OpenRaiser/Intern-Atlas huggingface dataset:
Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks
Venue: arXiv cs.CV 2018
Year: 2018

Idling vehicles waste energy and pollute the environment through exhaust emission. In some countries, idling a vehicle for more than a predefined duration is prohibited and automatic idling vehicle detection is desirable for law enforcement. We propose the first automatic system to detect idling cars, using infrared (IR) imaging and deep networks. We rely on the differences in spatio-temporal heat signatures of idling and stopped cars and monitor the car temperature with a long-wavelength IR camera. We formulate the idling car detection problem as spatio-temporal event detection in IR image sequences and employ deep networks for spatio-temporal modeling. We collected the first IR image sequence dataset for idling car detection. First, we detect the cars in each IR image using a convolutional neural network, which is pre-trained on regular RGB images and fine-tuned on IR images for higher accuracy. Then, we track the detected cars over time to identify the cars that are parked. Finally, we use the 3D spatio-temporal IR image volume of each parked car as input to convolutional and recurrent networks to classify them as idling or not. We carried out an extensive empirical evaluation of temporal and spatio-temporal modeling approaches with various convolutional and recurrent architectures. We present promising experimental results on our IR image sequence dataset.
TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

For the task of subdecimeter aerial imagery segmentation, fine-grained semantic segmentation results are usually difficult to obtain because of complex remote sensing content and optical conditions. Recently, convolutional neural networks (CNNs) have shown outstanding performance on this task. Although many deep neural network structures and techniques have been applied to improve the accuracy, few have paid attention to better differentiating the easily confused classes. In this paper, we propose TreeSegNet which adopts an adaptive network to increase the classification rate at the pixelwise level. Specifically, based on the infrastructure of DeepUNet, a Tree-CNN block in which each node represents a ResNeXt unit is constructed adaptively according to the confusion matrix and the proposed TreeCutting algorithm. By transporting feature maps through concatenating connections, the Tree-CNN block fuses multiscale features and learns best weights for the model. In experiments on the ISPRS 2D semantic labeling Potsdam dataset, the results obtained by TreeSegNet are better than those of other published state-of-the-art methods. Detailed comparison and analysis show that the improvement brought by the adaptive Tree-CNN block is significant.
Concurrent games and semi-random determinacy
Venue: arXiv cs.LO 2018
Year: 2018

Consider concurrent, infinite duration, two-player win/lose games played on graphs. If the winning condition satisfies some simple requirement, the existence of Player 1 winning (finite-memory) strategies is equivalent to the existence of winning (finite-memory) strategies in finitely many derived one-player games. Several classical winning conditions satisfy this simple requirement. Under an additional requirement on the winning condition, the non-existence of Player 1 winning strategies from all vertices is equivalent to the existence of Player 2 stochastic strategies winning almost surely from all vertices. Only few classical winning conditions satisfy this additional requirement, but a fairness variant of o
OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction
Venue: arXiv cs.CL 2018
Year: 2018

Motivation: Ontologies are widely used in biology for data annotation, integration, and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotations commonly used in ontologies include class labels, descriptions, or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such as semantic similarity measures. Results: We propose a novel method, OPA2Vec, to generate vector representations of biological entities in ontologies by combining formal ontology axioms and annotation axioms from the ontology meta-data. We apply a Word2Vec model that has been pre-trained on PubMed abstracts to produce feature vectors from our collected data. We validate our method in two different ways: first, we use the obtained vector representations of proteins as a similarity measure to predict protein-protein interaction (PPI) on two different datasets. Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations. These two experiments are just an illustration of the possible applications of our method. OPA2Vec can be used to produce vector representations of any biomedical entity given any type of biomedical ontology. Availability: https://github.com/bio-ontology-research-group/opa2vec Contact: robert.hoehndorf@kaust.edu.sa and xin.gao@kaust.edu.sa.
A QPTAS for Gapless MEC
Venue: arXiv cs.DS 2018
Year: 2018

We consider the problem Minimum Error Correction (MEC). A MEC instance is an n x m matrix M with entries from {0,1,-}. Feasible solutions are composed of two binary m-bit strings, together with an assignment of each row of M to one of the two strings. The objective is to minimize the number of mismatches (errors) where the row has a value that differs from the assigned solution string. The symbol "-" is a wildcard that matches both 0 and 1. A MEC instance is gapless, if in each row of M all binary entries are consecutive. Gapless-MEC is a relevant problem in computational biology, and it is closely related to segmentation problems that were introduced by [Kleinberg-Papadimitriou-Raghavan STOC'98] in the context of data mining. Without restrictions, it is known to be UG-hard to compute an O(1)-approximate solution to MEC. For both MEC and Gapless-MEC, the best polynomial time approximation algorithm has a logarithmic performance guarantee. We partially settle the approximation status of Gapless-MEC by providing a quasi-polynomial time approximation scheme (QPTAS). Additionally, for the relevant case where the binary part of a row is not contained in the binary part of another row, we provide a polynomial time approximation scheme (PTAS).
Effective throughput of MISO systems over $κ-μ$ shadowed fading channels: MGF based analysis
Venue: arXiv cs.IT 2018
Year: 2018

