Miracle-dz/UniFAR-FTUs huggingface dataset:
Photocatalytic degradation of Remazol Red B and Rhodamine B dyes using TiO2 nanomaterial: estimation of the effective operating parameters

The presence of persistent organic pollutants (POPs) including dyes in the aquatic ecosystem is of major concern worldwide because of their hazardous effect, and human and environmental risks. The degradation of conventional wastewater plants costs a lot, due to increased environmental restrictions. Using photocatalysis to remove (POPs) is a promising, effective and sustainable process. A simple one-step sol–gel procedure for degradation of Remazol Red B (RRB) and Rhodamine B (RhB) has been used to synthesize titanium dioxide photocatalyst. Various techniques, such as X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, Brunauer–Emmett–Teller, and parameters that influence the photodegradation process, including catalyst dose, pH, initial dye concentration, were used to study TiO2 photoactivity. The degradation of TiO2 via UV radiation for RRB and RhB after 75 min was 99.7% and 99.8% respectively. The photodegradation rates for two dyes using a Langmuir–Hinshelwood are shown pseudo-first-order. Besides, reactive species scavengers have been studied during degradation and the major species is OH• radicals. Photoluminescence experiments using terephthalic acid as a reference molecule determined OH• free radical formation during irradiation.
Learning Attribute-driven Disentangled Representations for Interactive Fashion Retrieval

Interactive retrieval for online fashion shopping provides the ability to change image retrieval results according to the user feedback. One common problem in interactive retrieval is that a specific user interaction (e.g., changing the color of a T-shirt) causes other aspects to change inadvertently (e.g., the retrieved item has a sleeve type different than the query). This is a consequence of existing methods learning visual representations that are semantically entangled in the embedding space, which limits the controllability of the retrieved results. We propose to leverage on the semantics of visual attributes to train convolutional networks that learn attribute-specific subspaces for each attribute to obtain disentangled representations. Thus operations, such as swapping out a particular attribute value for another, impact the attribute at hand and leave others untouched. We show that our model can be tailored to deal with different retrieval tasks while maintaining its disentanglement property. We obtain state-of-the-art performance on three interactive fashion retrieval tasks: attribute manipulation retrieval, conditional similarity retrieval, and outfit complementary item retrieval. Code and models are publicly available1.
Systems to optimise conversion efficiencies at Ontario Hydro's hydroelectric plants

Optimal dispatch systems at hydroelectric power plants manage the energy conversion process such that, under all conditions, the station efficiency is maximised. This paper describes an optimal dispatch system which has proven to be successful at two installations, one on the Nipigon river controlling three stations near Thunder Bay, Ontario, and the other at R.H. Saunders generating station on the St. Lawrence river near Cornwall, Ontario, Canada. The validated benefits amount to 15GWh per year, which is about 0.25% of the annual production at the respective stations. Secondary benefits arising from this system include increased decision making assistance, improved accessibility to plant data and an overall improvement in operational performance.
"How Do You Not Lose Friends?": Synthesizing a Design Space of Social Controls for Securing Shared Digital Resources Via Participatory Design Jams

Digital resources (streaming services, banking accounts, collaborative documents, etc.) are commonly shared among small, social groups. Yet, the security and privacy (S&P) controls for these resources map poorly onto the reality of shared access and ownership (e.g., one shared Netﬂix password for roommates). One challenge is that the design space for social S&P controls remains unclear. We bridged this gap by engaging end-users in participatory design workshops to envision social solutions to S&P challenges common to their groups. In analyzing the generated ideas and group discussions, we identiﬁed four design considerations salient to social S&P controls: social transparency; structures of governance; stakes and responsibility; and, promoting pro-group S&P behaviors. Additionally, we discovered trade-offs and challenges that arise when designing social S&P controls: balancing group security versus individual privacy; combating social friction; mitigating social herding behaviors; and, minimizing coordination costs.
Effective flexural stiffness of slender structural concrete columns

The CSA A23.3 standard permits the use of a moment-magnifier approach for the design of slender reinforced concrete and composite steel–concrete columns. This approach is strongly influenced by the effective flexural stiffness (EI), which varies due to the nonlinearity of the concrete stress–strain curve and the cracking along the column length, among other factors. The EI equations given in the CSA standard are approximate when compared with the EI values computed from the axial load – bending moment – curvature relationships. This study was conducted to determine the influence of a full range of variables on EI used for the design of slender reinforced concrete and composite steel–concrete columns, and also to examine the existing CSA EI equations. Over 27 000 isolated concrete columns, each with a different combination of specified variables, in symmetrical single-curvature bending were simulated to generate the stiffness data. Two new design equations to compute EI of structural concrete columns were .
Patient experience with healthcare: Feedback for a Post COVID-19 clinic at a tertiary care center in rural area

