OpenRaiser/Intern-Atlas huggingface dataset:
Long range teleoperation for fine manipulation tasks under time-delay network conditions
Venue: arXiv cs.RO 2019
Year: 2019

We present a coarse-to-fine approach based semi-autonomous teleoperation system using vision guidance. The system is optimized for long range teleoperation tasks under time-delay network conditions and does not require prior knowledge of the remote scene. Our system initializes with a self exploration behavior that senses the remote surroundings through a freely mounted eye-in-hand web cam. The self exploration stage estimates hand-eye calibration and provides a telepresence interface via real-time 3D geometric reconstruction. The human operator is able to specify a visual task through the interface and a coarse-to-fine controller guides the remote robot enabling our system to work in high latency networks. Large motions are guided by coarse 3D estimation, whereas fine motions use image cues (IBVS). Network data transmission cost is minimized by sending only sparse points and a final image to the human side. Experiments from Singapore to Canada on multiple tasks were conducted to show our system's capability to work in long range teleoperation tasks.
Rate-Flexible Fast Polar Decoders
Venue: arXiv cs.IT 2019
Year: 2019

Polar codes have gained extensive attention during the past few years and recently they have been selected for the next generation of wireless communications standards (5G). Successive-cancellation-based (SC-based) decoders, such as SC list (SCL) and SC flip (SCF), provide a reasonable error performance for polar codes at the cost of low decoding speed. Fast SC-based decoders, such as Fast-SSC, Fast-SSCL, and Fast-SSCF, identify the special constituent codes in a polar code graph off-line, produce a list of operations, store the list in memory, and feed the list to the decoder to decode the constituent codes in order efficiently, thus increasing the decoding speed. However, the list of operations is dependent on the code rate and as the rate changes, a new list is produced, making fast SC-based decoders not rate-flexible. In this paper, we propose a completely rate-flexible fast SC-based decoder by creating the list of operations directly in hardware, with low implementation complexity. We further propose a hardware architecture implementing the proposed method and show that the area occupation of the rate-flexible fast SC-based decoder in this paper is only $38\%$ of the total area of the memory-based base-line decoder when 5G code rates are supported.
Reduction of Markov Chains using a Value-of-Information-Based Approach
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we propose an approach to obtain reduced-order models of Markov chains. Our approach is composed of two information-theoretic processes. The first is a means of comparing pairs of stationary chains on different state spaces, which is done via the negative Kullback-Leibler divergence defined on a model joint space. Model reduction is achieved by solving a value-of-information criterion with respect to this divergence. Optimizing the criterion leads to a probabilistic partitioning of the states in the high-order Markov chain. A single free parameter that emerges through the optimization process dictates both the partition uncertainty and the number of state groups. We provide a data-driven means of choosing the `optimal' value of this free parameter, which sidesteps needing to a priori know the number of state groups in an arbitrary chain.
A Computer-Aided System for Determining the Application Range of a Warfarin Clinical Dosing Algorithm Using Support Vector Machines with a Polynomial Kernel Function
Venue: arXiv cs.LG 2019
Year: 2019

Determining the optimal initial dose for warfarin is a critically important task. Several factors have an impact on the therapeutic dose for individual patients, such as patients' physical attributes (Age, Height, etc.), medication profile, co-morbidities, and metabolic genotypes (CYP2C9 and VKORC1). These wide range factors influencing therapeutic dose, create a complex environment for clinicians to determine the optimal initial dose. Using a sample of 4,237 patients, we have proposed a companion classification model to one of the most popular dosing algorithms (International Warfarin Pharmacogenetics Consortium (IWPC) clinical model), which identifies the appropriate cohort of patients for applying this model. The proposed model functions as a clinical decision support system which assists clinicians in dosing. We have developed a classification model using Support Vector Machines, with a polynomial kernel function to determine if applying the dose prediction model is appropriate for a given patient. The IWPC clinical model will only be used if the patient is classified as "Safe for model". By using the proposed methodology, the dosing mode's prediction accuracy increases by 15 percent in terms of Root Mean Squared Error and 17 percent in terms of Mean Absolute Error in dose estimates of patients classified as "Safe for model".
Tackling Unit Commitment and Load Dispatch Problems Considering All Constraints with Evolutionary Computation
Venue: arXiv cs.CY 2019
Year: 2019

