Miracle-dz/UniFAR-FTUs huggingface dataset:
Robot Learning of Shifting Objects for Grasping in Cluttered Environments

Robotic grasping in cluttered environments is often infeasible due to obstacles preventing possible grasps. Then, pre-grasping manipulation like shifting or pushing an object becomes necessary. We developed an algorithm that can learn, in addition to grasping, to shift objects in such a way that their grasp probability increases. Our research contribution is threefold: First, we present an algorithm for learning the optimal pose of manipulation primitives like clamping or shifting. Second, we learn non-prehensible actions that explicitly increase the grasping probability. Making one skill (shifting) directly dependent on another (grasping) removes the need of sparse rewards, leading to more data-efficient learning. Third, we apply a real-world solution to the industrial task of bin picking, resulting in the ability to empty bins completely. The system is trained in a self-supervised manner with around 25 000 grasp and 2500 shift actions. Our robot is able to grasp and file objects with 274±3 picks per hour. Furthermore, we demonstrate the system’s ability to generalize to novel objects.
Applications of Compressive Sampling Technique to Radar and Localization

During the last decade, the emerging technique of compressive sampling (CS) has become a popular subject in signal processing and sensor systems. In particular, CS breaks through the limits imposed by the Nyquist sampling theory and is able to substantially reduce the huge amount of data generated by different sources. The technique of CS has been successfully applied in signal acquisition, image compression, and data reduction. Although the theory of CS has been investigated for some radar and localization problems, several important questions have not been answered yet. For example, the perfor- mance of CS radar in a cluttered environment has not been comprehensively studied. Applying CS to passive radars and electronic warfare receivers is another concern that needs more attention. Also, it is well known that applying this strategy leads to extra computational costs which might be prohibitive in large-sized localization networks. In this chapter, we first discuss the practical issues in the process of implementing CS radars and localization systems. Then, we present some promising and efficient solutions to overcome the arising problems.
Error estimates for optimal control problems of the Stokes system with Dirac measures

The aim of this work is to derive a priori error estimates for control-constrained optimal control problems that involve the Stokes system and Dirac measures. The first problem entails the minimization of a cost functional that involves point evaluations of the velocity field that solves the state equations. This leads to an adjoint problem with a linear combination of Dirac measures as a forcing term and whose solution exhibits reduced regularity properties. The second problem involves a control variable that corresponds to the amplitude of forces modeled as point sources. This leads to a solution of the state equations with reduced regularity properties. For each problem, we propose a solution technique and derive error estimates. Finally, we present numerical experiments in two and three dimensional domains.
Carbon emissions from tropical forest degradation caused by logging

The focus of land-use related efforts in developing countries to reduce carbon emissions has been on slowing deforestation, yet international agreements are to reduce emissions from both deforestation and forest degradation (REDD). The second ‘D’ is poorly understood and accounted for a number of technical and policy reasons. Here we introduce a complete accounting method for estimating emission factors from selective timber harvesting, a substantial form of forest degradation in many tropical developing countries. The method accounts separately for emissions from the extracted log, from incidental damage to the surrounding forest, and from logging infrastructure, and emissions are expressed as units of carbon per cubic meter of timber extracted to allow for simple application to timber harvesting statistics. We applied the method in six tropical countries (Belize, Bolivia, Brazil, Guyana, Indonesia, and Republic of Congo), resulting in total emission factors of 0.99−2.33 Mg C m−3. In all cases, emissions were dominated by damage to surrounding vegetation and the infrastructure rather than the logs themselves, and total emissions represented about 3–15% of the biomass carbon stocks of the associated unlogged forests. We then combined the emission factors with country level logging statistics for nine key timber producing countries represented by our study areas to gain an understanding of the order of magnitude of emissions from degradation compared to those recently reported for deforestation in the same countries. For the nine countries included, emissions from logging were on average equivalent to about 12% of those from deforestation. For those nine countries with relatively low emissions from deforestation, emissions from logging were equivalent to half or more of those from deforestation, whereas for those countries with the highest emissions from deforestation, emissions from logging were equivalent to <10% of those from deforestation. Understanding how to account emissions and the magnitude of each emissions source resulting from tropical timber harvesting practices helps identify where there are opportunities to reduce emissions from the second ‘D’ in REDD.
Transparent Real Time Monitoring for Multi-tenant J2EE Applications

