Archives for 2019

Today I read a paper titled “Merging of Bézier curves with box constraints”

The abstract is:
In this paper, we present a novel approach to the problem of merging of B\’ezier curves with respect to the $L_2$-norm.

We give illustrative examples to show that the solution of the conventional merging problem may not be suitable for further modification and applications.

As in the case of the degree reduction problem, we apply the so-called restricted area approach — proposed recently in (P.

Gospodarczyk, Computer-Aided Design 62 (2015), 143–151) — to avoid certain defects and make the resulting curve more useful.

A method of solving the new problem is based on box-constrained quadratic programming approach.

Today I read a paper titled “Efficient Hill-Climber for Multi-Objective Pseudo-Boolean Optimization”

The abstract is:
Local search algorithms and iterated local search algorithms are a basic technique.

Local search can be a stand along search methods, but it can also be hybridized with evolutionary algorithms.

Recently, it has been shown that it is possible to identify improving moves in Hamming neighborhoods for k-bounded pseudo-Boolean optimization problems in constant time.

This means that local search does not need to enumerate neighborhoods to find improving moves.

It also means that evolutionary algorithms do not need to use random mutation as a operator, except perhaps as a way to escape local optima.

In this paper, we show how improving moves can be identified in constant time for multiobjective problems that are expressed as k-bounded pseudo-Boolean functions.

In particular, multiobjective forms of NK Landscapes and Mk Landscapes are considered.

Today I read a paper titled “On Avoidance Learning with Partial Observability”

The abstract is:
We study a framework where agents have to avoid aversive signals.

The agents are given only partial information, in the form of features that are projections of task states.

Additionally, the agents have to cope with non-determinism, defined as unpredictability on the way that actions are executed.

The goal of each agent is to define its behavior based on feature-action pairs that reliably avoid aversive signals.

We study a learning algorithm, called A-learning, that exhibits fixpoint convergence, where the belief of the allowed feature-action pairs eventually becomes fixed.

A-learning is parameter-free and easy to implement.

Today I read a paper titled “Light Efficient Flutter Shutter”

The abstract is:
Flutter shutter is a technique in which the exposure is chopped into segments and light is only integrated part of the time.

By carefully selecting the chopping sequence it is possible to better condition the data for reconstruction problems such as motion deblurring, focal sweeping, and compressed sensing.

The partial exposure trades better conditioning for less energy.

In problems such as motion deblurring the available energy is what caused the problem in the first place (as strong illumination allows short exposure thus eliminates motion blur).

It is still beneficial because the benefit from the better conditioning outweighs the cost in energy.

This documents is focused on light efficient flutter shutter that provides better conditioning and better energy utilization than conventional flutter shutter.

Today I read a paper titled “A particle filter to reconstruct a free-surface flow from a depth camera”

The abstract is:
We investigate the combined use of a Kinect depth sensor and of a stochastic data assimilation method to recover free-surface flows.

More specifically, we use a Weighted ensemble Kalman filter method to reconstruct the complete state of free-surface flows from a sequence of depth images only.

This particle filter accounts for model and observations errors.

This data assimilation scheme is enhanced with the use of two observations instead of one classically.

We evaluate the developed approach on two numerical test cases: a collapse of a water column as a toy-example and a flow in an suddenly expanding flume as a more realistic flow.

The robustness of the method to depth data errors and also to initial and inflow conditions is considered.

We illustrate the interest of using two observations instead of one observation into the correction step, especially for unknown inflow boundary conditions.

Then, the performance of the Kinect sensor to capture temporal sequences of depth observations is investigated.

Finally, the efficiency of the algorithm is qualified for a wave in a real rectangular flat bottom tank.

It is shown that for basic initial conditions, the particle filter rapidly and remarkably reconstructs velocity and height of the free surface flow based on noisy measurements of the elevation alone.

Today I read a paper titled “Bayesian Opponent Exploitation in Imperfect-Information Games”

The abstract is:
The two most fundamental problems in computational game theory are computing a Nash equilibrium and learning to exploit opponents given observations of their play (aka opponent exploitation).

The latter is perhaps even more important than the former: Nash equilibrium does not have a compelling theoretical justification in game classes other than two-player zero-sum, and furthermore for all games one can potentially do better by exploiting perceived weaknesses of the opponent than by following a static equilibrium strategy throughout the match.