Unit commitment and load dispatch problems are important and complex problems in power system operations that have being traditionally solved separately. In this paper, both problems are solved together without approximations or simplifications. In fact, the problem solved has a massive amount of grid-connected photovoltaic units, four pump-storage hydro plants as energy storage units and ten thermal power plants, each with its own set of operation requirements that need to be satisfied. To face such a complex constrained optimization problem an adaptive repair method is proposed. By including a given repair method itself as a parameter to be optimized, the proposed adaptive repair method avoid any bias in repair choices. Moreover, this results in a repair method that adapt to the problem and will improve together with the solution during optimization. Experiments are conducted revealing that the proposed method is capable of surpassing exact method solutions on a simplified version of the problem with approximations as well as solve the otherwise intractable complete problem without simplifications. Moreover, since the proposed approach can be applied to other problems in general and it may not be obvious how to choose the constraint handling for a certain constraint, a guideline is provided explaining the reasoning behind. Thus, this paper open further possibilities to deal with the ever changing types of generation units and other similarly complex operation/schedule optimization problems with many difficult constraints.
Cliques in projective space and construction of Cyclic Grassmannian Codes
Venue: arXiv cs.IT 2019
Year: 2019

The construction of Grassmannian codes in some projective space is of highly mathematical nature and requires strong computational power for the resulting searches. In this paper was constructed, using GAP System for Computational Discrete Algebra and Wolfram Mathematica, cliques in the projective space Pq(n) and then we use these to produce cyclic Grassmannian codes.
Gabidulin Codes with Support Constrained Generator Matrices
Venue: arXiv cs.IT 2019
Year: 2019

Gabidulin codes are the first general construction of linear codes that are maximum rank distant (MRD). They have found applications in linear network coding, for example, when the transmitter and receiver are oblivious to the inner workings and topology of the network (the so-called incoherent regime). The reason is that Gabidulin codes can be used to map information to linear subspaces, which in the absence of errors cannot be altered by linear operations, and in the presence of errors can be corrected if the subspace is perturbed by a small rank. Furthermore, in distributed coding and distributed systems, one is led to the design of error correcting codes whose generator matrix must satisfy a given support constraint. In this paper, we give necessary and sufficient conditions on the support of the generator matrix that guarantees the existence of Gabidulin codes and general MRD codes. When the rate of the code is not very high, this is achieved with the same field size necessary for Gabidulin codes with no support constraint. When these conditions are not satisfied, we characterize the largest possible rank distance under the support constraints and show that they can be achieved by subcodes of Gabidulin codes. The necessary and sufficient conditions are identical to those that appear for MDS codes which were recently proven by Yildiz et al. and Lovett in the context of settling the GM-MDS conjecture.
Concurrent Transmission Scheduling for Perceptual Data Sharing in mmWave Vehicular Networks
Venue: arXiv cs.NI 2019
Year: 2019

Sharing perceptual data with other vehicles enhances the traffic safety of autonomous vehicles because it helps vehicles locate other vehicles and pedestrians in their blind spots. Such safety applications require high throughput and short delay, which cannot be achieved by conventional microwave vehicular communication systems. Therefore, millimeter-wave (mmWave) communications are considered to be a key technology for sharing perceptual data because of their wide bandwidth. One of the challenges of data sharing in mmWave communications is broadcasting because narrow-beam directional antennas are used to obtain high gain. Because many vehicles should share their perceptual data to others within a short time frame in order to enlarge the areas that can be perceived based on shared perceptual data, an efficient scheduling for concurrent transmission that improves spatial reuse is required for perceptual data sharing. This paper proposes a data sharing algorithm that employs a graph-based concurrent transmission scheduling. The proposed algorithm realizes concurrent transmission to improve spatial reuse by designing a rule that is utilized to determine if the two pairs of transmitters and receivers interfere with each other by considering the radio propagation characteristics of narrow-beam antennas. A prioritization method that considers the geographical information in perceptual data is also designed to enlarge perceivable areas in situations where data sharing time is limited and not all data can be shared. Simulation results demonstrate that the proposed algorithm doubles the area of the cooperatively perceivable region compared with a conventional algorithm that does not consider mmWave communications because the proposed algorithm achieves high-throughput transmission by improving spatial reuse. The prioritization also enlarges the perceivable region by a maximum of 20%.
Rods and Rings: Soft Subdivision Planner for R^3 x S^2
Venue: arXiv cs.CG 2019
Year: 2019

