Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy-Preserving Teacher-Student Deep Reinforcement Learning

Deep reinforcement learning agents may learn complex tasks more efﬁciently when they coordinate with one another. We consider a teacher-student coordination scheme wherein an agent may ask another agent for demonstrations. Despite the beneﬁts of sharing demonstrations, however, potential adversaries may obtain sensitive information belonging to the teacher by observing the demonstrations. In particular, deep reinforcement learning algorithms are known to be vulnerable to membership attacks, which make accurate inferences about the membership of the entries of training datasets. Therefore, there is a need to safeguard the teacher against such privacy threats. We ﬁx the teacher’s policy as the context of the demonstrations, which allows for different internal models across the student and the teacher, and contrasts the existing methods. We make the following two contributions. (i) We develop a differentially private mechanism that protects the privacy of the teacher’s training dataset. (ii) We propose a proximal policy-optimization objective that enables the student to beneﬁt from the demonstrations despite the perturbations of the privacy mechanism. We empirically show that the algorithm improves the student’s learning upon convergence rate and utility. Speciﬁcally, compared with an agent who learns the same task on its own, we observe that the student’s policy converges faster, and the converging policy accumulates higher rewards more robustly.
Speech enhancement with weakly labelled data from AudioSet

Speech enhancement is a task to improve the intelligibility and perceptual quality of degraded speech signal. Recently, neural networks based methods have been applied to speech enhancement. However, many neural network based methods require noisy and clean speech pairs for training. We propose a speech enhancement framework that can be trained with large-scale weakly labelled AudioSet dataset. Weakly labelled data only contain audio tags of audio clips, but not the onset or offset times of speech. We first apply pretrained audio neural networks (PANNs) to detect anchor segments that contain speech or sound events in audio clips. Then, we randomly mix two detected anchor segments containing speech and sound events as a mixture, and build a conditional source separation network using PANNs predictions as soft conditions for speech enhancement. In inference, we input a noisy speech signal with the one-hot encoding of"Speech"as a condition to the trained system to predict enhanced speech. Our system achieves a PESQ of 2.28 and an SSNR of 8.75 dB on the VoiceBank-DEMAND dataset, outperforming the previous SEGAN system of 2.16 and 7.73 dB respectively.
Meeting Effectiveness and Inclusiveness in Remote Collaboration

A primary goal of remote collaboration tools is to provide effective and inclusive meetings for all participants. To study meeting effectiveness and meeting inclusiveness, we first conducted a large-scale email survey (N=4,425; after filtering N=3,290) at a large technology company (pre-COVID-19); using this data we derived a multivariate model of meeting effectiveness and show how it correlates with meeting inclusiveness, participation, and feeling comfortable to contribute. We believe this is the first such model of meeting effectiveness and inclusiveness. The large size of the data provided the opportunity to analyze correlations that are specific to sub-populations such as the impact of video. The model shows the following factors are correlated with inclusiveness, effectiveness, participation, and feeling comfortable to contribute in meetings: sending a pre-meeting communication, sending a post-meeting summary, including a meeting agenda, attendee location, remote-only meeting, audio/video quality and reliability, video usage, and meeting size. The model and survey results give a quantitative understanding of how and where to improve meeting effectiveness and inclusiveness and what the potential returns are. Motivated by the email survey results, we implemented a post-meeting survey into a leading computer-mediated communication (CMC) system to directly measure meeting effectiveness and inclusiveness (during COVID-19). Using initial results based on internal flighting we created a similar model of effectiveness and inclusiveness, with many of the same findings as the email survey. This shows a method of measuring and understanding these metrics which are both practical and useful in a commercial CMC system. By improving meeting effectiveness, companies can save significant time and money. Improving meeting inclusiveness is hypothesized to improve meeting effectiveness, but also improves the working environment and employee retention at organizations.
Simple Combinatorial Algorithms for the Minimum Dominating Set Problem in Bounded Arboricity Graphs

