Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Affine Sampling: Ambiguity-Free and Efficient Sparse Phase Retrieval

Conventional sparse phase retrieval schemes can recover sparse signals from the magnitude of linear measurements only up to a global phase ambiguity. This work proposes a novel approach that instead utilizes the magnitude of affine measurements to achieve ambiguity-free signal reconstruction. The proposed method relies on two-stage approach that consists of support identification followed by the exact recovery of nonzero signal entries. In the noise-free case, perfect support identification using a simple counting rule is guaranteed subject to a mild condition on the signal sparsity, and subsequent exact recovery of the nonzero signal entries can be obtained in closed-form. The proposed approach is then extended to two noisy scenarios, namely, sparse noise (or outliers) and non-sparse bounded noise. For both cases, perfect support identification is still ensured under mild conditions on the noise model, namely, the support size for sparse outliers and the power of the bounded noise. Under perfect support identification, exact signal recovery can be achieved using a simple majority rule for the sparse noise scenario, and reconstruction up to a bounded error can be achieved using linear least-squares (LS) estimation for the non-sparse bounded noise scenario. The obtained analytic performance guarantee for the latter case also sheds light on the construction of the sensing matrix and bias vector. In fact, we show that a near optimal performance can be achieved with high probability by the random generation of the nonzero entries of the sparse sensing matrix and bias vector according to the uniform distribution over a circle. Computer simulations using both synthetic and real-world data sets are provided to demonstrate the effectiveness of the proposed scheme.
Deconfounded Recommendation for Alleviating Bias Amplification

Recommender systems usually amplify the biases in the data. The model learned from historical interactions with imbalanced item distribution will amplify the imbalance by over-recommending items from the majority groups. Addressing this issue is essential for a healthy ecosystem of recommendation in the long run. Existing work applies bias control to the ranking targets (e.g., calibration, fairness, and diversity), but ignores the true reason for bias amplification and trades off the recommendation accuracy. In this work, we scrutinize the cause-effect factors for bias amplification, identifying the main reason lies in the confounding effect of imbalanced item distribution on user representation and prediction score. The existence of such confounder pushes us to go beyond merely modeling the conditional probability and embrace the causal modeling for recommendation. Towards this end, we propose a Deconfounded Recommender System (DecRS), which models the causal effect of user representation on the prediction score. The key to eliminating the impact of the confounder lies in backdoor adjustment, which is however difficult to do due to the infinite sample space of the confounder. For this challenge, we contribute an approximation operator for backdoor adjustment which can be easily plugged into most recommender models. Lastly, we devise an inference strategy to dynamically regulate backdoor adjustment according to user status. We instantiate DecRS on two representative models FM [32] and NFM [16], and conduct extensive experiments over two benchmarks to validate the superiority of our proposed DecRS.
A Polynomial-time, Truthful, Individually Rational and Budget Balanced Ridesharing Mechanism

Ridesharing has great potential to improve transportation efficiency while reducing congestion and pollution. To realize this potential, mechanisms are needed that allocate vehicles optimally and provide the right incentives to riders. However, many existing approaches consider restricted settings (e.g., only one rider per vehicle or a common origin for all riders). Moreover, naive applications of standard approaches, such as the Vickrey-Clarke-Groves or greedy mechanisms, cannot achieve a polynomial-time, truthful, individually rational and budget balanced mechanism. To address this, we formulate a general ridesharing problem and apply mechanism design to develop a novel mechanism which satisfies all four properties and whose social cost is within 8.6% of the optimal on average.
Principled information fusion for multi-view multi-agent surveillance systems

A key objective of multi-agent surveillance systems is to monitor a much larger region than the limited field-of-view (FoV) of any individual agent by successfully exploiting cooperation among multi-view agents. Whenever either a centralized or a distributed approach is pursued, this goal cannot be achieved unless an appropriately designed fusion strategy is adopted. This paper presents a novel principled information fusion approach for dealing with multi-view multi-agent case, on the basis of Generalized Covariance Intersection (GCI). The proposed method can be used to perform multi-object tracking on both a centralized and a distributed peer-to-peer sensor network. Simulation experiments on realistic multi-object tracking scenarios demonstrate effectiveness of the proposed solution.
Continual World: A Robotic Benchmark For Continual Reinforcement Learning