The natural setting for opponent exploitation is the Bayesian setting where we have a prior model that is integrated with observations to create a posterior opponent model that we respond to.

The most natural, and a well-studied prior distribution is the Dirichlet distribution.

An exact polynomial-time algorithm is known for best-responding to the posterior distribution for an opponent assuming a Dirichlet prior with multinomial sampling in the case of normal-form games; however, for the case of imperfect-information games the best known algorithm is a sampling algorithm based on approximating an infinite integral without theoretical guarantees.

The main result is the first exact algorithm for accomplishing this in imperfect-information games.

We also present an algorithm for another natural setting where the prior is the uniform distribution over a polyhedron.

Today I read a paper titled “A Real-Time Soft Robotic Patient Positioning System for Maskless Head-and-Neck Cancer Radiotherapy: An Initial Investigation”

The abstract is:
We present an initial examination of a novel approach to accurately position a patient during head and neck intensity modulated radiotherapy (IMRT).

Position-based visual-servoing of a radio-transparent soft robot is used to control the flexion/extension cranial motion of a manikin head.

A Kinect RGB-D camera is used to measure head position and the error between the sensed and desired position is used to control a pneumatic system which regulates pressure within an inflatable air bladder (IAB).

Results show that the system is capable of controlling head motion to within 2mm with respect to a reference trajectory.

This establishes proof-of-concept that using multiple IABs and actuators can improve cancer treatment.

Today I read a paper titled “Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks”

The abstract is:
This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models.

Specifically, our system accepts a stream of raw sensor data at one end and, in real-time, produces an estimate of the entire environment state at the output including even occluded objects.

We achieve this by framing the problem as a deep learning task and exploit sequence models in the form of recurrent neural networks to learn a mapping from sensor measurements to object tracks.

In particular, we propose a learning method based on a form of input dropout which allows learning in an unsupervised manner, only based on raw, occluded sensor data without access to ground-truth annotations.

We demonstrate our approach using a synthetic dataset designed to mimic the task of tracking objects in 2D laser data — as commonly encountered in robotics applications — and show that it learns to track many dynamic objects despite occlusions and the presence of sensor noise.

Today I read a paper titled “11 x 11 Domineering is Solved: The first player wins”

The abstract is:
We have developed a program called MUDoS (Maastricht University Domineering Solver) that solves Domineering positions in a very efficient way.

This enables the solution of known positions so far (up to the 10 x 10 board) much quicker (measured in number of investigated nodes).

More importantly, it enables the solution of the 11 x 11 Domineering board, a board up till now far out of reach of previous Domineering solvers.

The solution needed the investigation of 259,689,994,008 nodes, using almost half a year of computation time on a single simple desktop computer.

The results show that under optimal play the first player wins the 11 x 11 Domineering game, irrespective if Vertical or Horizontal starts the game.

In addition, several other boards hitherto unsolved were solved.

Using the convention that Vertical starts, the 8 x 15, 11 x 9, 12 x 8, 12 x 15, 14 x 8, and 17 x 6 boards are all won by Vertical, whereas the 6 x 17, 8 x 12, 9 x 11, and 11 x 10 boards are all won by Horizontal.

Today I read a paper titled “Autonomous Vehicle Routing in Congested Road Networks”

The abstract is:
This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput.

We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not lead to an increase in congestion (in stark contrast to common belief).

From an algorithmic standpoint, such theoretical insight suggests that the problem of routing customers and rebalancing vehicles can be decoupled, which leads to a computationally-efficient routing and rebalancing algorithm for the autonomous vehicles.

Numerical experiments and case studies corroborate our theoretical insights and show that the proposed algorithm outperforms state-of-the-art point-to-point methods by avoiding excess congestion on the road.

Collectively, this paper provides a rigorous approach to the problem of congestion-aware, system-wide coordination of autonomously driving vehicles, and to the characterization of the sustainability of such robotic systems.

Today I read a paper titled “Fast keypoint detection in video sequences”

The abstract is:
A number of computer vision tasks exploit a succinct representation of the visual content in the form of sets of local features.

Given an input image, feature extraction algorithms identify a set of keypoints and assign to each of them a description vector, based on the characteristics of the visual content surrounding the interest point.

Several tasks might require local features to be extracted from a video sequence, on a frame-by-frame basis.

Although temporal downsampling has been proven to be an effective solution for mobile augmented reality and visual search, high temporal resolution is a key requirement for time-critical applications such as object tracking, event recognition, pedestrian detection, surveillance.

