantile functions, which provides a comprehensive view of the relationship between the response and covariates. Thus, it is necessary to study the different regression slopes at different quantiles, especially in situations where the quantile coefficients share some common feature. However, traditional Wald multiple tests not only increase the burden of computation but also bring greater FDR. In this paper, we transform the estimation and examination problem into a penalization problem, which estimates the parameters at different quantiles and identifies the interquantile commonality at the same time. To avoid the endogeneity caused by the spatial lag variables in SQAR models, we also introduce instrumental variables before estimation and propose two-stage estimation methods based on fused adaptive LASSO and fused adaptive sup-norm penalty approaches. The oracle properties of the proposed estimation methods are established. Through numerical investigations, it is demonstrated that the ∗The corresponding author: Yunquan Song. Email: syqfly1980@163.com. This research was supported by NNSF project (61503412) of China, NSF project (ZR2019MA016) of Shandong Province of China. 1 ar X iv :2 11 1. 07 06 7v 1 [ st at . M E ] 1 3 N ov 2 02 1 proposed methods lead to higher estimation efficiency than the traditional quantile regression.
On the second moment method and RS phase of multi-species spherical spin glasses

Excluding some special cases, computing the critical inverse-temperature $\beta_c$ of a mixed $p$-spin spin glass model is a difficult task. The only known method to calculate its value for a general model requires the full power of the Parisi formula. On the other hand, an easy application of the second moment method to the partition function yields an explicit lower bound $\beta_m\leq \beta_c$ to the critical inverse-temperature. Interestingly, in the important case of the Sherrington-Kirkpatrick model $\beta_m=\beta_c$. In this work we consider the multi-species spherical mixed $p$-spin models without external field, and characterize by a simple condition the models for which the second moment method works in the whole replica symmetric phase, namely, models such that $\beta_m=\beta_c$. In particular, for those models we obtain the value of $\beta_c$.
A Typology of Apparel Shopping Orientation Segments Among Female Consumers

The purposes of the study are (1) to segment female apparel shoppers into unique apparel shopping orientation groups; and (2) to develop a profile for each segment with respect to information sources, importance of store attributes, lifestyle activities, patronage behavior, and demographics. Data (N = 482) were collected via a questionnaire sent to a nationwide random sample of 1,400 females. By cluster analysis of apparel shopping orientation factors, three groups are identified: (1) Highly Involved Apparel Shopper (46%); (2) Apathetic Apparel Shopper (34%); and (3) Convenience-Oriented Catalog Shopper (20%). These three groups were then compared on several characteristics through multivariate analysis of variance (MANOVA) and chi-square statistics. The results indicate that shopping orientations are a base for segmenting female apparel shoppers and these groups are unique in consumer buying characteristics. These characteristics include: (1) three factors of information sources (Store Fashion Service/Promotion, Fashion Publications, and Mass Media); (2) five factors of importance of store attributes (Store Personnel, Visual Image of Store, Customer Service, Easy Access, and Brand/Fashion); (3) two factors of lifestyle activities (Cultural and Grooming), and (4) other variables (patronage behavior, annual wardrobe expenditure, occupation, housing area and income).
Bayesian modelling and computation utilising cycles in multiple network data

Modelling multiple network data is crucial for addressing a wide range of applied research questions. However, there are many challenges, both theoretical and computational, to address. Network cycles are often of particular interest in many applications, such as ecological studies, and an unexplored area has been how to incorporate networks' cycles within the inferential framework in an explicit way. The recently developed Spherical Network Family of models (SNF) offers a flexible formulation for modelling multiple network data that permits any type of metric. This has opened up the possibility to formulate network models that focus on network properties hitherto not possible or practical to consider. In this article we propose a novel network distance metric that measures similarities between networks with respect to their cycles, and incorporate this within the SNF model to allow inferences that explicitly capture information on cycles. These network motifs are of particular interest in ecological studies. We further propose a novel computational framework to allow posterior inferences from the intractable SNF model for moderate sized networks. Lastly, we apply the resulting methodology to a set of ecological network data studying aggressive interactions between species of fish. We show our model is able to make cogent inferences concerning the cycle behaviour amongst the species, and beyond those possible from a model that does not consider this network motif.
Evaluating the Effectiveness of Phishing Reports on Twitter

Phishing attacks are an increasingly potent web-based threat, with nearly 1.5 million such websites being created on a monthly basis. In this work, we present the first study towards identifying phishing attacks through reports shared by security conscious users on Twitter. We evaluated over 16.4k such reports posted by 701 Twitter accounts between June to August 2021, which contained 11.1k unique URLs, and analyzed their effectiveness using various quantitative and qualitative measures. Our findings indicate that not only these reports share a high volume of legitimate phishing URLs, but they also contain more information regarding the phishing websites (which can expedite the process of identifying and removing these threats), when compared to two popular open-source phishing feeds: PhishTank and OpenPhish. We also noticed that the URLs in the Twitter reports had very little overlap with the URLs found on PhishTank and OpenPhish, and also remained active for longer periods of time. However, despite having these attributes, we found that these reports have very low interaction from other users on Twitter, especially from the domains and organizations which were targeted by the reported URLs. Moreover, nearly 31% of these URLs were still active even after a week of them being reported while also being detected by very few anti-phishing tools. This suggests that a large majority of these reports remain undiscovered and underutilized. Thus, this work highlights the utility of phishing reports shared on Twitter, and the benefits of using them as an open source knowledge base for identifying new phishing websites.
Recent findings on seismic earth pressures

