Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers

Accurate detection of lane and road markings is a task of great importance for intelligent vehicles. In existing approaches, the detection accuracy often degrades with the increasing distance. This is due to the fact that distant lane and road markings occupy a small number of pixels in the image, and scales of lane and road markings are inconsistent at various distances and perspectives. The Inverse Perspective Mapping (IPM) can be used to eliminate the perspective distortion, but the inherent interpolation can lead to artifacts especially around distant lane and road markings and thus has a negative impact on the accuracy of lane marking detection and segmentation. To solve this problem, we adopt the Encoder-Decoder architecture in Fully Convolutional Networks and leverage the idea of Spatial Transformer Networks to introduce a novel semantic segmentation neural network. This approach decomposes the IPM process into multiple consecutive differentiable homography transform layers, which are called “Perspective Transformer Layers Furthermore, the interpolated feature map is refined by subsequent convolutional layers” thus reducing the artifacts and improving the accuracy. The effectiveness of the proposed method in lane marking detection is validated on two public datasets: TuSimple and ApolloScape.
Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. Existing works have mostly focused on either digital adversarial examples created via small and imperceptible perturbations, or physical-world adversarial examples created with large and less realistic distortions that are easily identified by human observers. In this paper, we propose a novel approach, called Adversarial Camouflage (\emph{AdvCam}), to craft and camouflage physical-world adversarial examples into natural styles that appear legitimate to human observers. Specifically, \emph{AdvCam} transfers large adversarial perturbations into customized styles, which are then “hidden” on-target object or off-target background. Experimental evaluation shows that, in both digital and physical-world scenarios, adversarial examples crafted by \emph{AdvCam} are well camouflaged and highly stealthy, while remaining effective in fooling state-of-the-art DNN image classifiers. Hence, \emph{AdvCam} is a flexible approach that can help craft stealthy attacks to evaluate the robustness of DNNs.
AQPDBJUT Dataset: Picture-Based PM Monitoring in the Campus of BJUT

Ensuring the students in good physical levels is imperative for their future health. In recent years, the continually growing concentration of Particulate Matter (PM) has done increasingly serious harm to student health. Hence, it is highly required to prevent and control PM concentrations in the campus. As the source of PM prevention and control, developing a good model for PM monitoring is extremely urgent and has posed a big challenge. It has been found in prior works that photobased methods are available for PM monitoring. To verify the effectiveness of existing PM monitoring methods in the campus, we establish a new dataset which includes 1,500 photos collected in the Beijing University of Technology. Experiments show that stated-of-the-art methods are far from ideal for PM monitoring in the campus.
Semi-analytic approximate stability selection for correlated data in generalized linear models

We consider the variable selection problem of generalized linear models (GLMs). Stability selection (SS) is a promising method proposed for solving this problem. Although SS provides practical variable selection criteria, it is computationally demanding because it needs to fit GLMs to many re-sampled datasets. We propose a novel approximate inference algorithm that can conduct SS without the repeated fitting. The algorithm is based on the replica method of statistical mechanics and vector approximate message passing of information theory. For datasets characterized by rotation-invariant matrix ensembles, we derive state evolution equations that macroscopically describe the dynamics of the proposed algorithm. We also show that their fixed points are consistent with the replica symmetric solution obtained by the replica method. Numerical experiments indicate that the algorithm exhibits fast convergence and high approximation accuracy for both synthetic and real-world data.
Stochastic Virtual Battery Modeling of Uncertain Electrical Loads Using Variational Autoencoder*

Effective utilization of flexible loads for grid services, while satisfying end-user preferences and constraints, requires an accurate estimation of the aggregated predictive flexibility offered by the electrical loads. Virtual battery (VB) models are often used to quantify the predictive flexibility in thermostatic loads (e.g. residential air-conditioners, electric water-heaters), which model the temporal evolution of a (virtual) energy state via a first order dynamics including self-dissipation rate, and power and energy capacities as parameters. Uncertainties and lack of information regarding end-usage and equipment models render deterministic VB models impractical. In this paper, we introduce the notion of stochastic VB models, and propose a variational autoencoder-based deep learning algorithm to identify the probability distribution of the VB model parameters. Using available sensors and meters data, the proposed algorithm generates not only point estimates of the VB parameters, but also confidence intervals around those values. Effectiveness of the proposed frameworks is demonstrated on a collection of electric water-heater loads, whose operation is driven by uncertain water usage profiles.
Approximately Supermodular Scheduling Subject to Matroid Constraints