With the emergence of PaaS and SaaS cloud delivery methods, accurate monitoring and metering become an important challenge for the cloud providers, as they assure the input that drives the elasticity of the solution, by dynamically provisioning and de-provisioning resources, and at the same time enable the chargeback for the resources used. This paper presents an approach for real time monitoring of multi-tenant Java J2EE based applications. The main requirements of such a monitoring solution are: low monitoring performance overhead on the JVM, capability to monitor code execution by JVM thread based on the tenant running it and dynamic granularity adjustment at Java method level, enabling relevant reporting of code execution. The solution proposed in this paper combines three methodologies of JVM and J2EE monitoring to achieve a correlated view of the code execution. The first layer is at JVM Tooling Interface by using a native agent that captures the thread execution events and adds dynamic instrumentation in the code of the target application at method level. The second layer is at JAAS level in J2EE where the authentication operations are hooked and linked to the JVM threads. Finally the third layer is at EJB container level where the EJB 3.0 interceptors are used to monitor EJB calls. The real time data from these three layers is consolidated based on the tenant's identity and reported to a metering application. The experimental results are focused on the accuracy of the monitoring solution implemented on Apache Geronimo 3.0. 0 application server, using a benchmark application. Finally the paper presents the monitoring performance overhead introduced by the solution proposed measured on SPECjvm2008 benchmark application, focusing on the granularity of monitored data.
The shape of an instant: measuring and modeling perceptual attack time with probability density functions (if a tree falls in the forest, when did 57 people hear it make a sound?)

A sound's Perceptual Attack Time ("PAT") is its perceived moment of rhythmic placement. PAT is notoriously difficult to measure, because all methods measure a test sound's PAT in relationship to an action or second sound, which add uncertainty to the measurements. This work uses the ideal impulse, which is an ideal reference because it is perfectly isolated in time with a very clear attack. However, its perfectly broad frequency spectrum is problematic because it is harder to perceive the relative timing of sounds when their spectra differ greatly. This motivates Spectrally Matched Click Synthesis, the creation of arbitrarily short clicks whose spectra approximate those of arbitrary input sounds. This work represents PAT as a function indicating the probability that a typical listener would hear the sound's PAT at each time, rather than as just one instant. The problem of deriving each sound's PAT from pairwise comparisons becomes one of estimating distributions of random variables for every sound's PAT given only observations of random variables corresponding to the difference two sounds' distributions plus noise. Methods to address this draw from maximum likelihood estimation and the shortest path problem. Subjects downloaded a listening test that presents a series of PAT measurement trials and allows them to adjust sounds' relative timing until they sound synchronous. This establishes perceptual "ground truth" for the PAT of a collection of 20 sounds compared against each other in various combinations. As hoped, subjects were able to align a sound more reliably to one of that sound's spectrally matched clicks than to other sounds of the same duration. The representation of PAT with probability density functions provides a new perspective on the problem of predicting PAT directly from acoustical signals. Rather than choosing a single moment for PAT given a segment of sound known a priori to contain a single musical event, these regression methods estimate continuous shapes of PAT distributions from continuous (not necessarily presegmented) audio signals, formulated as a supervised machine learning regression problem whose inputs are DSP functions computed from the sound. This work concludes with some preliminary musical applications of the resulting models.
Online Event Recognition from Moving Vehicles: Application Paper

Abstract We present a system for online composite event recognition over streaming positions of commercial vehicles. Our system employs a data enrichment module, augmenting the mobility data with external information, such as weather data and proximity to points of interest. In addition, the composite event recognition module, based on a highly optimised logic programming implementation of the Event Calculus, consumes the enriched data and identifies activities that are beneficial in fleet management applications. We evaluate our system on large, real-world data from commercial vehicles, and illustrate its efficiency.
Reliability, usability and security in anti-phishing software and its design

ion level: Misuser goal Still, the system is not completely phishing resistant. For example, when a user has not used his or her account, the system should notify the user to confirm its account. In this case, it is also possible to be spoofed. Its misuse case can be given as follows. Table 6: Misuse case sample B Name: Steal password and account name by sniffing plain messages
Optically Stimulated Luminescence Dating of Sediments over the Past 200,000 Years

Optical dating of sediment using optically stimulated luminescence has become important for studying Earth surface processes, and this technique continues to develop rapidly. A group of closely linked luminescence methods can be used to estimate the time since grains of quartz and feldspar were last exposed to daylight by detecting their subsequent response to environmental ionizing radiation exposure. The technique is well suited to the dating of deposits as young as one year to several hundred thousand years. Recent technical developments have established a dating protocol with improved precision, a high degree of reliability, and an in-built means to detect incomplete signal removal during deposition. This approach has been extended to age estimation for single grains, opening up a wider range of potential environments and new possibilities for understanding postdepositional grain movement. Ongoing research offers the possibility of significant age range extension and novel applications including low-t.
Multiresolution Techniques for Real-Time Visualization of Urban Environments and Terrains