Purpose: Post-acute sequelae of SARS-CoV-2 infection(PASC) is a complex condition with multi-system involvement. We assessed patients perspectives and experience with a PASC clinic established at University of Iowa in June 2020. Methods: We conducted a mixed-method survey in June 2021 to ask PASC clinic patients about 1) PASC symptoms and their impact on physical and mental health, and cognition using the PROMIS Global Health and Cognitive Function abilities items, and 2) satisfaction with clinic services and referrals, barriers to care, and recommended support resources. Findings: Ninety-seven patients (97/277, 35% response rate) completed the survey. Most were women(67%, n=65/97), Caucasian(93%, n=90/97) and received outpatient care during acute COVID-19 illness (79%). Fifty percent reported wait time of 1-3 months and 40% traveled >1 hour for the appointment at PASC clinic. The most common symptoms >3 months from initial infection were fatigue (77%), brain fog (73%), exercise intolerance (73%), anxiety (63%), sleep difficulties (56%) and depression (44%). A minority of patients reported significantly reduced functioning ([≥]1.5 SD below mean) of their physical health (22.5%), mental health (15.9%) and cognitive abilities (17.6%). Qualitative analysis of open-ended answers added valuable context to quantitative results. Satisfaction with clinical services was high though participants identified barriers to care including scheduling delays and financial concerns. Respondents suggested potential strategies for optimizing recovery including continuity of care, a co-located multispecialty clinic and being provided with timely information from emerging research. Conclusion: Our study reports high PASC symptom burden, its impact on health and patient experience with healthcare. It is important that primary healthcare professionals listen to patients with empathy and support them during recovery. Healthcare systems and policymakers should focus on accessible, comprehensive, and patient-centered integrated care.
Distributed network management in the flat structured mobile communities

Delivering proper management into the flat structured mobile communities is crucial for improving users experience and increase applications diversity in mobile networks. The available P2P applications do application-centric management, but it cannot replace network-wide management, especially when a number of different applications are used simultaneously in the network. The network-wide management is the key element required for a smooth transition from standalone P2P applications to the self-organizing mobile communities that maintain various services with quality and security guaranties. The classical centralized network management solutions are not applicable in the flat structured mobile communities due to the decentralized nature and high mobility of the underlying networks. Also the basic network management tasks have to be revised taking into account specialties of the flat structured mobile communities. The network performance management becomes more dependent on the current nodes' context, which also requires extension of the configuration management functionality. The fault management has to take into account high mobility of the network nodes. The performance and accounting managements are mainly targeted in maintain an efficient and fair access to the resources within the community, however they also allow unbalanced resource use of the nodes that explicitly permit it, e.g. as a voluntary donation to the community or due to the profession (commercial) reasons. The security management must implement the new trust models, which are based on the community feedback, professional authorization, and a mix of both. For fulfilling these and another specialties of the flat structured mobile communities, a new network management solution is demanded. The paper presents a distributed network management solution for flat structured mobile communities. Also the paper points out possible network management roles for the different parties (e.g. operators, service providing hubs/super nodes, etc.) involved in a service providing chain.
Compensation voltage control in dynamic voltage restorers by use of feed forward and state feedback scheme

This paper discusses the control of the compensation voltages in dynamic voltage restorers (DVR). It first analyzes the power circuit of a DVR system in order to come up with appropriate control limitations and control targets for the compensation voltage control. Based on this power stage analysis, a combined feed forward and state feedback control structure for the compensation voltages of DVRs is developed. This paper also discusses the time delay problems inherent in the digital control system of a DVR. Digital control systems normally have control delay from the sampling period, the switching frequency of the inverter, the sensor transmission time, etc. The control delay increases the dimension of the system transfer function. This makes the control system more unstable. This paper analyzes the control performance related with the control delay, closed loop damping factor, and the output filter parameters in DVR systems. Based on the control system analysis, design guidelines are proposed for the control gains and the inverter switching frequency of DVRs. The proposed theory is verified by an experimental DVR system with a full digital controller.
Max-algebraic hybrid automata: Modelling and equivalences

This article introduces the novel framework of max-algebraic hybrid automata as a hybrid modelling language in the max-plus algebra. We show that the modelling framework unifies and extends the switching max-plus linear systems framework and is analogous to the discrete hybrid automata framework in conventional algebra. In addition, we show that the framework serves as a bridge between automata-theoretic models in max-plus algebra and switching max-plus linear systems. In doing so, we formalise the relationship between max-plus automata and switching max-plus linear systems in a behavioural sense. This also serves as another step towards importing tools for analysis and optimal control from conventional time-driven hybrid systems to discrete-event systems in max-plus algebra.
Split rings for metamaterial and microwave circuit design: A review of recent developments (Invited paper)

