Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of a (E,E)‐Dioxime Containing Two 15‐Membered Dioxatrithiamacrocycles and its Mononuclear Ni(II) Complex as Ag+ Extractants

Abstract A macrocyclic vic‐dioxime (1) and its mononuclear Ni(II) complex (2) were studied as extractant. The aqueous solutions of Ag+, Mn2+, Pb2+, Ni2+, Cu2+, Cd2+, and Zn2+ picrates were used for extraction experiments. The solutions of the ligands in two different organic solvents were used as organic phases. The metal picrate extractions were carried out at 25±0.1°C by using UV‐visible spectrometry. The most effective transport was observed for Ag+ picrate among the tested metal picrates. The effect of pH on the extraction of Ag+ picrate was evaluated with the ligands. The ratio of extracted Ag‐complex to chloroform phase was 2:1 (L:M) for (2). In other cases the ratios were 1:1 for both (1) and (2). Molar ratio method was also used to demonstrate the composition. The values of the extraction constants (log Kex) were determined for the extracted Ag‐complexes.
Nitrogen Isotope Separation Using Porous Microreticular Cation-Exchange Resin

A long-distance chromatographic operation was carried out in order to study the isotope accumulation of 15N in the ion-exchange enrichment process by using a porous microreticular cation-exchange resin. Combined bands of ammonium and lithium ions were eluted up to 20 m, and thereafter the ammonium band was eluted up to a total migration distance of 60 m. The constant lengths of the adsorbed bands were maintained throughout the elution, and a sharp boundary at the rear end of the ammonium band was monitored by an electric conductivity meter. The enrichment of both 15N at the rear end of the ammonium band and 6Li at the rear end of the lithium band were confirmed in the experiment. The values of HETP in the system were kept almost constant, 49 mm, throughout the migration up to a distance of 60 m. Previously reported works on the same process are analyzed, and an empirical relation for HETP in terms of resin diameter and band velocity has been obtained as HETP = c(dpμB)84.
The Effect of Shear Field on the Hydrolysis of Cellulose

Abstract In an attempt to elucidate the effect of mixing in high‐shear regimens on the extent of enzymatic hydrolysis of cellulosic substrates, cellulose was hydrolyzed using a commercial cellulase preparation at various enzyme concentrations and by three agitation regimens. The activity of cellulase decreased with increasing agitation rate and elapsed mixing time. The effect of shear on enzyme denaturation decreased with increasing enzyme concentration.
DCT and DST Filtering With Sparse Graph Operators

Graph filtering is a fundamental tool in graph signal processing. Polynomial graph filters (PGFs), defined as polynomials of a fundamental graph operator, can be implemented in the vertex domain, and usually have a lower complexity than frequency domain filter implementations. In this paper, we focus on the design of filters for graphs with graph Fourier transform (GFT) corresponding to a discrete trigonometric transform (DTT), i.e., one of 8 types of discrete cosine transforms (DCT) and 8 discrete sine transforms (DST). In this case, we show that multiple sparse graph operators can be identified, which allows us to propose a generalization of PGF design: multivariate polynomial graph filter (MPGF). First, for the widely used DCT-II (type-2 DCT), we characterize a set of sparse graph operators that share the DCT-II matrix as their common eigenvector matrix. This set contains the well-known connected line graph. These sparse operators can be viewed as graph filters operating in the DCT domain, which allows us to approximate any DCT graph filter by a MPGF, leading to a design with more degrees of freedom than the conventional PGF approach. Then, we extend those results to all of the 16 DTTs as well as their 2D versions, and show how their associated sets of multiple graph operators can be determined. We demonstrate experimentally that ideal low-pass and exponential DCT/DST filters can be approximated with higher accuracy with similar runtime complexity. Finally, we apply our method to transform-type selection in a video codec, AV1, where we demonstrate significant encoding time savings, with a negligible compression loss.
Patch AutoAugment

Data augmentation (DA) plays a critical role in training deep neural networks for improving the generalization of models. Recent work has shown that automatic DA policy, such as AutoAugment (AA) [7], signiﬁcantly improves model performance. However, most automatic DA methods search for DA policies at the image-level without considering that the optimal policies for different regions in an image may be diverse. In this paper, we propose a patch-level automatic DA algorithm called Patch AutoAugment (PAA). PAA divides an image into a grid of patches and searches for the optimal DA policy of each patch. Speciﬁcally, PAA allows each patch DA operation to be controlled by an agent and models it as a Multi-Agent Reinforcement Learning (MARL) problem. At each step, PAA samples the most effective operation for each patch based on its content and the semantics of the whole image. The agents cooperate as a team and share a uniﬁed team reward for achieving the joint optimal DA policy of the whole image. The experiment shows that PAA consistently improves the target network performance on many benchmark datasets of image classiﬁcation and ﬁne-grained image recognition. PAA also achieves remarkable computational efﬁciency, i.e 2.3 × faster than FastAA [31] and 56.1 × faster than AA [7] on ImageNet. The code is available at https://github.com/LinShiqi047/ PatchAutoAugment
Purchase History and Product Personalization