In recent times we are witnessing a steep increase in the availability of data coming from real{life environments. Nowadays, virtually every one connected to the Internet may have instant access to a tremendous amount of data from satellite elevation maps, airborne time-ofight scanners and digital cameras, street{level photographs and even cadastral maps. As for other more traditional types of data such as documents, pictures and videos, users of digital exploration softwares expect commodity hardware to exhibit good performances for interactive purposes, regardless to the size of these data. In this thesis we propose novel solutions to the problem of rendering large terrain and urban models on site and remotely on commodity platforms. Our solutions build on the concept of multiresolution representation, where alternative representations of the same data with di erent accuracy are used to selectively distribute the computational power, and consequently the visual accuracy, where it is more needed on the base of user's point of view. In particular, we will introduce an e cient multiresolution data compression technique for planar and spherical surfaces applied to terrain data which is able to handle huge amount of information at a planetary scale. We will also describe a novel data structure for compact storage and rendering of urban entities such as buildings to allow real{time exploration of cityscapes from a remote online repository. Moreover, we will show how recent technologies can be exploited to transparently integrate virtual exploration and general computer graphics techniques with web applications.
A monocular vision system based on cooperative targets detection for aircraft pose measurement

In this paper, a monocular vision measurement system based on cooperative targets detection is proposed, which can capture the three-dimensional information of objects by recognizing the checkerboard target and calculating of the feature points. The aircraft pose measurement is an important problem for aircraft’s monitoring and control. Monocular vision system has a good performance in the range of meter. This paper proposes an algorithm based on coplanar rectangular feature to determine the unique solution of distance and angle. A continuous frame detection method is presented to solve the problem of corners’ transition caused by symmetry of the targets. Besides, a displacement table test system based on three-dimensional precision and measurement system human-computer interaction software has been built. Experiment result shows that it has a precision of 2mm in the range of 300mm to 1000mm, which can meet the requirement of the position measurement in the aircraft cabin.
LegenDary 2013 Soccer 2 D Simulation Team Description Paper

In LegenDary project, we have been continuing to develop our team based on Agent2D to achieve our goals and purposes. This year, we have optimized our shoot system by using Reinforcement Learning. We have improved our defense system by adding a new Marking system. In this paper, we are going to briefly describe our latest achievement to these tasks and strategies.
Context-Base Computational Value Prediction with Value Compression

In this paper, we propose context-base computation value prediction and aggressive cost reduction techniques exploiting value locality. Context-base computational value prediction tracks both base value and its stride for corresponding context to cover context-dependent delta patterns. We also propose an aggressive value compression scheme using value compression cache. This cache tracks higher order bits of tracking values. To maximize the storage efficiency, the data can be cached only after the predicting value have a certain confidence. We applied these proposals and dynamic coverage accuracy control on TAGE inspired value predictor, CBC-VTAGE. With 8KB storage budget, our optimized CBC-VTAGE achieves 17.1% speedup compared with processor without value prediction. The geomean of IPC for distributed 135 traces is 3.76.
A Method to Suggest Alternative Routes Based on Analysis of Automobiles' Trajectories

Inexperienced drivers usually use the most-known paths to move inside the cities, while drivers with a better knowledge of the road network normally taken alternative routes that are shorter, faster or safer. This knowledge about roads usage, when shared with other drivers, could offer more paths options to distribute the traffic load across the city by suggesting alternative routes. However, the problem lies in how to suggest alternative route directions for ordinary drivers considering knowledge gathered from experienced drivers. In order to try to solve this problem, it is proposed an algorithm, named TODS - Trajectory Outlier Detection and Segmentation, to group and segment car road trajectories in standard and alternative routes based on city roads usage in different day times periods. After that, the segmentation results are suggested as driving directions for ordinary drivers. To evaluate the results was performed a qualitative comparison with TRA-SOD algorithm considering the segmentation process. The tests were executed using two trajectories datasets collected by drivers in San Francisco - USA and Joinville - Brazil. The results assessment indicate that TODS is superior to TRA-SOD due to its segmentation characteristics. Besides that, it has been observed that the time period of the day influences how routes are used along the day.
A TOUGH 2 BASED PROGRAM FOR AUTOMATED SIMULATION OF COMPRESSED AIR TUNNEL ADVANCE

