nils-herrmann/clean_scirepeval huggingface dataset:
Similarity measure for CCITT Group 4 compressed document images

The similarity measure of document images has a crucial role in the area of document image retrieval. A method of measuring the similarity of CCITT Group 4 compressed document images is proposed. The features are extracted directly from the changing elements of the compressed images. Weighted Hausdorff distance is utilized to assign all of the word objects from two document images to corresponding classes by an unsupervised classifier, whereas the possible stop words are excluded. Document vectors are built by the occurrence frequency of the word object classes, and the pair-wise similarity of two document images is represented by the scalar product of the document vectors. Five group articles relating to different domains are used to test the validity of the presented approach.
Compact LTCC filter with vertically end-coupled resonators

A compact narrow-band low temperature co-fíred ceramic (LTCC) filter with a fractional bandwidth of 3% is proposed. The proposed filter consists of five cascaded half-wavelength resonators vertically arrayed on each LTCC layer. End-coupling between adjacent resonators is precisely controlled by rectangle-shaped slot on intermediate ground layer. The overall size of the filter is only 8 × 6 × 0.89 mm and a size reduction of 80% is achieved compared with a planar one. Measured S11 and S21 are better than −14 and −2.5 dB in the passband, respectively.
Phase retrieval via linear programming: Fundamental limits and algorithmic improvements

A recently proposed convex formulation of the phase retrieval problem estimates the unknown signal by solving a simple linear program. This new scheme, known as PhaseMax, is computationally efficient compared to standard convex relaxation methods based on lifting techniques. In this paper, we present an exact performance analysis of PhaseMax under Gaussian measurements in the large system limit. In contrast to previously known performance bounds in the literature, our results are asymptotically exact and they also reveal a sharp phase transition phenomenon. Furthermore, the geometrical insights gained from our analysis led us to a novel nonconvex formulation of the phase retrieval problem and an accompanying iterative algorithm based on successive linearization and maximization over a polytope. This new algorithm, which we call PhaseLamp, has provably superior recovery performance over the original PhaseMax method.
Analysis of epigenetic modifications by next generation sequencing

In plants and animals, gene expression can be altered by changes not to DNA itself but rather chemical modifications either to DNA or to histones that interact with DNA. These so called epigenetic modifications persist through cell cycle. Rapidly advancing technologies, such next generation DNA sequencing, have dramatically increased our ability to survey epigenetic markers genomewide. These techniques are revealing in great details massive epigenetic changes in cancer. Analysis of next generation sequencing data present a formidable computational challenge. We will discuss methods to address these challenges in the context of analyzing histone modifications and DNA methylation data. Several techniques useful in epigenetic data analysis will be discussed, mapping tags to reference genome incorporating all known SNPs, analysis of chIP-seq data, as well as restriction enzyme-based DNA methylation analysis.
Designing color filter arrays for the joint capture of visible and near-infrared images

Digital camera sensors are inherently sensitive to the near-infrared (NIR) part of the light spectrum. In this paper, we propose a general design for color filter arrays that allow the joint capture of visible/NIR images using a single sensor. We pose the CFA design as a novel spatial domain optimization problem, and provide an efficient iterative procedure that finds (locally) optimal solutions. Numerical experiments confirm the effectiveness of the proposed CFA design, which can simultaneously capture high quality visible and NIR image pairs.
Similar Handwritten Chinese Character Recognition Using Hierarchical CNN Model

We propose a hierarchical CNN model for the recognition of confusable similar handwritten Chinese characters, which are automatically extracted from a large character set by utilizing a classifier's recognition result. The proposed hierarchical CNN model takes advantage of deep networks and traditional hierarchical methods, and consists of two stages, which are expected to differentiate inter-group characters and intra-group characters, respectively. Different from traditional ways of expanding depth and/or width of general sole classifier CNNs, we explore the way of designing multiple parallel CNN classifiers to capture critical regions of similar characters. Each classifier along with their feature extraction layers is trained only with a group of similar characters so that the subtle shape difference can be captured. Totally, 368 similar characters (categorized into 172 groups) are extracted from 3755 frequently used Chinese characters. Experimental results on these similar characters demonstrate the superiority of the proposed method to the expanded CNN models.
A Mapping-Based Design for Nonsubsampled Hourglass Filter Banks in Arbitrary Dimensions