The trade-off between product personalization and price discrimination is at the center of the debate about the use of consumer data. Whereas firms use consumer data for product personalization, allowing the firm to better meet consumers' needs, consumer data in the form of purchase histories is also used for personalized pricing. Policy makers note that rich product lines may compensate for the costs of personalized pricing. Absent from this debate is that consumer data in the form of purchase histories is endogenously determined. On the one hand, the data is selected as it reflects the consumer's trade-off between a better product match and the costs of price discrimination. On the other hand, the firm designs which products the consumer can choose from, thereby controlling how informative purchase histories may be about the consumer's preferences.
Hydrodynamics of weak integrability breaking

We review recent progress in understanding nearly integrable models within the framework of generalized hydrodynamics (GHD). Integrable systems have infinitely many conserved quantities and stable quasiparticle excitations: when integrability is broken, only a few residual conserved quantities survive, eventually leading to thermalization, chaotic dynamics and conventional hydrodynamics. In this review, we summarize recent efforts to take into account small integrability breaking terms, and describe the transition from GHD to standard hydrodynamics. We discuss the current state of the art, with emphasis on weakly inhomogeneous potentials, generalized Boltzmann equations and collision integrals, as well as bound-state recombination effects. We also identify important open questions for future works.
ConDA: Continual Unsupervised Domain Adaptation

Domain Adaptation (DA) techniques are important for overcoming the domain shift between the source domain used for training and the target domain where testing takes place. However, current DA methods assume that the entire target domain is available during adaptation, which may not hold in practice. This paper considers a more realistic scenario, where target data become available in smaller batches and adaptation on the entire target domain is not feasible. In our work, we introduce a new, data-constrained DA paradigm where unlabeled target samples are received in batches and adaptation is performed continually. We propose a novel source-free method for continual unsupervised domain adaptation that utilizes a buffer for selective replay of previously seen samples. In our continual DA framework, we selectively mix samples from incoming batches with data stored in a buffer using buffer management strategies and use the combination to incrementally update our model. We evaluate the classification performance of the continual DA approach with state-of-the-art DA methods based on the entire target domain. Our results on three popular DA datasets demonstrate that our method outperforms many existing state-of-the-art DA methods with access to the entire target domain during adaptation.
Variational quantum compiling with double Q-learning

Quantum compiling aims to construct a quantum circuit V by quantum gates drawn from a native gate alphabet, which is functionally equivalent to the target unitary U. It is a crucial stage for the running of quantum algorithms on noisy intermediate-scale quantum (NISQ) devices. However, the space for structure exploration of quantum circuit is enormous, resulting in the requirement of human expertise, hundreds of experimentations or modifications from existing quantum circuits. In this paper, we propose a variational quantum compiling (VQC) algorithm based on reinforcement learning, in order to automatically design the structure of quantum circuit for VQC with no human intervention. An agent is trained to sequentially select quantum gates from the native gate alphabet and the qubits they act on by double Q-learning with ϵ-greedy exploration strategy and experience replay. At first, the agent randomly explores a number of quantum circuits with different structures, and then iteratively discovers structures with higher performance on the learning task. Simulation results show that the proposed method can make exact compilations with less quantum gates compared to previous VQC algorithms. It can reduce the errors of quantum algorithms due to decoherence process and gate noise in NISQ devices, and enable quantum algorithms especially for complex algorithms to be executed within coherence time.
A Systematical Study on Application Performance Management Libraries for Apps

Being able to automatically detect the performance issues in apps can significantly improve apps’ quality as well as having a positive influence on user satisfaction. Application Performance Management (APM) libraries are used to locate the apps’ performance bottleneck, monitor their behaviors at runtime, and identify potential security risks. Although app developers have been exploiting application performance management (APM) tools to capture these potential performance issues, most of them do not fully understand the internals of these APM tools and the effect on their apps. To fill this gap, in this paper, we conduct the first systematic study on APMs for apps by scrutinizing 25 widely-used APMs for Android apps and develop a framework named APMHunter for exploring the usage of APMs in Android apps. Using APMHunter, we conduct a large-scale empirical study on 500,000 Android apps to explore the usage patterns of APMs and discover the potential misuses of APMs. We obtain two major findings: 1) some APMs still employ deprecated permissions and approaches, which makes APMs fail to perform as expected; 2) inappropriate use of APMs can cause privacy leaks. Thus, our study suggests that both APM vendors and developers should design and use APMs scrupulously.
Human De-occlusion: Invisible Perception and Recovery for Humans