In recent years, more and more computationally efficient visual feature detectors and decriptors have been proposed.

Nonetheless, such approaches are tailored to still images.

In this paper we propose a fast keypoint detection algorithm for video sequences, that exploits the temporal coherence of the sequence of keypoints.

According to the proposed method, each frame is preprocessed so as to identify the parts of the input frame for which keypoint detection and description need to be performed.

Our experiments show that it is possible to achieve a reduction in computational time of up to 40%, without significantly affecting the task accuracy.

Today I read a paper titled “Unsupervised Learning in Neuromemristive Systems”

The abstract is:
Neuromemristive systems (NMSs) currently represent the most promising platform to achieve energy efficient neuro-inspired computation.

However, since the research field is less than a decade old, there are still countless algorithms and design paradigms to be explored within these systems.

One particular domain that remains to be fully investigated within NMSs is unsupervised learning.

In this work, we explore the design of an NMS for unsupervised clustering, which is a critical element of several machine learning algorithms.

Using a simple memristor crossbar architecture and learning rule, we are able to achieve performance which is on par with MATLAB’s k-means clustering.

Today I read a paper titled “The Bees Algorithm for the Vehicle Routing Problem”

The abstract is:
In this thesis we present a new algorithm for the Vehicle Routing Problem called the Enhanced Bees Algorithm.

It is adapted from a fairly recent algorithm, the Bees Algorithm, which was developed for continuous optimisation problems.

We show that the results obtained by the Enhanced Bees Algorithm are competitive with the best meta-heuristics available for the Vehicle Routing Problem (within 0.5% of the optimal solution for common benchmark problems).

We show that the algorithm has good runtime performance, producing results within 2% of the optimal solution within 60 seconds, making it suitable for use within real world dispatch scenarios.

Today I read a paper titled “Automatic Face Reenactment”

The abstract is:
We propose an image-based, facial reenactment system that replaces the face of an actor in an existing target video with the face of a user from a source video, while preserving the original target performance.

Our system is fully automatic and does not require a database of source expressions.

Instead, it is able to produce convincing reenactment results from a short source video captured with an off-the-shelf camera, such as a webcam, where the user performs arbitrary facial gestures.

Our reenactment pipeline is conceived as part image retrieval and part face transfer: The image retrieval is based on temporal clustering of target frames and a novel image matching metric that combines appearance and motion to select candidate frames from the source video, while the face transfer uses a 2D warping strategy that preserves the user’s identity.

Our system excels in simplicity as it does not rely on a 3D face model, it is robust under head motion and does not require the source and target performance to be similar.

We show convincing reenactment results for videos that we recorded ourselves and for low-quality footage taken from the Internet.

Today I read a paper titled “A Novel Human Computer Interaction Platform based College Mathematical Education Methodology”

The abstract is:
This article proposes the analysis on novel human computer interaction (HCI) platform based college mathematical education methodology.

Above for the application of virtual reality technology in teaching the problems in the study, only through the organization focus on the professional and technical personnel, and constantly improve researchers in development process of professional knowledge, close to the actual needs of the teaching can we achieve the satisfactory result.

To obtain better education output, we combine the Kinect to form the HCI based teaching environment.

We firstly review the latest HCI technique and principles of college math courses, then we introduce basic components of the Kinect including the gesture segmentation, systematic implementation and the primary characteristics of the platform.

As the further step, we implement the system with the re-write of script code to build up the personalized HCI assisted education scenario.

The verification and simulation proves the feasibility of our method.

Today I read a paper titled “Heuristics for Planning, Plan Recognition and Parsing”

The abstract is:
In a recent paper, we have shown that Plan Recognition over STRIPS can be formulated and solved using Classical Planning heuristics and algorithms.

In this work, we show that this formulation subsumes the standard formulation of Plan Recognition over libraries through a compilation of libraries into STRIPS theories.

The libraries correspond to AND/OR graphs that may be cyclic and where children of AND nodes may be partially ordered.

These libraries include Context-Free Grammars as a special case, where the Plan Recognition problem becomes a parsing with missing tokens problem.

Plan Recognition over the standard libraries become Planning problems that can be easily solved by any modern planner, while recognition over more complex libraries, including Context-Free Grammars (CFGs), illustrate limitations of current Planning heuristics and suggest improvements that may be relevant in other Planning problems too.