Control scheduling refers to the problem of assigning agents or actuators to act upon a dynamical system at specific times so as to minimize a quadratic control cost, such as the objectives of the linear-quadratic-Gaussian (LQG) or linear quadratic regulator problems. When budget or operational constraints are imposed on the schedule, this problem is in general NP-hard and its solution can therefore only be approximated even for moderately sized systems. The quality of this approximation depends on the structure of both the constraints and the objective. This article shows that greedy control scheduling is near-optimal when the constraints can be written as an intersection of matroids, algebraic structures that encode requirements such as limits on the number of agents deployed per time slot, total number of actuator uses, and duty cycle restrictions. To do so, it proves that the LQG cost function is <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>-supermodular and provides new <inline-formula><tex-math notation="LaTeX">$\alpha /(\alpha + P)$</tex-math></inline-formula>-optimality certificates for the greedy minimization of such functions over an intersection of <inline-formula><tex-math notation="LaTeX">$P$</tex-math></inline-formula> matroids. These certificates are shown to approach the <inline-formula><tex-math notation="LaTeX">$1/(1+P)$</tex-math></inline-formula> guarantee of supermodular functions in relevant settings. These results support the use of greedy algorithms in nonsupermodular quadratic control problems as opposed to typical heuristics such as convex relaxations and surrogate figures of merit, e.g., the <inline-formula><tex-math notation="LaTeX">$\log \det$</tex-math></inline-formula> of the controllability Gramian.
Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives

Visuomotor control (VMC) is an effective means of achieving basic manipulation tasks such as pushing or pick- and-place from raw images. Conditioning VMC on desired goal states is a promising way of achieving versatile skill primitives. However, common conditioning schemes either rely on task-specific fine tuning - e.g. using one-shot imitation learning (IL) - or on sampling approaches using a forward model of scene dynamics i.e. model-predictive control (MPC), leaving deployability and planning horizon severely limited. In this paper we propose a conditioning scheme which avoids these pitfalls by learning the controller and its conditioning in an end-to-end manner. Our model predicts complex action sequences based directly on a dynamic image representation of the robot motion and the distance to a given target observation. In contrast to related works, this enables our approach to efficiently perform complex manipulation tasks from raw image observations without predefined control primitives or test time demonstrations. We report significant improvements in task success over representative MPC and IL baselines. We also demonstrate our model's generalisation capabilities in challenging, unseen tasks featuring visual noise, cluttered scenes and unseen object geometries.
Predicting Performance of Asynchronous Differentially-Private Learning

We consider training machine learning models using Training data located on multiple private and geographically-scattered servers with different privacy settings. Due to the distributed nature of the data, communicating with all collaborating private data owners simultaneously may prove challenging or altogether impossible. In this paper, we develop differentially-private asynchronous algorithms for collaboratively training machine-learning models on multiple private datasets. The asynchronous nature of the algorithms implies that a central learner interacts with the private data owners one-on-one whenever they are available for communication without needing to aggregate query responses to construct gradients of the entire fitness function. Therefore, the algorithm efficiently scales to many data owners. We define the cost of privacy as the difference between the fitness of a privacy-preserving machine-learning model and the fitness of trained machine-learning model in the absence of privacy concerns. We prove that we can forecast the performance of the proposed privacy-preserving asynchronous algorithms. We demonstrate that the cost of privacy has an upper bound that is inversely proportional to the combined size of the training datasets squared and the sum of the privacy budgets squared. We validate the theoretical results with experiments on financial and medical datasets. The experiments illustrate that collaboration among more than 10 data owners with at least 10,000 records with privacy budgets greater than or equal to 1 results in a superior machine-learning model in comparison to a model trained in isolation on only one of the datasets, illustrating the value of collaboration and the cost of the privacy. The number of the collaborating datasets can be lowered if the privacy budget is higher.
DISCOVERY OF PROCESS MODEL USING CLICK STREAM ANALYSIS IN WEB MINING

: Web mining uses the data mining techniques to automatically discover and extract information (Knowledge) from Web documents and services. The method of general web access pattern is extracted and analyzed using knowledge discovery techniques to understand the patterns. This usage mining can be done from the click stream data of the website by the users. Sequence of tasks are observed through click stream analysis and a business process model may be discovered in web structure mining. This paper suggests a plan and proposal about how to consider the user behavior in E commerce sites as process and to discover a process model which enhances business intelligence.
Quantum image classifier with single photons.