In this paper, we tackle the problem of human deocclusion which reasons about occluded segmentation masks and invisible appearance content of humans. In particular, a two-stage framework is proposed to estimate the invisible portions and recover the content inside. For the stage of mask completion, a stacked network structure is devised to refine inaccurate masks from a general instance segmentation model and predict integrated masks simultaneously. Additionally, the guidance from human parsing and typical pose masks are leveraged to bring prior information. For the stage of content recovery, a novel parsing guided attention module is applied to isolate body parts and capture context information across multiple scales. Besides, an Amodal Human Perception dataset (AHP) is collected to settle the task of human de-occlusion. AHP has advantages of providing annotations from real-world scenes and the number of humans is comparatively larger than other amodal perception datasets. Based on this dataset, experiments demonstrate that our method performs over the state-of-the-art techniques in both tasks of mask completion and content recovery. Our AHP dataset is available at https://sydney0zq.github. io/ahp/.
Engineering an IoT-Edge-Cloud Computing System Architecture: Lessons Learnt from An Undergraduate Lab Course

With the rapid advances in IoT, edge and cloud computing solutions, it is critical to educate and train students in computer science and engineering in various aspects of IoT-edge-cloud (IoT-E-C) system architecture implementations. We outline the design and development of an undergraduate laboratory course that sets the goal of implementing various interfaces and communication protocols to connect IoT, edge and cloud computing systems and evaluating their performance. The lab setup is modular and based on open source tools. In the IoT context, it consists of low-cost processing platforms with various sensors and actuators. In the edge and cloud computing context, we implement and deploy single board computers and Firebase cloud solutions, respectively. The modular lab setup allows students to engineer and integrate various communication protocol solutions, including MQTT, COAP and HTTP. In addition to the system implementation, students can evaluate and benchmark the performance of the entire system.
Sequential Extraction of Metal Contaminated Soils with Radiochemical Assessment of Readsorption Effects

The operational speciation of selected trace metals in several contaminated Toronto area soils was determined by a modified BCR (Community Bureau of Reference) sequential extraction procedure. This type of environmental analysis is now increasingly used, but its reliability is questioned due to concerns regarding redistribution of extracted metals occurring during the procedure. Using radiotracer techniques, the redistribution of Cd and Zn during the sequential extraction was directly quantified. For a fill material taken from downtown Toronto, metal partitioning behavior conformed with the theory and anecdotal evidence in the soil chemistry literature: Cd was highly mobile, while Cu and Pb were primarily associated with oxidizable organic matter, and Zn was distributed across all soil fractions. Soil from an abandoned metals recycling operation had unusual physical characteristics, and the trace metals within were largely nonextractable. In the soils examined, some 20−30% of the Cd and Zn released from .
A theorem on meromorphic descent and the specialization of the pro-\'etale fundamental group

Given a Noetherian formal scheme X̂ over SpfpRq, where R is a complete DVR, we first prove a theorem of meromorphic descent along a possibly infinite cover of X̂. Using this we construct a specialization functor from the category of continuous representations of the pro-étale fundamental group of the special fiber to the category of F -divided sheaves on the generic fiber. This specialization functor partially recovers the specialization functor of the étale fundamental groups. We also express the pro-étale fundamental group of a connected scheme X of finite type over a field as coproducts and quotients of the free group and the étale fundamental groups of the normalizations of the irreducible components of X and those of its singular loci.
Recovery of Joint Probability Distribution from One-Way Marginals: Low Rank Tensors and Random Projections

Joint probability density or joint probability mass function (PDF/PMF) estimation is a fundamental machine learning problem. The number of free parameters scales exponentially with respect to the number of random variables. Hence, most work on nonparametric joint distribution estimation is based on some structural assumptions such as clique factorization adopted by probabilistic graphical models, imposition of low rank on the joint probability tensor and reconstruction from 3-way or 2-way marginals, etc. In the current work, we link random projections of data to the problem of PMF estimation using techniques from tomography. Using it alongside low-rank tensor decomposition, we present an approach to estimate joint distribution from just one-way marginals in a transformed space. We provide a novel algorithm for recovering factors of the tensor from one-way marginals, test it across synthetic and real-world datasets, and also perform MAP inference on the estimated model for classification.
QCD analysis of near-threshold photon-proton production of heavy quarkonium