Multidimensional hourglass filter banks decompose the frequency spectrum of input signals into hourglass-shaped directional subbands, each aligned with one of the frequency axes. The directionality of the spectral partitioning makes these filter banks useful in separating the directional information in multidimensional signals. Despite the existence of various design techniques proposed for the 2-D case, to our best knowledge, the design of hourglass filter banks in 3-D and higher dimensions with finite impulse response (FIR) filters and perfect reconstruction has not been previously reported. In this paper, we propose a novel mapping-based design for the hourglass filter banks in arbitrary dimensions, featuring perfect reconstruction, FIR filters, efficient implementation using lifting/ladder structures, and a near-tight frame construction. The effectiveness of the proposed mapping- based design depends on the study of a set of conditions on the frequency supports of the mapping kernels. These conditions ensure that we can still get good frequency responses when the component filters used are nonideal. Among all feasible choices, we then propose an optimal specification for the mapping kernels, which leads to the simplest passband shapes and involves the fewest number of frequency variables. Finally, we illustrate the proposed techniques by a design example in 3-D, and an application in video denoising.
Joint Optimization for PAPR Reduction and Opportunistic Beamforming (OBF) in OFDM Systems

Weighting vectors with periodical structure, which are used in opportunistic beamforming (OBF) OFDM system, are generated to reduce the PAPR of the OFDM signal and the complexities of the tradition OBF-OFDM system simultaneously. The simulation results show that with less complexities, the throughput, both in maximum throughput scheduling scheme and the proportional fairness (PF) scheduling scheme, can be improved using the proposed scheme as comparison with the OFDM without OBF.
Optimal Replenishment Cycle in an EOQ Model with Defective Items

The optimal order cycle is very important in production decision, especially in the process of continuous production. In this paper, we try to determine the optimal replenishment cycle in an EOQ model for defective items. In the proposed model, the defective rate of products is a random variable and the order cycle is determined by the expected defective rate, order quantity and demand rate. Two possibilities of residual or shortage at the end of the cycle are considered. The optimal replenishment and the order quantity are determined by maximizing the expected profit as the objective. The validity of the model is verified through the simulation experiments. Sensitivity analysis of the main parameters is presented.
Adaptive sensing and inference for single-photon imaging

In recent years, there have been increasing efforts to develop solid-state sensors with single-photon sensitivity, with applications ranging from bio-imaging to 3D computer vision. In this paper, we present adaptive sensing models, theory and algorithms for these single-photon sensors, aiming to improve their dynamic ranges. Mapping different sensor configurations onto a finite set of states, we represent adaptive sensing schemes as finite-state parametric Markov chains. After deriving an asymptotic expression for the Fisher information rate of these Markovian systems, we propose a design criterion for sensing policies based on minimax ratio regret. We also present a suboptimal yet effective sensing policy based on random walks. Numerical experiments demonstrate the strong performance of the proposed scheme, which expands the sensor dynamic ranges of existing nonadaptive approaches by several orders of magnitude.
A binocular vision based auxiliary navigation system of the unmanned aerial vehicle for power line inspection

To achieve the wire-landing and wire-walking inspection of the unmanned aerial vehicle (UAV) for power line inspection, a binocular vision was developed based auxiliary navigation system on the embedded development platform. Based on the specific imaging target, line laser is shine on the power system wire, making it a cooperative target to assist the landing of UAV. During the course of UAV flight, the distance between the wire and mounted binocular ranging device and the angle between the Yaw direction and wire direction can be obtained, which can function as the auxiliary Navigation for UAV and improve the success rate of the landing. In the realization of binocular vision algorithm, the process of extracting characteristic points can be simplified by swiping the wire using laser beam. This can increase the implement speed of the algorithm while maintaining the precision. After the distance is obtained, the angle between the Yaw direction of UAV and the wire direction can be calculated through a simple mathematical model. The results showed that the relative error of binocular distance measurement was 2-3 %. The minimum deviation angle that could be identified with a Yaw direction at 50 cm was 5°. The imaging processing frame rate was 30Hz, which met the accuracy and real-time requirements of UAV landing.
Energy-efficient adaptive MIMO decoders