Machine learning, with promising applications in quantum computation, has been introduced to a variety of quantum mechanical platforms, where its interplay with quantum physics offers exciting prospects toward quantum advantages. A central difficulty, however, lies in the access and control of the large Hilbert space required by quantum machine learning protocols, through the limited number of noisy qubits available to near-term quantum devices. Whereas it is recognized that a viable solution lies in the design of quantum algorithms that incorporates quantum entanglement and interference, a demonstration of quantum machine learning protocols capable of solving practical tasks is still lacking. Here we report the classification of real-life, hand-drawn images on a quantum mechanical platform of single photons. Adopting a tensor-network-based machine learning algorithm with an entanglement-guided optimization, we achieve an efficient representation of the quantum feature space using matrix product states. This allows us to demonstrate image classification with a high success rate using single-photon interferometry networks. Our experiment establishes a general and scalable framework for quantum machine learning, which is readily accessible on other physical platforms.
Foreign Banks, Liquidity Shocks, and Credit Stability

This paper investigates whether foreign banks help mitigate the effects of domestic liquidity shocks by exploiting a policy-induced shock to the U.S. wholesale market for liquidity and matched bank-syndicated loan data. We find that, following the 2011 Federal Deposit Insurance Corporation (FDIC) regulatory change to the cost of wholesale liquidity, foreign banks, which faced a relatively positive liquidity shock, accumulated more reserves by engaging in liquidity hoarding, but did not expand their lending. These responses are more pronounced for foreign banks affiliated with complex global bank holding companies and whose parent banking systems experienced distress at the moment of the shock.
DEMAND-BASED CONTROL OF HVAC SYSTEM FOR INDOOR AIR QUALITY AND THERMAL COMFORT

People spend most of our time in indoors. In indoors the health of the occupant is mostly affected by the indoor air quality (IAQ) and thermal condition. IAQ can be affected by microbial contaminants and gaseous pollutants. Since generation of carbon dioxide (CO2) often increases its concentrations to uncomfortable level, an occupant inside room affects mostly CO2 concentration level out of other IAQ parameters. Changes in the thermal condition inside room is mostly due to seasonal changes, however thermal comfort inside room can be achieved by desired level of temperature and relative humidity.Usually for controlling indoor air quality (IAQ) and thermal comfort inside buildings a costly heating, ventilation and air conditioning (HVAC) system is used. To achieve these comfort conditions inside single room it is not feasible to install this costly HVAC system, although to partially achieve these comfort conditions different components operating independently were used but individual operation ofthese components can’t achieve desired comfort conditions.In this paper,PC based automatic virtual instrumentation system was designed to integrate working of individual components to achieve the comfort conditions inside a room. The three indoor parameters (i.e. CO2 concentration, temperature and relative humidity) were monitored in a room online using sensors and data acquisition device and controlled through the application software implemented on LabVIEW platform. A fuzzy controller wasdesigned and implemented to control CO2 concentration and temperature and relative humidity was controlled by ON-OFF controller. Control signals were issued to vary the exhaust fan ventilation rate, heating or cooling power of the heating or cooling system installed in the room to provide desired comfort conditions along with saving of electrical energy.
Complete genome sequence of the bacteriophage YMC/09/04/ R1988 MRSA BP: a lytic phage from a methicillin-resistant Staphylococcus aureus isolate

Methicillin-resistant Staphylococcus aureus (MRSA) is an increasing cause of serious infection, both in the community and hospital settings. Despite sophis-ticated strategies and efforts, the antibiotic options for treating MRSA infection have been narrowed due to the limited number of newly developed antimicro-bials. Herein, we analyze the completely sequenced genome of a novel virulent phage YMC/09/04/R1988 MRSA BP as a potential alternative anti-MRSA agent, which lysed clinical isolates from a patient admitted to the hospital due to hip disarticulation. The phage contains a linear double-stranded DNA genome of 44,459 bp in length, with 33.37% GC content, 62 predicted open reading frames (ORFs), and annotated functions of only 23 ORFs that are associated with structural assembly, host lysis, DNA replication, and modiﬁcation. It showed a broad host range (17 of 30 strains) against MRSA strains in clinical isolates.
Neural Fuzzy Extractors: A Secure Way to Use Artificial Neural Networks for Biometric User Authentication

Powered by new advances in sensor development and artificial intelligence, the decreasing cost of computation, and the pervasiveness of handheld computation devices, biometric user authentication (and identification) is rapidly becoming ubiquitous. Modern approaches to biometric authentication, based on sophisticated machine learning techniques, cannot avoid storing either trained-classifier details or explicit user biometric data, thus exposing users’ credentials to falsification. In this paper, we introduce a secure way to handle user-specific information involved with the use of artificial neural networks for biometric authentication. Our proposed architecture, called a Neural Fuzzy Extractor (NFE), allows the coupling of pre-existing classifiers with fuzzy extractors, through an artificial-neuralnetwork-based buffer called an expander, with minimal or no performance degradation. The NFE thus offers all the performance advantages of modern deep-learningbased classifiers and all the security of standard fuzzy extractors. We demonstrate the NFE retrofit of a few classic artificial neural networks, for simple biometric authentication scenarios.
Excitation based cone-beam X-ray luminescence tomography of nanophosphors with different concentrations