Continual learning (CL) -- the ability to continuously learn, building on previously acquired knowledge -- is a natural requirement for long-lived autonomous reinforcement learning (RL) agents. While building such agents, one needs to balance opposing desiderata, such as constraints on capacity and compute, the ability to not catastrophically forget, and to exhibit positive transfer on new tasks. Understanding the right trade-off is conceptually and computationally challenging, which we argue has led the community to overly focus on catastrophic forgetting. In response to these issues, we advocate for the need to prioritize forward transfer and propose Continual World, a benchmark consisting of realistic and meaningfully diverse robotic tasks built on top of Meta-World as a testbed. Following an in-depth empirical evaluation of existing CL methods, we pinpoint their limitations and highlight unique algorithmic challenges in the RL setting. Our benchmark aims to provide a meaningful and computationally inexpensive challenge for the community and thus help better understand the performance of existing and future solutions. Information about the benchmark, including the open-source code, is available at https://sites.google. com/view/continualworld.
Finite Blocklength Secrecy Analysis of Polar and Reed-Muller Codes in BEC Semi-Deterministic Wiretap Channels

We consider a semi-deterministic wiretap channel where the main channel is noiseless and the eavesdropper’s channel is a binary erasure channel (BEC). We provide a lower bound for the achievable secrecy rates of polar and Reed-Muller codes, and compare it to the second order coding rate. To the best of our knowledge, this is the first work which demonstrates the secrecy performance of polar and Reed-Muller codes in short blocklengths. The results show that under a total variation secrecy metric, Reed-Muller codes can achieve secrecy rates very close to the second order approximation rate. On the other hand, we observe a significant gap between the lower bound for the achievable rates of polar codes and the the second order approximation rate for short blocklengths.
Face Anonymization by Manipulating Decoupled Identity Representation

Privacy protection on human biological information has drawn increasing attention in recent years, among which face anonymization plays an importance role. We pro-pose a novel approach which protects identity information of facial images from leakage with slightest modiﬁ-cation. Speciﬁcally, we disentangle identity representation from other facial attributes leveraging the power of generative adversarial networks trained on a conditional multi-scale reconstruction (CMR) loss and an identity loss. We evaulate the disentangle ability of our model, and pro-pose an effective method for identity anonymization, namely Anonymous Identity Generation (AIG), to reach the goal of face anonymization meanwhile maintaining similarity to the original image as much as possible. Quantitative and qualitative results demonstrate our method’s superiority compared with the SOTAs on both visual quality and anonymization success rate.
An Algorithm for Reordering Buffer Management Problem and Experimental Evaluations on Discrete Distributions

In the reordering buffer management problem, a sequence of requests must be executed by a service station, where a cost occurs for each pair of consecutive requests with different attributes. A reordering buffer management algorithm aims to permute the input sequence using the buffer to minimize the total cost. Reordering buffers has many potential applications in computer sciences and economics. In this article, we proved the minimum buffer length for the optimal solution to the reordering buffer management problem in the offline setting. With the assumption that color selection is always made when the buffer is full, selecting the most frequent color from the buffer given the smallest buffer size $k$ that satisfies either $o_1<2 \cdot \lceil \frac{k}{\sigma} \rceil$ OR $o_2<\lceil \frac{k}{\sigma} \rceil$ guarantees the optimal solution, where $o_1$ and $o_2$ represent respectively the frequency of the most and the second most frequent colors in the input sequence $\mathcal{X}$, and $\sigma$ is the number of distinct colors appearing in $\mathcal{X}$. We proposed a new algorithm for the online setting of the problem that uses the results of the proof made on the minimum buffer length required for the optimal solution. Moreover, we presented the results of the first experimental setup that uses input sequences following discrete distributions to evaluate the performance of algorithms. Out of 432 cases, the new algorithm showed the best performance in 409 cases that is approximately $95\%$ of all cases.
Dissecting Click Fraud Autonomy in the Wild

Although the use of pay-per-click mechanisms stimulates the prosperity of the mobile advertisement network, fraudulent ad clicks result in huge financial losses for advertisers. Extensive studies identify click fraud according to click/traffic patterns based on dynamic analysis. However, in this study, we identify a novel click fraud, named humanoid attack, which can circumvent existing detection schemes by generating fraudulent clicks with similar patterns to normal clicks. We implement the first tool ClickScanner to detect humanoid attacks on Android apps based on static analysis and variational AutoEncoders (VAEs) with limited knowledge of fraudulent examples. We define novel features to characterize the patterns of humanoid attacks in the apps' bytecode level. ClickScanner builds a data dependency graph (DDG) based on static analysis to extract these key features and form a feature vector. We then propose a classification model only trained on benign datasets to overcome the limited knowledge of humanoid attacks. We leverage ClickScanner to conduct the first large-scale measurement on app markets (i.e., 120,000 apps from Google Play and Huawei AppGallery) and reveal several unprecedented phenomena. First, even for the top-rated 20,000 apps, ClickScanner still identifies 157 apps as fraudulent, which shows the prevalence of humanoid attacks. Second, it is observed that the ad SDK-based attack (i.e., the fraudulent codes are in the third-party ad SDKs) is now a dominant attack approach. Third, the manner of attack is notably different across apps of various categories and popularities. Finally, we notice there are several existing variants of the humanoid attack. Additionally, our measurements demonstrate the proposed ClickScanner is accurate and time-efficient (i.e., the detection overhead is only 15.35% of those of existing schemes).
How They Tweet? An Insightful Analysis of Twitter Handles of Saudi Arabia