We revisit the minimum dominating set problem on graphs with arboricity bounded by α . In the (standard) centralized setting, Bansal and Umboh [BU17] gave an O ( α ) -approximation LP rounding algorithm. Moreover, [BU17] showed that it is NP-hard to achieve an asymptotic improvement. On the other hand, the previous two non-LP-based algorithms, by Lenzen and Wattenhofer [LW10], and Jones et al. [JLR + 13], achieve an approximation factor of O ( α 2 ) in linear time. There is a similar situation in the distributed setting: While there are poly log n -round LP-based O ( α ) -approximation algorithms [KMW06, DKM19], the best non-LP-based algorithm by Lenzen and Wattenhofer [LW10] is an implementation of their centralized algorithm, providingan O ( α 2 ) -approximation within O (log n ) rounds with high probability; they also gave a faster deterministic algorithm but with a worse approximation guarantee. We address the question of whether one can achieve a simple, elementary O ( α ) -approximation algo-rithm not based on any LP-based methods, either in the centralized setting or in the distributed setting. We resolve these questions in the afﬁrmative. More speciﬁcally, our contribution is two-fold: 1. In the centralized setting, we provide a surprisingly simple combinatorial algorithm that is asymptotically optimal in terms of both approximation factor and running time: an O ( α ) -approximation in linear time. 2. Based on our centralized algorithm, we design a distributed combinatorial O ( α ) -approximation algorithm in the CONGEST model that runs in O ( α log n ) rounds with high probability. Our round complexity outperforms the best LP-based distributed algorithm for a wide range of parameters
How Decentralized is the Governance of Blockchain-based Finance: Empirical Evidence from four Governance Token Distributions

Novel blockchain technology provides the infrastructure layer for the creation of decentralized appli-cations. A rapidly growing ecosystem of applications is built around financial services, commonly referred to as decentralized finance. Whereas the intangible concept of decentralization is presented as a key driver for the applications, defining and measuring decentralization is multifaceted. This pa-per provides a framework to quantify decentralization of governance power among blockchain appli-cations. Governance of the applications is increasingly important and requires striking a balance be-tween broad distribution, fostering user activity, and financial incentives. Therefore, we aggregate, parse, and analyze empirical data of four finance applications calculating coefficients for the statistical dispersion of the governance token distribution. The gauges potentially support IS scholars for an objective evaluation of the capabilities and limitations of token governance and for fast iteration in design-driven governance mechanisms.
Continual Learning for Blind Image Quality Assessment

The explosive growth of image data facilitates the fast development of image processing and computer vision methods for emerging visual applications, meanwhile introducing novel distortions to processed images. This poses a grand challenge to existing blind image quality assessment (BIQA) models, which are weak at adapting to subpopulation shift. Recent work suggests training BIQA methods on the combination of all available human-rated IQA datasets. However, this type of approach is not scalable to a large number of datasets and is cumbersome to incorporate a newly created dataset as well. In this paper, we formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data. We first identify five desiderata in the continual setting with three criteria to quantify the prediction accuracy, plasticity, and stability, respectively. We then propose a simple yet effective continual learning method for BIQA. Specifically, based on a shared backbone network, we add a prediction head for a new dataset and enforce a regularizer to allow all prediction heads to evolve with new data while being resistant to catastrophic forgetting of old data. We compute the overall quality score by a weighted summation of predictions from all heads. Extensive experiments demonstrate the promise of the proposed continual learning method in comparison to standard training techniques for BIQA, with and without experience replay. We made the code publicly available at https://github.com/zwx8981/BIQA_CL.
PCaaD: Towards Automated Determination and Exploitation of Industrial Processes

Over the last decade, Programmable Logic Controllers (PLCs) have been increasingly targeted by attackers to obtain control over industrial processes that support critical services. Such targeted attacks typically require detailed knowledge of system-specific attributes, including hardware configurations, adopted protocols, and PLC control-logic, i.e. process comprehension. The consensus from both academics and practitioners suggests stealthy process comprehension obtained from a PLC alone, to conduct targeted attacks, is impractical. In contrast, we assert that current PLC programming practices open the door to a new vulnerability class based on control-logic constructs. To support this, we propose the concept of Process Comprehension at a Distance (PCaaD), as a novel methodological and automatable approach for system-agnostic exploitation of PLC library functions, leading to the targeted exfiltration of operational data, manipulation of control-logic behavior, and establishment of covert command and control channels through unused memory. We validate PCaaD on widely used PLCs, by identification of practical attacks.
Trends in Vehicle Re-identification Past, Present, and Future: A Comprehensive Review

Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches targeted vehicle over non-overlapping views in multiple camera network. However, it becomes more difficult due to inter-class similarity, intra-class variability, viewpoint changes, and spatio-temporal uncertainty. In order to draw a detailed picture of vehicle re-id research, this paper gives a comprehensive description of the various vehicle re-id technologies, applicability, datasets, and a brief comparison of different methodologies. Our paper specifically focuses on vision-based vehicle re-id approaches, including vehicle appearance, license plate, and spatio-temporal characteristics. In addition, we explore the main challenges as well as a variety of applications in different domains. Lastly, a detailed comparison of current state-of-the-art methods performances over VeRi-776 and VehicleID datasets is summarized with future directions. We aim to facilitate future research by reviewing the work being done on vehicle re-id till to date.
Training cascaded networks for speeded decisions using a temporal-difference loss