In compressed air tunneling an excess air pressure is applied inside the tunnel to prevent groundwaterinflow. Due to the excess pressure compressed air partly penetrates into the surrounding ground. The costs for the continuous generation of the compressed air may reach a significant share of the total tunnel construction costs. Especially contractors are therefore interested in predictions of the expected air losses to determine the capacity of the air supply equipment on site and to estimate the energy costs for the generation of the compressed air. Consulting engineers want to know how the escaping air influences surface settlements and tunnel face stability. ASCATA—a TOUGH2 based program, was developed in order to compute the transient air flow rates during the tunnel advance and to simulate the effects of the escaping compressed air to the ground. TOUGH2 was chosen as core of the new program because of the available flow module EOS3 (active air and water flow) and the open source code which, among other things, allowed for the implementation of an automatic advance sequence. Initially, an introduction of tunneling under compressed air is given in the paper. In the following, the main features of the developed program are described. Special attention is given to the algorithms which are used for the automatic advance simulation. The functionality of the program is demonstrated by two case studies.
Domain Registry : a Highly Available Infrastructure for Hyperty Discovery

With an increased demand for reliable and performant distributed systems, nowadays infrastructures are built with the common concern of reducing servers downtimes and eliminating single points of failure. Align with this, we present the Domain Registry, a core component of the European research project reThink. The Domain Registry is a highly available distributed system with no single points of failure that exposes a Representational State Transfer (REST) Application Programming Interface (API) that allows reThink enabled applications to register, update and delete information about what applications are running in user’s devices, and as a result, allowing the communication between such users. Moreover, to ensure a healthy environment we monitor and log all Domain Registry applications and services. It allows to proactively react on possible failures, perform efficient troubleshooting and gather near real-time information about running services. Our proposal, which is comprised by a core application and a failure prone deployment architecture, is presented in detail, and validated through scalability and performance metrics. We show that the Domain Registry is performant and that it scales horizontally while adding more servers.
A SLACKS-BASED MEASURE OF EFFICIENCY FOR PARALLEL AND SERIES PRODUCTION SYSTEMS

Data envelopment analysis (DEA) is a mathematical approach for evaluating the efficiency of decision making units (DMUs) that convert multiple inputs into multiple outputs. In many cases, production systems may have internal or network structures, such as series and parallel structures, which are composed of several processes interacting with each other. In the last two decades, several authors have proposed DEA models which usually utilize the radial measure to calculate the efficiency of the DMUs with internal structure. In this paper, we first introduce a non-radial DEA model in the slacks-based measure (SBM) framework for evaluating the overall efficiency of parallel production systems by considering the parallel relationship between sub-processes. We will, then extend the methodology to series production systems. A numerical experiment is used to demonstrate and compare the results with obtained using new methods and show the application of the proposed approach.
Deep Convolutional Neural Networks as a Method to Classify Rotating Objects based on Monostatic Radar Cross Section

Radar systems emit a time-varying signal and measure the response of a radar-reflecting surface. In the case of narrowband, monostatic radar signal domain, all spatial information is projected into a Radar Cross Section (RCS) scalar. We address the challenging problem of determining shape class using monostatic RCS estimates collected as a time series from a rotating object tumbling with unknown motion parameters under detectability limitations and signal noise. Previous shape classification methods have relied on image-like synthetic aperture radar (SAR) or multistatic (multiview) radar configurations with known geometry. Convolutional neural networks (CNNs) have revolutionized learning tasks in the computer vision domain by leveraging images and video rich with high-resolution 2D or 3D spatial information. We show that a feed-forward CNN can be trained to successfully classify object shape using only noisy monostatic RCS signals with unknown motion. We construct datasets containing over 100,000 simulated RCS signals belonging to different shape classes. We introduce deep neural network architectures that produce 2% classification error on testing data. We also introduce a refinement network that transforms simulated signals to appear more realistic and improve training utility. The results are a pioneering step toward the recognition of more complex targets using narrowband, monostatic radar.
Supplemental Material For " Differenctially Private Empirical Risk Minimization with Non-convex Loss Functions "

Let {Wt}t≥0 be a continuous-time homogeneous Markov process with values in Rd , and P = {Pt}t≥0 be the corresponding Markov semigroup. That is Psg(Wt) = E[g(Ws+t)|Wt] for all s, t ≥ 0 and all bounded measurable functions g ∶ Rd → R. A Borel probability measure is called stationary or invariant if ∫Rd Ptgd = ∫Rd gd for all g and t. Each of Pt can be extended to a bounded linear operator on L 2( ), such that Ptg ≥ 0 whenever g ≥ 0 and Pt1 = 1 for all t. The infinitestimal generator of the semigroup is a linear operator  defined on a dense subspace () of L2( ) such that for any g ∈ (), we have )tPtg = Ptg. Also,  can be defined as g(Wt) ∶= lim h→0 Phg(Wt) − g(Wt) h .