Despite a lack of evidence or credible case histories of engineered retaining walls and basement walls being damaged during earthquakes, design codes and standard of engineering practice have dictated that seismic earth pressures should be included in the design of earth retaining structures in seismic regions. Methods of estimating seismic earth pressures are based on experiments and theories performed and developed decades ago without consideration of geometric and material property equivalence or scaling. More recent experiments using centrifuges that can properly scale geometry and material properties have given new insights on the development of seismic earth pressures on various types of earth retaining wall systems with different types of retained soil. Copyright © 2012 John Wiley & Sons, Ltd.
Large Order-Invariant Bayesian VARs with Stochastic Volatility

Many popular specifications for Vector Autoregressions (VARs) with multivariate stochastic volatility are not invariant to the way the variables are ordered due to the use of a Cholesky decomposition for the error covariance matrix. We show that the order invariance problem in existing approaches is likely to become more serious in large VARs. We propose the use of a specification which avoids the use of this Cholesky decomposition. We show that the presence of multivariate stochastic volatility allows for identification of the proposed model and prove that it is invariant to ordering. We develop a Markov Chain Monte Carlo algorithm which allows for Bayesian estimation and prediction. In exercises involving artificial and real macroeconomic data, we demonstrate that the choice of variable ordering can have non-negligible effects on empirical results. In a macroeconomic forecasting exercise involving VARs with 20 variables we find that our order-invariant approach leads to the best forecasts and that some choices of variable ordering can lead to poor forecasts using a conventional, non-order invariant, approach.
Curriculum Learning for Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) is a task where an agent navigates in an embodied indoor environment under human instructions. Previous works ignore the distribution of sample difficulty and we argue that this potentially degrade their agent performance. To tackle this issue, we propose a novel curriculum-based training paradigm for VLN tasks that can balance human prior knowledge and agent learning progress about training samples. We develop the principle of curriculum design and re-arrange the benchmark Room-to-Room (R2R) dataset to make it suitable for curriculum training. Experiments show that our method is model-agnostic and can significantly improve the performance, the generalizability, and the training efficiency of current state-of-the-art navigation agents without increasing model complexity.
Signaling Games for Log-Concave Distributions: Number of Bins and Properties of Equilibria

We investigate the equilibrium behavior for the decentralized cheap talk problem for real random variables and quadratic cost criteria in which an encoder and a decoder have misaligned objective functions. In prior work, it has been shown that the number of bins in any equilibrium has to be countable, generalizing a classical result due to Crawford and Sobel who considered sources with density supported on [0, 1]. In this paper, we first refine this result in the context of log-concave sources. For sources with two-sided unbounded support, we prove that, for any finite number of bins, there exists a unique equilibrium. In contrast, for sources with semi-unbounded support, there may be a finite upper bound on the number of bins in equilibrium depending on certain conditions stated explicitly. Moreover, we prove that for log-concave sources, the expected costs of the encoder and the decoder in equilibrium decrease as the number of bins increases. Furthermore, for strictly log-concave sources with two-sided unbounded support, we prove convergence to the unique equilibrium under best response dynamics which starts with a given number of bins, making a connection with the classical theory of optimal quantization and convergence results of Lloyd’s method. In addition, we consider more general sources which satisfy certain assumptions on the tail(s) of the distribution and we show that there exist equilibria with infinitely many bins for sources with two-sided unbounded support. Further explicit characterizations are provided for sources with exponential, Gaussian, and compactly-supported probability distributions.
Efficient Training of Visual Transformers with Small Datasets

Visual Transformers (VTs) are emerging as an architectural paradigm alternative to Convolutional networks (CNNs). Differently from CNNs, VTs can capture global relations between image elements and they potentially have a larger representation capacity. However, the lack of the typical convolutional inductive bias makes these models more data-hungry than common CNNs. In fact, some local properties of the visual domain which are embedded in the CNN architectural design, in VTs should be learned from samples. In this paper, we empirically analyse different VTs, comparing their robustness in a small training-set regime, and we show that, despite having a comparable accuracy when trained on ImageNet, their performance on smaller datasets can be largely different. Moreover, we propose a self-supervised task which can extract additional information from images with only a negligible computational overhead. This task encourages the VTs to learn spatial relations within an image and makes the VT training much more robust when training data are scarce. Our task is used jointly with the standard (supervised) training and it does not depend on specific architectural choices, thus it can be easily plugged in the existing VTs. Using an extensive evaluation with different VTs and datasets, we show that our method can improve (sometimes dramatically) the final accuracy of the VTs. Our code is available at: https://github.com/yhlleo/VTs-Drloc.