The effective throughput of multiple-input single-output (MISO) wireless communication systems over $κ-μ$ shadowed fading channels is analysed. To obtain exact closed-from expression, the moment generating function (MGF) of the instantaneous signal to noise ratio (SNR) with independent and identically distributed (i.i.d.) transmit antennas is employed. In addition, a special case of integer values for the fading parameter is performed to simplify the derived expression. The asymptotic behaviour of the effective throughput at high values of SNR is also studied. The Monte Carlo simulations and the numerical results are presented to verify the validation of our analysis.
Transmission Capacity of Full-Duplex MIMO Ad-Hoc Network with Limited Self-Interference Cancellation
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we propose a joint transceiver beamforming design to simultaneously mitigate self-interference (SI) and partial inter-node interference for full-duplex multiple-input and multiple-output ad-hoc network, and then derive the transmission capacity upper bound (TC-UB) for the corresponding network. Condition on a specified transceiver antenna's configuration, we allow the SI effect to be cancelled at transmitter side, and offer an additional degree-of-freedom at receiver side for more inter-node interference cancellation. In addition, due to the proposed beamforming design and imperfect SI channel estimation, the conventional method to obtain the TC-UB is not applicable. This motivates us to exploit the dominating interferer region plus Newton-Raphson method to iteratively formulate the TC-UB. The results show that the derived TC-UB is quite close to the actual one especially when the number of receive-antenna is small. Moreover, our proposed beamforming design outperforms the existing beamforming strategies, and FD mode works better than HD mode in low signal-to-noise ratio region.
Site-Directed Insertion: Decision Problems, Maximality and Minimality
Venue: arXiv cs.FL 2018
Year: 2018

Site-directed insertion is an overlapping insertion operation that can be viewed as analogous to the overlap assembly or chop operations that concatenate strings by overlapping a suffix and a prefix of the argument strings. We consider decision problems and language equations involving site-directed insertion. By relying on the tools provided by semantic shuffle on trajectories we show that one variable equations involving site-directed insertion and regular constants can be solved. We consider also maximal and minimal variants of the site-directed insertion operation.
A Memory Controller with Row Buffer Locality Awareness for Hybrid Memory Systems
Venue: arXiv cs.AR 2018
Year: 2018

Non-volatile memory (NVM) is a class of promising scalable memory technologies that can potentially offer higher capacity than DRAM at the same cost point. Unfortunately, the access latency and energy of NVM is often higher than those of DRAM, while the endurance of NVM is lower. Many DRAM-NVM hybrid memory systems use DRAM as a cache to NVM, to achieve the low access latency, low energy, and high endurance of DRAM, while taking advantage of the large capacity of NVM. A key question for a hybrid memory system is what data to cache in DRAM to best exploit the advantages of each technology while avoiding the disadvantages of each technology as much as possible. We propose a new memory controller design that improves hybrid memory performance and energy efficiency. We observe that both DRAM and NVM banks employ row buffers that act as a cache for the most recently accessed memory row. Accesses that are row buffer hits incur similar latencies (and energy consumption) in both DRAM and NVM, whereas accesses that are row buffer misses incur longer latencies (and higher energy consumption) in NVM than in DRAM. To exploit this, we devise a policy that caches heavily-reused data that frequently misses in the NVM row buffers into DRAM. Our policy tracks the row buffer miss counts of recently-used rows in NVM, and caches in DRAM the rows that are predicted to incur frequent row buffer misses. Our proposed policy also takes into account the high write latencies of NVM, in addition to row buffer locality and more likely places the write-intensive pages in DRAM instead of NVM.
A Linear-Time Approximation Algorithm for the Orthogonal Terrain Guarding Problem
Venue: arXiv cs.CG 2018
Year: 2018

In this paper, we consider the 1.5-dimensional orthogonal terrain guarding problem. In this problem, we assign an x-monotone chain T because each edge is either horizontal or vertical, and determine the minimal number of vertex guards for all vertices of T. A vertex vi sees a point p on T if the line segment connecting vi to p is on or above T. We provide an optimal algorithm with O(n) for a subproblem of the orthogonal terrain guarding problem. In this subproblem, we determine the minimal number of vertex guards for all right(left) convex verteices of T. Finally, we provide a 2-approximation algorithm that solves the 1.5-dimensional orthogonal terrain guarding problem in O(n).
Evolution of Visual Odometry Techniques
Venue: arXiv cs.CV 2018
Year: 2018