We consider path planning for a rigid spatial robot moving amidst polyhedral obstacles. Our robot is either a rod or a ring. Being axially-symmetric, their configuration space is R^3 x S^2 with 5 degrees of freedom (DOF). Correct, complete and practical path planning for such robots is a long standing challenge in robotics. While the rod is one of the most widely studied spatial robots in path planning, the ring seems to be new, and a rare example of a non-simply-connected robot. This work provides rigorous and complete algorithms for these robots with theoretical guarantees. We implemented the algorithms in our open-source Core Library. Experiments show that they are practical, achieving near real-time performance. We compared our planner to state-of-the-art sampling planners in OMPL. Our subdivision path planner is based on the twin foundations of ε-exactness and soft predicates. Correct implementation is relatively easy. The technical innovations include subdivision atlases for S^2, introduction of Σ_2 representations for footprints, and extensions of our feature-based technique for "opening up the blackbox of collision detection".
Surfing the Web quicker than QUIC via a shared Address Validation
Venue: arXiv cs.CR 2019
Year: 2019

QUIC is a performance-optimized secure transport protocol and a building block of the upcoming HTTP/3 standard. To protect against denial-of-service attacks, QUIC servers need to validate the IP addresses claimed by their clients. So far, the QUIC protocol conducts address validation for each hostname separately using validation tokens. In this work, we review this practice and introduce a new QUIC transport parameter to allow a shared address validation across hostnames. This parameter indicates to the client, that an issued validation token can be used to abbreviate the address validation when connecting to specific other hostnames. Based on trust-relations between real-world hostnames we evaluate the performance benefits of our proposal. Our results suggest that a shared address validation saves a round-trip time on almost 60% of the required handshakes to different hosts during the first loading of an average website. Assuming a typical transatlantic connection with a round-trip time of 90ms. We find that deploying our proposal reduces the delay overhead to establish all required connections for an average website by 142.2ms.
Aggregated Deep Local Features for Remote Sensing Image Retrieval
Venue: arXiv cs.CV 2019
Year: 2019

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.
Using SMT Solvers to Validate Models for AI Problems
Venue: arXiv cs.AI 2019
Year: 2019

Artificial Intelligence problems, ranging form planning/scheduling up to game control, include an essential crucial step: describing a model which accurately defines the problem's required data, requirements, allowed transitions and established goals. The ways in which a model can fail are numerous and often lead to a failure of search strategies to provide a quick, optimal, or even any solution. This paper proposes using SMT (Satisfiability Modulo Theories) solvers, such as Z3, to check the validity of a model. We propose two tests: checking whether a final(goal) state exists in the model's described problem space and checking whether the transitions described can provide a path from the identified initial states to any the goal states (meaning a solution has been found). The advantage of using an SMT solver for AI model checking is that they substitute actual search strategies and they work over an abstract representation of the model, that is, a set of logical formulas. Reasoning at an abstract level is not as expensive as exploring the entire solution space. SMT solvers use efficient decision procedures which provide proofs for the logical formulas corresponding to the AI model. A recent addition to Z3 allowed us to describe sequences of transitions as a recursive function, thus we can check if a solution can be found in the defined model.
Substation One-Line Diagram Automatic Generation and Visualization
Venue: arXiv cs.OH 2019
Year: 2019

In Energy Management System (EMS) applications and many other off-line planning and study tools, one-line diagram (OLND) of the whole system and stations is a straightforward view for planners and operators to design, monitor, analyze, and control the power system. Large-scale power system OLND is usually manually developed and maintained. The work is tedious, time-consuming and ease to make mistake. Meanwhile, the manually created diagrams are hard to be shared among the on-line and off-line systems. To save the time and efforts to draw and maintain OLNDs, and provide the capability to share the OLNDs, a tool to automatically develop substation based upon Common Information Model (CIM) standard is needed. Currently, there is no standard rule to draw the substation OLND. Besides, the substation layouts can be altered from the typical formats in textbooks based on factors of economy, efficiency, engineering practice, etc. This paper presents a tool on substation OLND automatic generation and visualization. This tool takes the substation CIM/E model as input, then automatically computes the coordinates of all components and generates the substation OLND based on its components attributes and connectivity relations. Evaluation of the proposed approach is presented using a real provincial power system. Over 95\% of substation OLNDs are decently presented and the rest are corner cases, needing extra effort to do specific reconfiguration.
The Multi-Event-Class Synchronization (MECS) Algorithm
Venue: arXiv cs.HC 2019
Year: 2019