The emergence of social network site has attracted many users across the world to share their feeling, news, achievements and personal thoughts over several platforms. The recent crisis due to worldwide lockdown amid COVID 19 has shown how these online social platforms have grown stronger and turned up as the major source of connection among people when there is social distancing everywhere. Therefore, we have surveyed Twitter users and their mannerism with respect to languages, frequency of tweets, the region of belonging, etc. The above observations have been considered especially with respect to Saudi Arabia. An insightful analysis of the tweets and twitter handles of the kingdom has been presented. The results show some interesting facts that are envisaged to lay a platform for further research in the field of social, political and data sciences related to the Middle East.
The relationship between economic growth and environment. Testing the EKC hypothesis for Latin American countries

We employ an ARDL bounds testing approach to cointegration and Unrestricted Error Correction Models (UECMs) to estimate the relationship between income and CO2 emissions per capita in 21 Latin American Countries (LACs) over 1960-2017. Using time series we estimate six different specifications of the model to take into account the independent effect on CO2 emissions per capita of different factors considered as drivers of different dynamics of CO2 emissions along the development path. This approach allows to address two concerns. First, the estimation of the model controlling for different variables serves to assess if the EKC hypothesis is supported by evidence in any of the LACs considered and to evaluate if this evidence is robust to different model specifications. Second, the inclusion of control variables accounting for the effect on CO2 emissions is directed at increasing our understanding of CO2 emissions drivers in different countries. The EKC hypothesis effectively describes the long term income-emissions relationship only in a minority of LACs and, in many cases, the effect on CO2 emissions of different factors depends on the individual country experience and on the type and quantity of environmental policies adopted. Overall, these results call for increased environmental action in the region.
The Early Bird Catches the Worm: Better Early Life Cycle Defect Predictors

—Before researchers rush to reason across all available data, they should ﬁrst check if the information is densest within some small region. We say this since, in 240 GitHub projects, we ﬁnd that the information in that data “clumps” towards the earliest parts of the project. In fact, a defect prediction model learned from just the ﬁrst 150 commits works as well, or better than state-of-the-art alternatives. Using just this early life cycle data, we can build models very quickly (using weeks, not months, of CPU time). Also, we can ﬁnd simple models (with just two features) that generalize to hundreds of software projects. Based on this experience, we warn that prior work on generalizing software engineering defect prediction models may have needlessly complicated an inherently simple process. Further, prior work that focused on later-life cycle data now needs to be revisited since their conclusions were drawn from relatively uninformative regions.
Learning Baseline Values for Shapley Values

This paper aims to formulate the problem of estimating the optimal baseline values for the Shapley value in game theory. The Shapley value measures the attribution of each input variable of a complex model, which is computed as the marginal benefit from the presence of this variable w.r. t. its absence under different contexts. To this end, people usually set the input variable to its baseline value to represent the absence of this variable (i.e. the no-signal state of this variable). Previous studies usually determine the baseline values in an empirical manner, which hurts the trustworthiness of the Shapley value. In this paper, we revisit the feature representation of a deep model from the perspective of game theory, and define the multi-variate interaction patterns of input variables to define the no-signal state of an input variable. Based on the multi-variate interaction, we learn the optimal baseline value of each input variable. Experimental results have demonstrated the effectiveness of our method.
Modified Graviton Dynamics From Spin Foams: The Area Regge Action

A number of approaches to 4D quantum gravity, such as holography and loop quantum gravity, propose areas instead of lengths as fundamental variables. The Area Regge action, which can be defined for general 4D triangulations, is a natural choice for an action based on areas. It does indeed appear in the semi-classical limit of spin foam models. The Area Regge action does however only lead to a discrete version of the gravitational equations of motion, if one implements constraints, that ensure that the areas are compatible with a consistent length assignment to the edges of the triangulation. The constrained version is then classically equivalent to the Length Regge action, which provides a discretization of the Einstein-Hilbert action. Here we perform the first systematic analysis of the Area Regge dynamics on a hyper-cubical lattice. Surprisingly, we find that the linearized Area Regge action on a hyper-cubical lattice does single out the Length Regge action by its scaling behaviour in the lattice constant. That is, integrating out the variables describing fluctuations in the area-length constraints one finds the linearized Length Regge action plus terms of higher order in the lattice constant. This appears without any explicit implementation of the area-length constraints.
Towards Knowledge Organization Ecosystems