With rapid advancements in the area of mobile robotics and industrial automation, a growing need has arisen towards accurate navigation and localization of moving objects. Camera based motion estimation is one such technique which is gaining huge popularity owing to its simplicity and use of limited resources in generating motion path. In this paper, an attempt is made to introduce this topic for beginners covering different aspects of vision based motion estimation task. The evolution of VO schemes over last few decades is discussed under two broad categories, that is, geometric and non-geometric approaches. The geometric approaches are further detailed under three different classes, that is, feature-based, appearance-based, and a hybrid of feature and appearance based schemes. The non-geometric approach is one of the recent paradigm shift from conventional pose estimation technique and is thus discussed in a separate section. Towards the end, a list of different datasets for visual odometry and allied research areas are provided for a ready reference.
IncentMe: Effective Mechanism Design to Stimulate Crowdsensing Participants with Uncertain Mobility
Venue: arXiv cs.NI 2018
Year: 2018

Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile crowdsensing system, it is paramount to incentivize smartphone users to provide sensing services in a timely and reliable manner. Given sensed information is often valid for a limited period of time, the capability of smartphone users to execute sensing tasks largely depends on their mobility, which is often uncertain. For this reason, in this paper we propose IncentMe, a framework that solves this fundamental problem by leveraging game-theoretical reverse auction mechanism design. After demonstrating that the proposed problem is NP-hard, we derive two mechanisms that are parallelizable and achieve higher approximation ratio than existing work. IncentMe has been extensively evaluated on a road traffic monitoring application implemented using mobility traces of taxi cabs in San Francisco, Rome, and Beijing. Results demonstrate that the mechanisms in IncentMe outperform prior work by improving the efficiency in recruiting participants by 30%.
Constraint Answer Set Programming without Grounding
Venue: arXiv cs.PL 2018
Year: 2018

Extending ASP with constraints (CASP) enhances its expressiveness and performance. This extension is not straightforward as the grounding phase, present in most ASP systems, removes variables and the links among them, and also causes a combinatorial explosion in the size of the program. Several methods to overcome this issue have been devised: restricting the constraint domains (e.g., discrete instead of dense), or the type (or number) of models that can be returned. In this paper we propose to incorporate constraints into s(ASP), a goal-directed, top-down execution model which implements ASP while retaining logical variables both during execution and in the answer sets. The resulting model, s(CASP), can constrain variables that, as in CLP, are kept during the execution and in the answer sets. s(CASP) inherits and generalizes the execution model of s(ASP) and is parametric w.r.t. the constraint solver. We describe this novel execution model and show through several examples the enhanced expressiveness of s(CASP) w.r.t. ASP, CLP, and other CASP systems. We also report improved performance w.r.t. other very mature, highly optimized ASP systems in some benchmarks. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).
Opium in science and society: Numbers
Venue: arXiv cs.DL 2018
Year: 2018

In science and beyond, numbers are omnipresent when it comes to justifying different kinds of judgments. Which scientific author, hiring committee-member, or advisory board panelist has not been confronted with page-long "publication manuals", "assessment reports", "evaluation guidelines", calling for p-values, citation rates, h-indices, or other statistics in order to motivate judgments about the "quality" of findings, applicants, or institutions? Yet, many of those relying on and calling for statistics do not even seem to understand what information those numbers can actually convey, and what not. Focusing on the uninformed usage of bibliometrics as worrysome outgrowth of the increasing quantification of science and society, we place the abuse of numbers into larger historical contexts and trends. These are characterized by a technology-driven bureaucratization of science, obsessions with control and accountability, and mistrust in human intuitive judgment. The ongoing digital revolution increases those trends. We call for bringing sanity back into scientific judgment exercises. Despite all number crunching, many judgments - be it about scientific output, scientists, or research institutions - will neither be unambiguous, uncontroversial, or testable by external standards, nor can they be otherwise validated or objectified. Under uncertainty, good human judgment remains, for the better, indispensable, but it can be aided, so we conclude, by a toolbox of simple judgment tools, called heuristics. In the best position to use those heuristics are research evaluators (1) who have expertise in the to-be-evaluated area of research, (2) who have profound knowledge in bibliometrics, and (3) who are statistically literate.
ReHabgame A non-immersive virtual reality rehabilitation system with applications in neuroscience
Venue: arXiv cs.HC 2018
Year: 2018