The near-threshold photo or electroproduction of heavy vector quarkonium off the proton is studied in quantum chromodynamics. Similar to the high-energy limit, the production amplitude can be factorized in terms of gluonic generalized parton distributions and the quarkonium distribution amplitude. At the threshold, the threshold kinematics has a large skewness parameter ξ , leading to the dominance of the spin-2 contribution over higher-spin twist-2 operators. Thus, threshold production data are useful to extract the gluonic gravitational form factors, allowing studying the gluonic contributions to the quantum anomalous energy, mass radius, spin, and mechanical pressure in the proton. We use the recent GlueX data on the J= ψ photoproduction to illustrate the potential physics impact from the high-precision data from the future Jefferson Laboratory 12 GeV and Electron-Ion Collider physics program. DOI:
A note on sampling recovery of multivariate functions in the uniform norm

We study the recovery of multivariate functions from reproducing kernel Hilbert spaces in the uniform norm. Our main interest is to obtain preasymptotic estimates for the corresponding sampling numbers. We obtain results in terms of the decay of related singular numbers of the compact embedding into L2(D, ̺D) multiplied with the supremum of the Christoffel function of the subspace spanned by the first m singular functions. Here the measure ̺D is at our disposal. As an application we obtain near optimal upper bounds for the sampling numbers for periodic Sobolev type spaces with general smoothness weight. Those can be bounded in terms of the corresponding benchmark approximation number in the uniform norm, which allows for preasymptotic bounds. By applying a recently introduced sub-sampling technique related to Weaver’s conjecture we mostly lose a √ log n and sometimes even less. Finally we point out a relation to the corresponding Kolmogorov numbers.
Linear elasticity of polymer gels: Fundamental differences from rubbers

We recently found that the energy contribution to the linear elasticity of polymer gels in the as-prepared state can be a significant negative value; the shear modulus is not proportional to the temperature. Our finding prompts a paradigm shift in the understanding of polymer-gel elasticity, challenging the conventional notion that the polymer-gel elasticity is mainly determined by entropy contribution. Existing molecular models of classical rubber elasticity theories, including the affine, phantom, or junction affine network model, cannot be used to estimate the structural parameters of polymer gels. In this focus review, we summarize the experimental studies on the linear elasticity of polymer gels in the as-prepared state using tetra gels with a homogenous polymer network. We also provide a unified formula for the linear elasticity of polymer gels with various network topologies and densities. Using the unified formula, we resolve the past experimental results that seem to be inconsistent with each other. Finally, we mention that there are still fundamental unresolved problems with the linear elasticity of polymer gels.
MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans

We propose a novel method for reconstructing floor plans from noisy 3D point clouds. Our main contribution is a principled approach that relies on the Monte Carlo Tree Search (MCTS) algorithm to maximize a suitable objective function efficiently despite the complexity of the problem. Like previous work, we first project the input point cloud to a top view to create a density map and extract room proposals from it. Our method selects and optimizes the polygonal shapes of these room proposals jointly to fit the density map and outputs an accurate vectorized floor map even for large complex scenes. To do this, we adapt MCTS, an algorithm originally designed to learn to play games, to select the room proposals by maximizing an objective function combining the fitness with the density map as predicted by a deep network and regularizing terms on the room shapes. We also introduce a refinement step to MCTS that adjusts the shape of the room proposals. For this step, we propose a novel differentiable method for rendering the polygonal shapes of these proposals. We evaluate our method on the recent and challenging Structured3D and Floor-SP datasets and show a significant improvement over the state-of-the-art, without imposing any hard constraints nor assumptions on the floor plan configurations.
Directional shear-jamming

In this work we study shear reversals of dense non-Brownian suspensions composed of cohesionless elliptical particles. By numerical simulations we show that a new fragility appears for frictionless ellipses in the flowing states, where particles are able to flow indefinitely in one direction at applied shear stresses but shear-jams in the other direction upon shear stress reversal. This new fragility, absent in the isotropic particle case, is linked to the directional order of the elongated particles at steady shear and its reorientation at shear stress reversal, which forces the suspensions to pass through a disordered state in which it might get arrested.
Machine Learning Empowered Resource Allocation in IRS Aided MISO-NOMA Networks

A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster. The goal is to maximize the sum-rate of all users by jointly optimizing the passive beamforming vector at the IRS, decoding order, power allocation coefficient vector and number of clusters, subject to the rate requirements of users. In order to tackle the formulated problem, a three-step approach is proposed. More particularly, a long short-term memory (LSTM) based algorithm is first adopted for predicting the mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM) algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN) based algorithm is invoked for jointly determining the phase shift matrix and power allocation policy. Simulation results are provided for demonstrating that the proposed algorithm outperforms the benchmarks, while the throughput gain of 35% can be achieved by invoking NOMA technique instead of orthogonal multiple access (OMA).