Although deep feedforward neural networks share some characteristics with the primate visual sys-tem, a key distinction is their dynamics. Deep nets typically operate in sequential stages wherein each layer fully completes its computation before processing begins in subsequent layers. In contrast, biological systems have cascaded dynamics: information propagates from neurons at all layers in parallel but transmission is gradual over time. In our work, we construct a cascaded ResNet by introducing a propagation delay into each residual block and updating all layers in parallel in a stateful manner. Because information transmitted through skip connections avoids delays, the functional depth of the architecture increases over time and yields a trade off between processing speed and accuracy. We introduce a temporal-difference (TD) training loss that achieves a strictly superior speed-accuracy proﬁle over standard losses. The C ASCADED TD model has intriguing properties, including: typical instances are classiﬁed more rapidly than atypical instances; C ASCAD - ED TD is more robust to both persistent and transient noise than is a conventional ResNet; and the time-varying output trace of C ASCADED TD provides a signal that can be used by ‘meta-cognitive’ models for OOD detection and to determine when to terminate processing.
Revisiting thermodynamics in computation and information theory

One of the primary motivations of the research in the field of computation is to optimize the cost of computation. The major ingredient that a computer needs is the energy to run a process, i.e., the thermodynamic cost. The analysis of the thermodynamic cost of computation is one of the prime focuses of research. It started back since the seminal work of Landauer where it was commented that the computer spends kB T ln2 amount of energy to erase a bit of information (here T is the temperature of the system and kB represents the Boltzmann's constant). The advancement of statistical mechanics has provided us the necessary tool to understand and analyze the thermodynamic cost for the complicated processes that exist in nature, even the computation of modern computers. The advancement of physics has helped us to understand the connection of the statistical mechanics (the thermodynamics cost) with computation. Another important factor that remains a matter of concern in the field of computer science is the error correction of the error that occurs while transmitting the information through a communication channel. Here in this article, we have reviewed the progress of the thermodynamics of computation starting from Landauer's principle to the latest model, which simulates the modern complex computation mechanism. After exploring the salient parts of computation in computer science theory and information theory, we have reviewed the thermodynamic cost of computation and error correction. We have also discussed about the alternative computation models that have been proposed with thermodynamically cost-efficient.
Effective and Efficient Vote Attack on Capsule Networks

Standard Convolutional Neural Networks (CNNs) can be easily fooled by images with small quasi-imperceptible artificial perturbations. As alternatives to CNNs, the recently proposed Capsule Networks (CapsNets) are shown to be more robust to white-box attacks than CNNs under popular attack protocols. Besides, the class-conditional reconstruction part of CapsNets is also used to detect adversarial examples. In this work, we investigate the adversarial robustness of CapsNets, especially how the inner workings of CapsNets change when the output capsules are attacked. The first observation is that adversarial examples misled CapsNets by manipulating the votes from primary capsules. Another observation is the high computational cost, when we directly apply multi-step attack methods designed for CNNs to attack CapsNets, due to the computationally expensive routing mechanism. Motivated by these two observations, we propose a novel vote attack where we attack votes of CapsNets directly. Our vote attack is not only effective but also efficient by circumventing the routing process. Furthermore, we integrate our vote attack into the detection-aware attack paradigm, which can successfully bypass the class-conditional reconstruction based detection method. Extensive experiments demonstrate the superior attack performance of our vote attack on CapsNets.
Viscosity controls humidity dependence of N2O5 uptake to citric acid aerosol

Abstract. The heterogeneous loss of dinitrogen pentoxide (N2O5) to aerosol particles has a significant impact on the night-time nitrogen oxide cycle and therefore the oxidative capacity in the troposphere. Using a 13N short-lived radioactive tracer method, we studied the uptake kinetics of N2O5 on citric acid aerosol particles as a function of relative humidity (RH). The results show that citric acid exhibits lower reactivity than similar dicarboxylic and polycarboxylic acids, with uptake coefficients between ∼ 3 × 10-4–∼ 3 × 10-3 depending on humidity (17–70 % RH). At RH above 50 %, the magnitude and the humidity dependence can be best explained by the viscosity of citric acid as compared to aqueous solutions of simpler organic and inorganic solutes and the variation of viscosity with RH and, hence, diffusivity in the organic matrix. Since the diffusion rates of N2O5 in highly concentrated citric acid solutions are not well established, we present four different parameterizations of N2O5 diffusivity based on the available literature data or estimates for viscosity and diffusivity of H2O. Above 50 % RH, uptake is consistent with the reacto-diffusive kinetic regime whereas below 50 % RH, the uptake coefficient is higher than expected from hydrolysis of N2O5 within the bulk of the particles, and the uptake kinetics is most likely limited by loss on the surface only. This study demonstrates the impact of viscosity in highly oxidized and highly functionalized secondary organic aerosol material on the heterogeneous chemistry of N2O5 and may explain some of the unexpectedly low loss rates to aerosol derived from field studies.
Maximum permissible and negligible concentrations for metals and metalloids in the Netherlands, taking into account background concentrations.