Energy efficiency is a primary design goal of modern wireless communication systems, especially those deployed in portable devices such as mobile phones. This paper presents a novel concept, which allows energy-efficient systems to exploit the changing nature of their environment in order to save power. This concept is employed to design an advanced MIMO decoder, which continuously monitors the signal-to-noise ratio (SNR) and re-configures its hardware in order to minimize its energy consumption. The design is implemented in 65nm technology in order to evaluate its performance and costs. The results show that the proposed adaptive architecture can achieve up to 50% of energy consumption savings compared to existing designs at the cost of an increased area overhead.
Integrating word embeddings and traditional NLP features to measure textual entailment and semantic relatedness of sentence pairs

Recent years the distributed representations of words (i.e., word embeddings) have been shown to be able to significantly improve performance in many natural language processing tasks, such as pos-of-tag tagging, chunking, named entity recognition and sentiment polarity judgement, etc. However, previous tasks only involve a single sentence. In contrast, this paper evaluates the effectiveness of word embeddings in sentence pair classification or regression problems. Specifically, we propose novel simple yet effective features based on word embeddings and extract many traditional linguistic features. Then these features serve as input of a classification/regression algorithm in isolation and in combination. Evaluations are conducted on three sentence pair classification/regression tasks, i.e., textual entailment, cross-lingual textual entailment and semantic relatedness estimation. Experiments on benchmark datasets provided by Semantic Evaluation 2013 and 2014 showed that using word embeddings is able to significantly improve the performance and our results outperform the best achieved results so far.
A Markov Game Theoritic Approach for Power Grid Security

The extensive use of information and communication technologies in power grid systems make them vulnerable to cyber-attacks. One class of cyber-attack is advanced persistent threats where highly skilled attackers can steal user authentication information's and then move laterally in the network, from host to host in a hidden manner, until they reach an attractive target. Once the presence of the attacker has been detected in the network, appropriate actions should be taken quickly to prevent the attacker going deeper. This paper presents a game theoretic approach to optimize the defense against an invader attempting to use a set of known vulnerabilities to reach critical nodes in the network. First, the network is modeled as a vulnerability multi-graph where the nodes represent physical hosts and edges the vulnerabilities that the attacker can exploit to move laterally from one host to another. Secondly, a two-player zero-sum Markov game is built where the states of the game represent the nodes of the vulnerability multi-graph graph and transitions correspond to the edge vulnerabilities that the attacker can exploit. The solution of the game gives the optimal strategy to disconnect vulnerable services and thus slow down the attack.
Adaptive Environment Map for Relighting: Using Cameras and Projected Light

Scene relighting has found applications in many areas, from movie special effects to building immersive environments. In this paper, we present a framework to render a scene under any prescribed lighting environments. First, we propose a 3D scanner that captures the scene geometry and reflectance simultaneously, by effectively using an uncalibrated camera, and light and shadows projected from a controlled lighting plane onto the scene. Then, a large camera array, composed of 48 cameras forming a planar array, is built to capture a lighting environment as a 4D incident light field. With the scene properties captured by the 3D scanner, and the lighting environment captured by the camera array, we can then relight the scene using a technique we develop, called the adaptive environment map (AEM). AEM, which fully describes the lighting environment including both spatial and directional variations, can render more realistic relighting results than existing techniques in literature, as shown in extensive experiments.
Robust wireless video streaming using hybrid spatial/temporal retransmission

Bandwidth demands and timing constraints are two major challenges for wireless video streaming applications. In this paper, we present a hybrid spatial/temporal retransmission protocol that tackles both of these challenges. To increase individual throughput as well as overall network capacity, the system uses an opportunistic retransmission protocol (PRO, Protocol for Retransmitting Opportunistically) that relies on overhearing nodes distributed in physical space to function as relays that retransmit failed packets on behalf of the source. Specifically, the best relay out of the set of nodes that currently have the copy of the packet is responsible for retransmitting (relaying) the packet. Relays with stronger connectivity to the destination have a higher chance of delivering packets successfully than the source, thereby resulting in a more efficient use of the channel. To meet timing constraints, a Time-based Adaptive Retransmission strategy (TAR) is applied by both the source and the relays. With TAR, the MAC dynamically determines whether to (re)transmit or discard a packet based on the retransmission deadline of the packet assigned by the video server. This significantly reduces the number of late packet arrivals at the receiver. Our extensive evaluation results both on a testbed and in the real world demonstrate that hybrid temporal/spatial retransmission can boost streaming performance in diverse wireless environments. The benefits are most pronounced for busy networks, under fading conditions, or for mobile users.
3D Reasoning from Blocks to Stability