Cone-beam X-ray luminescence computed tomography (CB-XLCT), which has been proposed as a new molecular imaging modality recently, can obtain both anatomical and functional tomographic images of an object efficiently, with the excitation of nanophosphors in vivo or in vitro by cone-beam X-rays. However, the ill-posedness of the CB-XLCT inverse problem degrades the image quality and makes it difficult to resolve adjacent luminescence targets with different concentrations, which is essential in drug delivery and treatment monitoring in vivo. To address this problem, a multi-voltage excitation imaging scheme combined with principal component analysis is proposed in this study. Imaging experiments performed on physical phantoms by a custom-made CB-XLCT system demonstrate that two adjacent targets, with the concentration differences of 50 and 100 mg/ml and an edge-toedge distance of 0 mm, can be effectively resolved. Keywords—image reconstruction techniques; optical tomography;X-ray imaging; principal component analysis
Securing safe havens for the Helmeted Hornbill Rhinoplax vigil

The plight of the Critically Endangered Helmeted Hornbill Rhinoplax vigil needs no introduction to birdwatchers and ornithologists with an interest in Asian birds. Despite having a wide distribution spanning five countries—Brunei, Indonesia (Sumatra, Kalimantan), Malaysia (Peninsular, Sabah and Sarawak), Myanmar and Thailand (BirdLife International 2018)—this bird has suffered one of the fastest declines of any avian species in Asia in recent years, with the possible exception of Gyps vultures and the Straw-headed Bulbul Pycnonotus zeylanicus. The causes have been very specific—the escalation in the trade of its solid reddish-orange casque (Collar 2015, Beastall et al. 2016, Bale 2018) superimposed on the well-established problem of degradation and destruction of its forest habitat. In 2015, Yok Yok Hadiprakarsa of the hornbill conservation organisation Rangkong Indonesia revealed the unprecedented and unsustainable levels of poaching from Kalimantan and brought the crisis to the attention of the international conservation community (Hii 2015). This was followed by a BirdingASIA article on Helmeted Hornbill (Collar 2015). Since then, bird conservationists have collaborated and worked hard to raise awareness of the issue, set up the IUCN Species Survival Commission (SSC) Helmeted Hornbill Working Group (a subgroup of the IUCN SSC Hornbill Specialist Group), and intensified national and international conservation efforts. Here we highlight some of the recent collaborative efforts on Helmeted Hornbill conservation by the BirdLife International Partnership, Planet Indonesia, Gaia, Wildlife Reserves Singapore and others, with the aim of inviting further collaboration and to synergise actions with other conservation efforts, including those for large mammals. Given the urgency of the issue, it is vital for concerted conservation effort to be implemented immediately.
Guide ( Version 2 . 1 ) Guideline for Documenting Instruments in the Open Access Repository for Measurement Instruments ( ZIS )

2 2. Instrument . 2 Instruction . 2 Items . 2 Response specifications . 2 Scoring.2 Application field . 3 3. Theory . 3 4. Scale development . 4 Item generation and selection . 4 Samples . 5 Item analyses . 6 Item parameter . 7 5. Quality criteria . 7 Objectivity . 7 Reliability . 8 Validity . 8 Descriptive statistics (scaling) . 9 Further quality criteria . 9 6. Literature and data sources . 10 Further literature . 10 Data sources . 10 Acknowledgements . 10 Contact details . 10 References. 10 Appendix . 14 A1: Exploratory factor analysis . 14 A2: Structural equation modeling . 16 A3: Reliability estimators . 17 A4: Types of validity . 18 A5: Measurement invariance levels . 19 A6: Norming sample . 22 Nießen, Groskurth, Schmidt, & Bluemke: ZIS Publication Guide 2.1 2 Zusammenstellung sozialwissenschaftlicher Items und Skalen (ZIS): www.gesis. org/en/zis The Documentation of Measurement Instruments in ZIS For publishing your instrument, use the template (English) to ease the documentation and publication process. Any publication in ZIS is structured in six sections: overview, instrument, theoretical background, scale development, quality criteria, and literature and data sources. Due to the consistent format, users can compare different instruments easily and quickly. This section follows the structure laid out in the template and includes information to help you optimally design your documentation using the guidelines of the German Council for Social and Economic Data (Rat für Sozialund Wirtschaftsdaten; RatSWD, 2014). Likewise, it is helpful to browse ZIS to familiarize yourself with the structure and scope of the publications.