Synchronization is a fundamental component of computational models of human behavior, at both intra-personal and inter-personal level. Event synchronization analysis was originally conceived with the aim of providing a simple and robust method to measure synchronization between two time series. In this paper we propose a novel method extending the state-of-the-art of the event synchronization techniques: the Multi-Event-Class Synchronization (MECS) algorithm. MECS measures the synchronization between relevant events belonging to different event classes that are detected in multiple time series. Its motivation emerged from the need to model non-verbal multimodal signals in Human-Computer Interaction. Using MECS, synchronization can be computed between events belonging to the same class (intra-class synchronization) or between events belonging to different classes (inter-class synchronization). In the paper we also show how our technique can deal with macro-events (i.e., sets of events satisfying constraints) and macro-classes (i.e., sets of classes). In the last part of the paper, we apply the proposed method to two types of data i) artificial and 2) real-world case study concerning analysis of human multimodal behavior.
Iterative Reinforcement Learning Based Design of Dynamic Locomotion Skills for Cassie
Venue: arXiv cs.RO 2019
Year: 2019

Deep reinforcement learning (DRL) is a promising approach for developing legged locomotion skills. However, the iterative design process that is inevitable in practice is poorly supported by the default methodology. It is difficult to predict the outcomes of changes made to the reward functions, policy architectures, and the set of tasks being trained on. In this paper, we propose a practical method that allows the reward function to be fully redefined on each successive design iteration while limiting the deviation from the previous iteration. We characterize policies via sets of Deterministic Action Stochastic State (DASS) tuples, which represent the deterministic policy state-action pairs as sampled from the states visited by the trained stochastic policy. New policies are trained using a policy gradient algorithm which then mixes RL-based policy gradients with gradient updates defined by the DASS tuples. The tuples also allow for robust policy distillation to new network architectures. We demonstrate the effectiveness of this iterative-design approach on the bipedal robot Cassie, achieving stable walking with different gait styles at various speeds. We demonstrate the successful transfer of policies learned in simulation to the physical robot without any dynamics randomization, and that variable-speed walking policies for the physical robot can be represented by a small dataset of 5-10k tuples.
Optimizing the Access to Healthcare Services in Dense Refugee Hosting Urban Areas: A Case for Istanbul
Venue: arXiv cs.CY 2019
Year: 2019

With over 3.5 million refugees, Turkey continues to host the world's largest refugee population. This introduced several challenges in many areas including access to healthcare system. Refugees have legal rights to free healthcare services in Turkey's public hospitals. With the aim of increasing healthcare access for refugees, we looked at where the lack of infrastructure is felt the most. Our study attempts to address these problems by assessing whether Migrant Health Centers' locations are optimal. The aim of this study is to improve refugees' access to healthcare services in Istanbul by improving the locations of health facilities available to them. We used call data records provided by Turk Telekom.
Anti-Turing Machine
Venue: arXiv cs.OH 2019
Year: 2019

The invention of CPU-centric computing paradigm was incredible breakthrough of computer science that revolutionized our everyday life dramatically. However, the CPU- centric paradigm is based on the Turing machine concept and, as a result, expensive and power-hungry data transferring between the memory and CPU core is inevitable operation. Anti-Turing machine paradigm can be based on two fundamental principles: (1) data-centric computing, and (2) decentralized computing. Anti-Turing machine is able to execute a special type of programs. The commands of such program have to be addressed to the 2D or 3D persistent memory space is able to process data in-place. This program should not define the position or structure of data but it has to define the goal of data processing activity. Generally speaking, it needs to consider the whole memory space like the data transformation space. But the data placement, particular algorithm implementation, and strategy of algorithm execution are out of scope of the program.
Capsule Networks with Max-Min Normalization
Venue: arXiv cs.CV 2019
Year: 2019

Capsule Networks (CapsNet) use the Softmax function to convert the logits of the routing coefficients into a set of normalized values that signify the assignment probabilities between capsules in adjacent layers. We show that the use of Softmax prevents capsule layers from forming optimal couplings between lower and higher-level capsules. Softmax constrains the dynamic range of the routing coefficients and leads to probabilities that remain mostly uniform after several routing iterations. Instead, we propose the use of Max-Min normalization. Max-Min performs a scale-invariant normalization of the logits that allows each lower-level capsule to take on an independent value, constrained only by the bounds of normalization. Max-Min provides consistent improvement in test accuracy across five datasets and allows more routing iterations without a decrease in network performance. A single CapsNet trained using Max-Min achieves an improved test error of 0.20% on the MNIST dataset. With a simple 3-model majority vote, we achieve a test error of 0.17% on MNIST.