It is needless to mention the (already established) overarching importance of knowledge organization and its tried-and-tested high-quality schemes in knowledge-based Artificial Intelligence (AI) systems. But equally, it is also hard to ignore that, increasingly, standalone KOSs are becoming functionally ineffective components for such systems, given their inability to capture the continuous facetization and drift of domains. The paper proposes a radical re-conceptualization of KOSs as a first step to solve such an inability, and, accordingly, contributes in the form of the following dimensions: (i) an explicit characterization of Knowledge Organization Ecosystems (KOEs) (possibly for the first time) and their positioning as pivotal components in realizing sustainable knowledge-based AI solutions, (ii) as a consequence of such a novel characterization, a first examination and characterization of KOEs as Socio-Technical Systems (STSs), thus opening up an entirely new stream of research in knowledge-based AI, and (iii) motivating KOEs not to be mere STSs but STSs which are grounded in Ethics and Responsible Artificial Intelligence cardinals from their very genesis. The paper grounds the above contributions in relevant research literature in a distributed fashion throughout the paper, and finally concludes by outlining the future research possibilities.
Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning

Reinforcement learning (RL) is an effective technique for training decision-making agents through interactions with their environment. The advent of deep learning has been associated with highly notable successes with sequential decision making problems — such as defeating some of the highest-ranked human players at Go. In digital advertising, real-time bidding (RTB) is a common method of allocating advertising inventory through real-time auctions. Bidding strategies need to incorporate logic for dynamically adjusting parameters in order to deliver pre-assigned campaign goals. Here we discuss techniques toward using RL to train bidding agents. As a campaign metric we particularly focused on viewability: the percentage of inventory which goes on to be viewed by an end user. This paper is presented as a survey of techniques and experiments which we developed through the course of this research. We discuss expanding our training data to include edge cases by training on simulated interactions. We discuss the experimental results comparing the performance of several promising RL algorithms, and an approach to hyperparameter optimization of an actor/critic training pipeline through Bayesian optimization. Finally, we present live-traffic tests of some of our RL agents against a rulebased feedback-control approach, demonstrating the potential for this method as well as areas for further improvement. This paper therefore presents an arrangement of our findings in this quickly developing field, and ways that it can be applied to an RTB use case.
Regenerative Copper–Alumina H2S Sorbent for Hot Gas Cleaning through Chemical Swing Adsorption

This article focuses on desulfurization, of hot syngas from gasification of solid fossil fuels, in the temperature range of 300–500 °C via copper-based adsorbents. The slip of H2S above the developed adsorbent materials for hot cleaning of syngas has been studied together with the regeneration mechanism, using thermodynamic analysis, thermogravimetry, and packed-bed reactor experiments, in order to establish an efficient approach to regenerate the adsorbent. Supported copper on gamma alumina used as H2S adsorbent in this study shows H2S slips lower than 5 ppm in the temperature range of 350–550 °C. The copper-based sorbent shows around 2 wt % sulfur sorption capacity in the temperature range of study. The kinetic evaluation confirms that the sorption kinetics for this sorbent yield sufficient performance for real process operation even at such low temperatures. Aiming at isothermal operation, the chemical swing process is identified as an efficient way to regenerate the adsorbent. In this regeneration pro.
Light-curve classification with recurrent neural networks for GOTO: dealing with imbalanced data

The advent of wide-field sky surveys has led to the growth of transient and variable source discoveries. The data deluge produced by these surveys has necessitated the use of machine learning (ML) and deep learning (DL) algorithms to sift through the vast incoming data stream. A problem that arises in real-world applications of learning algorithms for classification is imbalanced data, where a class of objects within the data is underrepresented, leading to a bias for overrepresented classes in the ML and DL classifiers. We present a recurrent neural network (RNN) classifier that takes in photometric time-series data and additional contextual information (such as distance to nearby galaxies and on-sky position) to produce real-time classification of objects observed by the Gravitational-wave Optical Transient Observer, and use an algorithm-level approach for handling imbalance with a focal loss function. The classifier is able to achieve an Area Under the Curve (AUC) score of 0.972 when using all available photometric observations to classify variable stars, supernovae, and active galactic nuclei. The RNN architecture allows us to classify incomplete light curves, and measure how performance improves as more observations are included. We also investigate the role that contextual information plays in producing reliable object classification.