Abstract The aim of the paper is to provide interested parties the methods that were used for generic hazard assessment in The Netherlands, and the resulting so-called Maximum Permissible Concentrations (MPCs) and the Negligible Concentrations (NCs) for 18 metals and metalloids. The MPCs and NCs were derived for water, sediment, soil and air. The concentration in the environment above which the risk of adverse effects was considered unacceptable to ecosystems is called the MPC. The MPCs take into account that the substances are distributed among the different environmental compartments, and are harmonised accordingly. Included in the MPC and NC are existing background concentrations in The Netherlands following the so-called ‘added risk approach’. The MPCs served as a basis for the Dutch government to set generic Environmental Quality Standards (EQS) in The Netherlands. EQS in turn are used by the Dutch Government to assess the environmental quality and for other environmental policy purposes. Depending on the number of ecotoxicological data, the reliability of the MPCs differed. For water only, MPCs were based on sufficient data, but for the other compartments, there is a great demand for further ecotoxicity data. For soil and sediment, almost no ecotoxicological data were available, and MPCs for those compartments have, in many cases, been derived from MPCs in water applying a modified equilibrium partitioning method (modified EqP-method), resulting in MPCs with greater uncertainty. Some of the methods and underlying assumptions that have been used may warrant further discussion. For example, the lack of data, the factor 100 between MPC and NC, the background concentration, the essential elements, the modified EqP-method and bioavailability, speciation and water chemistry, acid volatile sulfide, and the method that is used for harmonising MPCs for air. Interested parties may use the information and methods on the MPCs and NCs as a general guideline for deriving generic environmental quality standards.
Patient prioritisation methods to shorten waiting times for elective surgery: A systematic review of how to improve access to surgery

ABSTRACT Background: Concern about long waiting times for elective surgeries is not a recent phenomenon, but it has been heightened by the impact of the COVID-19 pandemic and its associated measures. One way to alleviate the problem might be to use prioritisation methods for patients on the waiting list and a wide range of research is available on such methods. However, significant variations and inconsistencies have been reported in prioritisation protocols from various specialties, institutions, and health systems. To bridge the evidence gap in existing literature, this comprehensive systematic review will synthesise global evidence on policy strategies with a unique insight to patient prioritisation methods to reduce waiting times for elective surgeries. This will provide evidence that might help with the tremendous burden of surgical disease that is now apparent in many countries because of operations that were delayed or cancelled due to the COVID-19 pandemic and inform policy for sustainable healthcare management systems. Methods: We searched PubMed, EMBASE, SCOPUS, Web of Science, and the Cochrane Library, with our most recent searches in January 2020. Articles published after 2013 on major elective surgery lists of adult patients were eligible, but cancer and cancer-related surgeries were excluded. Both randomised and non-randomised studies were eligible and the quality of studies was assessed with ROBINS-I and CASP tools. We registered the review in PROSPERO (CRD42019158455) and reported it in accordance with the PRISMA statement. Results: The electronic search in five bibliographic databases yielded 7543 records (PubMed, EMBASE, SCOPUS, Web of Science, and Cochrane) and 17 eligible articles were identified in the screening. There were four quasi-experimental studies, 11 observational studies and two systematic reviews. These demonstrated a moderate to low risk of bias in their research methods. Three studies tested generic approaches using common prioritisation systems for all elective surgeries in common. The other studies assessed specific prioritisation approaches for re-ordering the waiting list for a particular surgical specialty. Conclusions: Explicit prioritisation tools with a standardised scoring system based on clear evidence-based criteria are likely to reduce waiting times and improve equitable access to health care. Multiple attributes need to be considered in defining a fair prioritisation system to overcome limitations with local variations and discriminations. Collating evidence from a diverse body of research provides a single framework to improve the quality and efficiency of elective surgical care provision in a variety of health settings. Universal prioritisation tools with vertical and horizontal equity would help with re-ordering patients on waiting lists for elective surgery and reduce waiting times. Keywords: Patient prioritisation, elective surgery, waiting time, systematic review
Metal slanted columnar thin film THz optical sensors

We demonstrate that the anisotropic optical response of metal (cobalt) slanted columnar thin films (STF) at THz frequencies strongly depends on the dielectric properties of the dielectric ambient surrounding the slanted columnar thin films. An effective medium dielectric function approach is used to describe the combined optical response of metal slanted columnar thin film and dielectric ambient. Our observations indicate that metal (cobalt) slanted columnar thin films can be used as sensors which will enable detection and characterization of minute amounts of dielectrics at THz frequencies, such as for flow-based detection of liquid chemical constituents.