This article is a review of recent applications of split rings to the design of planar microwave circuits based on metamaterial concepts. The considered resonators, namely, split‐ring resonators (SRRs), complementary SRRs (CSRRs), and their open counterparts (OSRRs and OCSRRs), are reviewed, and the equivalent circuit models of artificial lines based on such resonators, including parasitics, are presented and discussed. The second part of the article is devoted to highlight some recent applications of the considered resonators. This will include the design of dual‐band components and wideband bandpass filters based on the combination of OSRRs and OCSRRs, the design of tunable components based on cantilever‐type SRRs, and the design of CSRR‐based differential (balanced) lines with common‐mode suppression. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE 22:439–458, 2012.
DVCFlow: Modeling Information Flow Towards Human-like Video Captioning

Dense video captioning (DVC) aims to generate multi-sentence descriptions to elucidate the multiple events in the video, which is challenging and demands visual consistency, discoursal coherence, and linguistic diversity. Existing methods mainly generate captions from individual video segments, lacking adaptation to the global visual context and progressive alignment between the fast-evolved visual content and textual descriptions, which results in redundant and spliced descriptions. In this paper, we introduce the concept of information flow to model the progressive information changing across video sequence and captions. By designing a Cross-modal Information Flow Alignment mechanism, the visual and textual information flows are captured and aligned, which endows the captioning process with richer context and dynamics on event/topic evolution. Based on the Cross-modal Information Flow Alignment module, we further put forward DVCFlow framework, which consists of a Global-local Visual Encoder to capture both global features and local features for each video segment, and a pre-trained Caption Generator to produce captions. Extensive experiments on the popular ActivityNet Captions and YouCookII datasets demonstrate that our method significantly outperforms competitive baselines, and generates more human-like text according to subject and objective tests.
Solving SDP Faster: A Robust IPM Framework and Efficient Implementation

This paper introduces a new robust interior point method analysis for semidefinite programming (SDP). This new robust analysis can be combined with either logarithmic barrier or hybrid barrier.Under this new framework, we can improve the running time of semidefinite programming (SDP) with variable size $n\times n$ and m constraints up to $\epsilon$ accuracy.We show that for the case $m=\Omega(n^{2})$, we can solve SDPs in $m^{\omega}$ time. This suggests solving SDP is nearly as fast as solving the linear system with equal number of variables and constraints. This is the first result that tall dense SDP can be solved in the nearly-optimal running time, and it also improves the stateof-the-art SDP solver [Jiang, Kathuria, Lee, Padmanabhan and Song, FOCS 2020]. In addition to our new IPM analysis, we also propose a number of techniques that might be of further interest, such as, maintaining the inverse of a Kronecker product using lazy updates, a general amortization scheme for positive semi-definite matrices.
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling

Individual Treatment Effect (ITE) prediction is an important area of research in machine learning which aims at explaining and estimating the causal impact of an action at the granular level. It represents a problem of growing interest in multiple sectors of application such as healthcare, online advertising or socioeconomics. To foster research on this topic we release a publicly available collection of 13.9 million samples collected from several randomized control trials, scaling up previously available datasets by a healthy 210x factor. We provide details on the data collection and perform sanity checks to validate the use of this data for causal inference tasks. First, we formalize the task of uplift modeling (UM) that can be performed with this data, along with the relevant evaluation metrics. Then, we propose synthetic response surfaces and heterogeneous treatment assignment providing a general set-up for ITE prediction. Finally, we report experiments to validate key characteristics of the dataset leveraging its size to evaluate and compare - with high statistical significance - a selection of baseline UM and ITE prediction methods.
Near-Chandrasekhar-mass Type Ia Supernovae from the Double-degenerate Channel

Recent observational evidence has demonstrated that white dwarf (WD) mergers are a highly efficient mechanism for mass accretion onto WDs in the galaxy. In this paper, we show that WD mergers naturally produce highly magnetized, uniformly rotating WDs, including a substantial population within a narrow mass range close to the Chandrasekhar mass (M Ch). These near-M Ch WD mergers subsequently undergo rapid spin up and compression on a ∼ 102 yr timescale, either leading to central ignition and a normal SN Ia via the DDT mechanism, or alternatively to a failed detonation and SN Iax through pure deflagration. The resulting SNe Ia and SNe Iax will have spectra, light curves, polarimetry, and nucleosynthetic yields similar to those predicted to arise through the canonical near-M Ch single degenerate (SD) channel, but with a t −1 delay time distribution characteristic of the double-degenerate channel. Furthermore, in contrast to the SD channel, WD merger near-M Ch SNe Ia and SNe Iax will not produce observable companion signatures. We discuss a range of implications of these findings, from SNe Ia explosion mechanisms, to galactic nucleosynthesis of iron peak elements including manganese.
Deep IDA: A Deep Learning Method for Integrative Discriminant Analysis of Multi-View Data with Feature Ranking–An Application to COVID-19 severity

COVID-19 severity is due to complications from SARS-Cov-2 but the clinical course of the infection varies for individuals, emphasizing the need to better understand the disease at the molecular level. We use clinical and multiple molecular data (or views) obtained from patients with and without COVID-19 who were (or not) admitted to the intensive care unit to shed light on COVID-19 severity. Methods for jointly associating the views and separating the COVID-19 groups (i.e., one-step methods) have focused on linear relationships. The relationships between the views and COVID-19 patient groups, however, are too complex to be understood solely by linear methods. Existing nonlinear one-step methods cannot be used to identify signatures to aid in our understanding of the complexity of the disease. We propose Deep IDA (Integrative Discriminant Analysis) to address analytical challenges in our problem of interest. Deep IDA learns nonlinear projections of two or more views that maximally associate the views and separate the classes in each view, and permits feature ranking for interpretable findings. Our applications demonstrate that Deep IDA has competitive classification rates compared to other state-of-the-art methods and is able to identify molecular signatures that facilitate an understanding of COVID-19 severity.
Optimal Allocation of Water and Sanitation Facilities To Prevent Communicable Diarrheal Diseases in Senegal Under Partial Interference

For several decades, Senegal has faced inadequate water, sanitation, and hygiene (WASH) facilities in households, contributing to persistent, high levels of communicable diarrheal diseases. Unfortunately, the ideal WASH policy where every household in Senegal installs WASH facilities is impossible due to logistical and budgetary concerns. This work proposes to estimate an optimal allocation rule of WASH facilities in Senegal by combining recent advances in personalized medicine and partial interference in causal inference. Our allocation rule helps public health officials in Senegal decide what fraction of total households in a region should get WASH facilities based on block-level and household-level characteristics. We characterize the excess risk of the allocation rule and show that our rule outperforms other allocation policies in Senegal.
Rail-to-rail fully differential sample and hold based on differential difference amplifier

A high-performance rail-to-rail sample and hold circuit is presented. It is based on a class AB fully differential rail-to-rail differential difference amplifier. Experimental results of a test chip in 0.5 mum CMOS technology show that it has SFDR = 69.5 dB with plusmn 1.25 V supplies, 2 MHz clock and 2V pp , 200 kHz input signals.
Enhanced countering adversarial attacks via input denoising and feature restoring

Despite the fact that deep neural networks (DNNs) have achieved prominent performance in various applications, it is well known that DNNs are vulnerable to adversarial examples/samples (AEs) with imperceptible perturbations in clean/original samples. To overcome the weakness of the existing defense methods against adversarial attacks, which damages the information on the original samples, leading to the decrease of the target classifier accuracy, this paper presents an enhanced countering adversarial attack method IDFR (via Input Denoising and Feature Restoring). The proposed IDFR is made up of an enhanced input denoiser (ID) and a hidden lossy feature restorer (FR) based on the convex hull optimization. Extensive experiments conducted on benchmark datasets show that the proposed IDFR outperforms the various state-of-the-art defense methods, and is highly effective for protecting target models against various adversarial black-box or white-box attacks. \footnote{Souce code is released at: \href{https://github.com/ID-FR/IDFR}{https://github.com/ID-FR/IDFR}}
Automatic texture inspection in the classification of papers and cloths with neural networks method

Purpose – The purpose of this paper is to present an automatic inspection method of colors and textures classification of paper and cloth objects.Design/methodology/approach – In this system, the color image is transformed from RGB model to other suitable color model with one of the components being chosen as the gray‐level image for extracting textures. The gray‐level image is decomposed into four child images using wavelet transformation. Two child images capable of detecting variations along columns and rows are used to generate 0° and 90° co‐occurrence matrices, respectively. Some of the distinguishable texture features are derived from the two co‐occurrence matrixes. Finally, the test image is classified using neural networks. Nine color papers and eight color cloths are used to test the developed classification method.Findings – The results show that recognition rate higher than 97.86 percent can be achieved if color and texture features are both used as the inputs to the networks.Originality/value .