This paper proposes the use of a non-immersive virtual reality rehabilitation system ReHabgame developed using Microsoft Kinect and the Thalmic Labs Myo gesture control armband. The ReHabgame was developed based on two third-person video games that provide a feasible possibility of assessing postural control and functional reach tests. It accurately quantifies specific postural control mechanisms including timed standing balance, functional reach tests using real-time anatomical landmark orientation, joint velocity, and acceleration while end trajectories were calculated using an inverse kinematics algorithm. The game was designed to help patients with neurological impairment to be subjected to physiotherapy activity and practice postures of daily activities. The subjective experience of the ReHabgame was studied through the development of an Engagement Questionnaire (EQ) for qualitative, quantitative and Rasch model. The Monte-Carlo Tree Search (MCTS) and Random object (ROG) generator algorithms were used to adapt the physical and gameplay intensity in the ReHabgame based on the Motor Assessment Scale (MAS) and Hierarchical Scoring System (HSS). Rasch analysis was conducted to assess the psychometric characteristics of the ReHabgame and to identify if these are any misfitting items in the game. Rasch rating scale model (RSM) was used to assess the engagement of players in the ReHabgame and evaluate the effectiveness and attractiveness of the game. The results showed that the scales assessing the rehabilitation process met Rasch expectations of reliability, and unidimensionality. Infit and outfit mean squares values are in the range of (0.68 1.52) for all considered 16 items. The Root Mean Square Residual (RMSR) and the person separation reliability were acceptable. The item/person map showed that the persons and items were clustered symmetrically.
Mosaic: An Application-Transparent Hardware-Software Cooperative Memory Manager for GPUs
Venue: arXiv cs.OS 2018
Year: 2018

Modern GPUs face a trade-off on how the page size used for memory management affects address translation and demand paging. Support for multiple page sizes can help relax the page size trade-off so that address translation and demand paging optimizations work together synergistically. However, existing page coalescing and splintering policies require costly base page migrations that undermine the benefits multiple page sizes provide. In this paper, we observe that GPGPU applications present an opportunity to support multiple page sizes without costly data migration, as the applications perform most of their memory allocation en masse (i.e., they allocate a large number of base pages at once). We show that this en masse allocation allows us to create intelligent memory allocation policies which ensure that base pages that are contiguous in virtual memory are allocated to contiguous physical memory pages. As a result, coalescing and splintering operations no longer need to migrate base pages. We introduce Mosaic, a GPU memory manager that provides application-transparent support for multiple page sizes. Mosaic uses base pages to transfer data over the system I/O bus, and allocates physical memory in a way that (1) preserves base page contiguity and (2) ensures that a large page frame contains pages from only a single memory protection domain. This mechanism allows the TLB to use large pages, reducing address translation overhead. During data transfer, this mechanism enables the GPU to transfer only the base pages that are needed by the application over the system I/O bus, keeping demand paging overhead low.
Improving Performance of Iterative Methods by Lossy Checkponting
Venue: arXiv cs.DC 2018
Year: 2018

Iterative methods are commonly used approaches to solve large, sparse linear systems, which are fundamental operations for many modern scientific simulations. When the large-scale iterative methods are running with a large number of ranks in parallel, they have to checkpoint the dynamic variables periodically in case of unavoidable fail-stop errors, requiring fast I/O systems and large storage space. To this end, significantly reducing the checkpointing overhead is critical to improving the overall performance of iterative methods. Our contribution is fourfold. (1) We propose a novel lossy checkpointing scheme that can significantly improve the checkpointing performance of iterative methods by leveraging lossy compressors. (2) We formulate a lossy checkpointing performance model and derive theoretically an upper bound for the extra number of iterations caused by the distortion of data in lossy checkpoints, in order to guarantee the performance improvement under the lossy checkpointing scheme. (3) We analyze the impact of lossy checkpointing (i.e., extra number of iterations caused by lossy checkpointing files) for multiple types of iterative methods. (4)We evaluate the lossy checkpointing scheme with optimal checkpointing intervals on a high-performance computing environment with 2,048 cores, using a well-known scientific computation package PETSc and a state-of-the-art checkpoint/restart toolkit. Experiments show that our optimized lossy checkpointing scheme can significantly reduce the fault tolerance overhead for iterative methods by 23%~70% compared with traditional checkpointing and 20%~58% compared with lossless-compressed checkpointing, in the presence of system failures.
On the Interaction between Autonomous Mobility-on-Demand and Public Transportation Systems
Venue: arXiv cs.SY 2018
Year: 2018

In this paper we study models and coordination policies for intermodal Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles provides on-demand mobility jointly with public transit. Specifically, we first present a network flow model for intermodal AMoD, where we capture the coupling between AMoD and public transit and the goal is to maximize social welfare. Second, leveraging such a model, we design a pricing and tolling scheme that allows to achieve the social optimum under the assumption of a perfect market with selfish agents. Finally, we present a real-world case study for New York City. Our results show that the coordination between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation.