Miracle-dz/UniFAR-FTUs huggingface dataset:
RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions

Segmenting each moving object instance in a scene is essential for many applications. But like many other computer vision tasks, this task performs well in optimal weather, but then adverse weather tends to fail. To be robust in weather conditions, the usual way is to train network in data of given weather pattern or to fuse multiple sensors. We focus on a new possibility, that is, to improve its resilience to weather interference through the network's structural design. First, we propose a novel FPN structure called RiWFPN with a progressive top-down interaction and attention refinement module. RiWFPN can directly replace other FPN structures to improve the robustness of the network in non-optimal weather conditions. Then we extend SOLOV2 to capture temporal information in video to learn motion information, and propose a moving object instance segmentation network with RiWFPN called RiWNet. Finally, in order to verify the effect of moving instance segmentation in different weather disturbances, we propose a VKTTI-moving dataset which is a moving instance segmentation dataset based on the VKTTI dataset, taking into account different weather scenes such as rain, fog, sunset, morning as well as overcast. The experiment proves how RiWFPN improves the network's resilience to adverse weather effects compared to other FPN structures. We compare RiWNet to several other state-of-the-art methods in some challenging datasets, and RiWNet shows better performance especially under adverse weather conditions.
Statistical Privacy Guarantees of Machine Learning Preprocessing Techniques

Differential privacy provides strong privacy guarantees for machine learning applications. Much recent work has been focused on developing differentially private models, however there has been a gap in other stages of the machine learning pipeline, in particular during the preprocessing phase. Our contributions are twofold: we adapt a privacy violation detection framework based on statistical methods to empirically measure privacy levels of machine learning pipelines, and apply the newly created framework to show that resampling techniques used when dealing with imbalanced datasets cause the resultant model to leak more privacy. These results highlight the need for developing private preprocessing techniques.
Fast Image-Anomaly Mitigation for Autonomous Mobile Robots

Camera anomalies like rain or dust can severely degrade image quality and its related tasks, such as localization and segmentation. In this work we address this important issue by implementing a pre-processing step that can effectively mitigate such artifacts in a real-time fashion, thus supporting the deployment of autonomous systems with limited compute capabilities. We propose a shallow generator with aggregation, trained in an adversarial setting to solve the ill-posed problem of reconstructing the occluded regions. We add an enhancer to further preserve high-frequency details and image colorization. We also produce one of the largest publicly available datasets1 to train our architecture and use realistic synthetic raindrops to obtain an improved initialization of the model. We benchmark our framework on existing datasets and on our own images obtaining state-of-the-art results while enabling real-time performance, with up to 40x faster inference time than existing approaches.
Stretching your data with taffy filters

Popular approximate membership query structures such as Bloom filters and cuckoo filters are widely used in databases, security, and networking. These structures represent sets approximately, and support at least two operations—insert and lookup; lookup always returns true on elements in the structure; it also returns true with some probability ε$$ \varepsilon $$ on elements not in the structure. These latter elements are called false positives. Compensatory for these false positives, filters can be much smaller than hash tables that represent the same set. However, unlike hash tables, cuckoo filters, and Bloom filters must be initialized with the intended number of maximum entries, and cannot grow larger—inserts beyond this number fail or significantly increase the false positive probability. This article presents designs and implementations of filters than can grow without inserts failing and without meaningfully increasing the false positive probability, even if the filters are created with a small initial size. The resulting code is available on GitHub under a permissive open source license.
A duality approach to a price formation MFG model

where Q, uT , and m0 are given functions, m0 is a probability measure on R, and the triplet (u,m,̟) is the unknown. Here, the state of a typical agent is the variable x ∈ R and represents the assets of that agent. The distribution of assets in the population of the agents at time t is encoded in the probability measure m(·, t). The agents change their assets by trading at a price ̟(t). The trading is subject to a balance condition encoded in the third equation in (1.1). This integral constraint that guarantees supply Q(t) meets demand is represented by the term on the left-hand side of that condition. As introduced in [36], u is the value function of an agent who trades a commodity with supply Q and price ̟. The function u is characterized by the first equation in (1.1) and the terminal condition in (1.2). Each agent selects their trading rate in order to minimize a given cost functional (see (1.7) below). The optimal control selection is −Hp(x,̟(t) + ux(t, x)). Under this optimal control, the density m describing the population of agents evolves according to the second equation in (1.1) and the initial condition in (1.2). The third equation in (1.1), which we refer to as the balance condition, is an integral constraint that guarantees supply meets demand.
Experimental violations of Leggett-Garg inequalities on a quantum computer

Leggett-Garg’s inequalities predict sharp bounds for some classical correlation functions that address the quantum or classical nature of real-time evolutions. We experimentally observe the violations of these bounds on singleand multi-qubit systems, in different settings, exploiting the IBM Quantum platform. In the multi-qubit case we introduce the Leggett-Garg-Bell’s inequalities as an alternative to the previous ones. Measuring these correlation functions, we find quantum error mitigation to be essential to spot inequalities violations. Accessing only two qubit readouts, we assess Leggett-Garg-Bell’s inequalities to emerge as the most efficient quantum coherence witnesses to be used for investigating quantum hardware, as the complexity of their calculation does not scale with the number of constituents of the system. Our analysis highlights the limits of nowadays quantum platforms, showing that the above-mentioned correlation functions deviate from theoretical prediction as the number of qubits and the depth of the circuit grow.
Estimation of individual causal effects in observational longitudinal studies

Causal effect sizes may vary among individuals and they can even be of opposite directions. When there exists serious effect heterogeneity, the population average causal effect (ACE) is not very informative. It is well-known that individual causal effects (ICEs) cannot be determined in cross-sectional studies, but we will show that ICEs can be retrieved from longitudinal data under certain conditions. We will present a general framework for individual causality where we will view effect heterogeneity as an individual-specific effect modification that can be parameterized with a latent variable, the receptiveness factor. The distribution of the receptiveness factor can be retrieved, and it will enable us to study the contrast of the potential outcomes of an individual under stationarity assumptions. Within the framework, we will study the joint distribution of the individual’s potential outcomes conditioned on all individual’s factual data and subsequently the distribution of the cross-world causal effect (CWCE). We discuss conditions such that the latter converges to a degenerated distribution, in which case the ICE can be estimated consistently. To demonstrate the use of this general framework, we present examples in which the outcome process can be parameterized as a (generalized) linear mixed model. Keywords— Individual causal effect; causal inference; longitudinal data; structural causal model; latent variable model; heterogeneity of treatment effect.
On Mass Spectra of Primordial Black Holes

Evidence for the primordial black holes (PBH) presence in the early Universe renews permanently. New limits on their mass spectrum challenge existing models of PBH formation. One of the known models is based on the closed walls collapse after the inflationary epoch. Its intrinsic feature is the multiple production of small mass PBH which might contradict observations in the nearest future. We show that the mechanism of walls collapse can be applied to produce substantially different PBH mass spectra if one takes into account the classical motion of scalar fields together with their quantum fluctuations at the inflationary stage. Analytical formulas have been developed that contain both quantum and classical contributions.
Incentives for Collective Innovation

Agents exert hidden effort to produce randomly-sized innovations in a technology they share. Returns from using the technology grow as it develops, but so does the opportunity cost of effort, due to an 'exploration-exploitation' trade-off. As monitoring is imperfect, there exists a unique (strongly) symmetric equilibrium, and effort in any equilibrium ceases no later than in the single-agent problem. Small innovations may hurt all agents in the symmetric equilibrium, as they severely reduce effort. Allowing agents to discard innovations increases effort and payoffs, preserving uniqueness. Under natural conditions, payoffs rise above those of all equilibria with forced disclosure.
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser

Considering the importance of building a good Visual Dialog (VD) Questioner, many researchers study the topic under a Q-Bot-A-Bot image-guessing game setting, where the Questioner needs to raise a series of questions to collect information of an undisclosed image. Despite progress has been made in Supervised Learning (SL) and Reinforcement Learning (RL), issues still exist. Firstly, previous methods do not provide explicit and effective guidance for Questioner to generate visually related and informative questions. Secondly, the effect of RL is hampered by an incompetent component, i.e., the Guesser, who makes image predictions based on the generated dialogs and assigns rewards accordingly. To enhance VD Questioner: 1) we propose a Related entity enhanced Questioner (ReeQ) that generates questions under the guidance of related entities and learns entity-based questioning strategy from human dialogs; 2) we propose an Augmented Guesser (AugG) that is strong and is optimized for the VD setting especially. Experimental results on the VisDial v1.0 dataset show that our approach achieves state-of-theart performance on both image-guessing task and question diversity. Human study further proves that our model generates more visually related, informative and coherent questions.
Robust Control Barrier Functions With Sector-Bounded Uncertainties

This letter focuses on safety-critical control with sector-bounded uncertainties at the plant input. The uncertainties can represent nonlinear and/or time-varying components. We propose a new robust control barrier function (RCBF) approach to enforce safety requirements in the presence of these uncertainties. The primary objective is to minimally alter the given baseline control command to guarantee safety in the presence of modeled uncertainty. The resulting min-norm optimization problem can be recast as a Second-Order Cone Program (SOCP) to enable online implementation. Properties of this controller are studied, and a numerical example is provided to illustrate the effectiveness of this approach.
Exploiting Spatial-Temporal Semantic Consistency for Video Scene Parsing

Compared with image scene parsing, video scene parsing introduces temporal information, which can effectively improve the consistency and accuracy of prediction. In this paper, we propose a Spatial-Temporal Semantic Consistency method to capture class-exclusive context information. Specifically, we design a spatial-temporal consistency loss to constrain the semantic consistency in spatial and temporal dimensions. In addition, we adopt an pseudo-labeling strategy to enrich the training dataset. We obtain the scores of 59.84% and 58.85% mIoU on development (test part 1) and testing set of VSPW, respectively. And our method wins the 1st place on VSPW challenge at ICCV2021.
Large-scale flow in a cubic Rayleigh–Bénard cell: long-term turbulence statistics and Markovianity of macrostate transitions

We investigate the large-scale circulation (LSC) in a turbulent Rayleigh-Bénard convection flow in a cubic closed convection cell by means of direct numerical simulations at a Rayleigh number Ra = 106. The numerical studies are conducted for single flow trajectories up to 105 convective free-fall times to obtain a sufficient sampling of the four discrete LSC states, which can be summarized to one macrostate, and the two crossover configurations which are taken by the flow in between for short periods. We find that large-scale dynamics depends strongly on the Prandtl number Pr of the fluid which has values of 0.1, 0.7, and 10. Alternatively, we run an ensemble of 3600 short-term direct numerical simulations to study the transition probabilities between the discrete LSC states. This second approach is also used to probe the Markov property of the dynamics. Our ensemble analysis gave strong indication of Markovianity of the transition process from one LSC state to another, even though the data are still accompanied by considerable noise. It is based on the eigenvalue spectrum of the transition probability matrix, further on the distribution of persistence times and the joint distribution of two successive microstate persistence times. This article is part of the theme issue ‘Mathematical problems in physical fluid dynamics (part 1)’.
LoRa-RL: Deep Reinforcement Learning for Resource Management in Hybrid Energy LoRa Wireless Networks

LoRa wireless networks are considered as a key enabling technology for next-generation Internet of Things (IoT) systems. New IoT deployments (e.g., smart city scenarios) can have thousands of devices per square kilometer leading to huge amount of power consumption to provide connectivity. In this article, we investigate green LoRa wireless networks powered by a hybrid of the grid and renewable energy sources, which can benefit from harvested energy while dealing with the intermittent supply. This article proposes resource management schemes of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway energy efficiency. First, the problem of grid power consumption minimization while satisfying the system’s quality of service demands is formulated. Specifically, both scenarios the uncorrelated and time-correlated channels are investigated. The optimal resource management problem is solved by decoupling the formulated problem into two subproblems: 1) channel and SF assignment problem and 2) energy management problem. Since the optimal solution is obtained with high complexity, online resource management heuristic algorithms that minimize the grid energy consumption are proposed. Finally, taking into account the channel and energy correlation, adaptable resource management schemes based on reinforcement learning (RL) are developed. Simulation results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.
You should evaluate your language model on marginal likelihood over tokenisations

Neural language models typically tokenise input text into sub-word units to achieve an open vocabulary. The standard approach is to use a single canonical tokenisation at both train and test time. We suggest that this approach is unsatisfactory and may bottleneck our evaluation of language model performance. Using only the one-best tokenisation ignores tokeniser uncertainty over alternative tokenisations, which may hurt model out-of-domain performance. In this paper, we argue that instead, language models should be evaluated on their marginal likelihood over tokenisations. We compare different estimators for the marginal likelihood based on sampling, and show that it is feasible to estimate the marginal likelihood with a manageable number of samples. We then evaluate a pretrained language model on both the one-best-tokenisation and marginal perplexities, and show that the marginal perplexity can be significantly better than the one best, especially on out-of-domain data. We link this difference in perplexity to the tokeniser uncertainty as measured by tokeniser entropy. We discuss some implications of our results for language model training and evaluation, particularly with regard to tokenisation robustness.
Ultrasound‐Assisted Anti‐Solvent Crystallization of Telmisartan Using Dimethyl Sulfoxide as Organic Solvent

In recent years, ultrasound assisted anti‐solvent crystallization has emerged as an excellent approach to reduce the particle size of poorly water‐soluble drugs, thereby achieving improvement in the solubility and bioavailability. In the present study, this concept is employed for telmisartan by studying various critical parameters like the effect of sonication time, sonication amplitude, drug solution concentration, solvent to anti‐solvent ratio, injection rate and crystallization temperature. The particle size of the commercial telmisartan is reduced from 10 µm to 200 nm by anti‐solvent crystallization method by applying 60% ultrasound amplitude for 15 min with 10 mg mL−1 drug concentration, 1:3 of solvent to anti‐solvent ratio, and 10 mL min−1 of injection rate at 30 °C. Physicochemical characteristics; morphology, crystalline structure, the presence of other chemical compounds, enthalpy change, and the melting point of a recovered drug are analyzed using various analytical tools. It is possible to achieve nanoparticles of telmisartan by symbiotic effect of sonication with anti‐solvent crystallization.
Influence of NaMo App on Twitter

Social media plays a crucial role in today's society. It results in paradigm changes in how people relate and communicate, convey and exchange ideas. Moreover, social media has evolved into critical knowledge networks for consumers and also affects decision-making. In elections, social media became an integral part of political campaigning to reach a greater audience and gather more support. The 2019 Lok Sabha election saw a massive spike in the usage of online social media platforms such as Twitter, Facebook, and WhatsApp; with every major political party launching its own organized social media campaigns. In 2014, Bhartiya Janta Party (BJP) took one step ahead in organizing the campaign by launching its app - NaMo App. We focus our research on Twitter and NaMo App during the 2019 Lok Sabha elections and CAA protests. Twitter is a platform where every individual can express their views and is not biased. In contrast, NaMo App is one of the first apps centered around a specific political party. It acted as a digital medium for BJP for organizing the political campaign to make people's opinion in their favour. This research aims to characterize the role of the NaMo App in a more traditional network as Twitter in shaping political discourse and studies the existence of an online echo chamber. We began by analyzing the amount and type of content shared using the NaMo App on Twitter. We performed content and network analysis for the existence of the echo chamber. We also applied Hawkes process to see the influence that NaMo App has on Twitter. Through this research, we can conclude that the users who share content using NaMo App, may be part of an online echo chamber and are likely to be BJP workers. We show the reach and influence that the NaMo App has on Twitter is significantly less, indicating its inability to break through the diverse audience and change the narrative on Twitter.
Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources

1. The occurrence and distributions of wildlife populations and communities are shifting as a result of global changes. To evaluate whether these shifts are negatively impacting biodiversity processes, it is critical to monitor the status, trends and effects of environmental variables on entire communities. However, modelling the dynamics of multiple species simultaneously can require large amounts of diverse data, and few modelling approaches exist to simultaneously provide species and community‐level inferences.
EsmamDS: A more diverse exceptional survival model mining approach

A variety of works in the literature strive to uncover the factors associated with survival behaviour. However, the computational tools to provide such information are global models designed to predict if or when a (survival) event will occur. When approaching the problem of explaining differences in survival behaviour, those approaches rely on (assumptions of) predictive features followed by risk stratification. In other words, they lack the ability to discover new information on factors related to survival. In contrast, we approach such a problem from the perspective of descriptive supervised pattern mining to discover local patterns associated with different survival behaviours. Hence, we introduce the EsmamDS algorithm: an Exceptional Model Mining framework to provide straightforward characterisations of subgroups presenting unusual survival models -- given by the Kaplan-Meier estimates. This work builds on the Esmam algorithm to address the problem of pattern redundancy and provide a more informative and diverse characterisation of survival behaviour.
Communication Efficient Tensor Factorization for Decentralized Healthcare Networks

Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional Electronic Health Records (EHRs) with patients history of medical procedures, medications, diagnosis, lab tests, etc., are converted to meaningful and interpretable medical concepts. Federated tensor factorization distributes the tensor computation to multiple workers under the coordination of a central server, which enables jointly learning the phenotypes across multiple hospitals while preserving the privacy of the patient information. However, existing federated tensor factorization algorithms encounter the single-point-failure issue with the involvement of the central server, which is not only easily exposed to external attacks, but also limits the number of clients sharing information with the server under restricted uplink bandwidth. In this paper, we propose CiderTF, a communication-efficient decentralized generalized tensor factorization, which reduces the uplink communication cost by leveraging a four-level communication reduction strategy designed for a generalized tensor factorization, which has the flexibility of modeling different tensor distribution with multiple kinds of loss functions. Experiments on two real-world EHR datasets demonstrate that CiderTF achieves comparable convergence with the communication reduction up to 99.99%.