Objects occupy physical space and obey physical laws. To truly understand a scene, we must reason about the space that objects in it occupy, and how each objects is supported stably by each other. In other words, we seek to understand which objects would, if moved, cause other objects to fall. This 3D volumetric reasoning is important for many scene understanding tasks, ranging from segmentation of objects to perception of a rich 3D, physically well-founded, interpretations of the scene. In this paper, we propose a new algorithm to parse a single RGB-D image with 3D block units while jointly reasoning about the segments, volumes, supporting relationships, and object stability. Our algorithm is based on the intuition that a good 3D representation of the scene is one that fits the depth data well, and is a stable, self-supporting arrangement of objects (i.e., one that does not topple). We design an energy function for representing the quality of the block representation based on these properties. Our algorithm fits 3D blocks to the depth values corresponding to image segments, and iteratively optimizes the energy function. Our proposed algorithm is the first to consider stability of objects in complex arrangements for reasoning about the underlying structure of the scene. Experimental results show that our stability-reasoning framework improves RGB-D segmentation and scene volumetric representation.
Semi-supervised learning with kernel locality-constrained linear coding

Semi-supervised learning uses both labeled and unlabeled data for machine learning tasks. It's especially useful in the scenarios where labeled data is very scarce or expensive to obtain. In this work, we present kernel LLC, the kernel locality-constrained linear coding within a data-dependent kernel space, for data representation. The data-dependent kernel captures the underlying data geometry on the ambient feature space. The kernel LLC further exploits the locality association among the data on its manifold. Promising results on both image classification and content-based image retrieval scenarios suggest kernel LLC to be a good candidate for data representation in semi-supervised learning.
Optimal Pose for Face Recognition

Researchers in psychology have well studied the impact of the pose of a face as perceived by humans, and concluded that the so-called 3/4 view, halfway between the front view and the profile view, is the easiest for face recognition by humans. For face recognition by machines, while much work has been done to create recognition algorithms that are robust to pose variation, little has been done in finding the most representative pose for recognition. In this paper, we use a number of algorithms to evaluate face recognition performance when various poses are used for training. The result, similar to findings in psychology that the 3/4 view is the best, is also justified by the discrimination power of different regions on the face, computed from both the appearance and the geometry of these regions. We believe our study is both scientifically interesting and practically beneficial for many applications.
Fast image alignment in the Fourier domain

In this paper we propose a framework for gradient descent image alignment in the Fourier domain. Specifically, we propose an extension to the classical Lucas & Kanade (LK) algorithm where we represent the source and template image's intensity pixels in the complex 2D Fourier domain rather than in the 2D spatial domain. We refer to this approach as the Fourier LK (FLK) algorithm. The FLK formulation is especially advantageous, over traditional LK, when it comes to pre-processing the source and template images with a bank of filters (e.g., Gabor filters) as: (i) it can handle substantial illumination variations, (ii) the inefficient pre-processing filter bank step can be subsumed within the FLK algorithm as a sparse diagonal weighting matrix, (iii) unlike traditional LK the computational cost is invariant to the number of filters and as a result far more efficient, (iv) this approach can be extended to the inverse compositional form of the LK algorithm where nearly all steps (including Fourier transform and filter bank pre-processing) can be pre-computed leading to an extremely efficient and robust approach to gradient descent image matching. We demonstrate robust image matching performance on a variety of objects in the presence of substantial illumination differences with exactly the same computational overhead as that of traditional inverse compositional LK during fitting.
Retrieval of 3D protein structures

A protein molecule is made of a long chain of amino acid sequences that fold into a complex three-dimensional structure. It is often the geometrical shapes that determine the protein functions. In molecular biology, researchers use sequence alignment and structure matching to compare the similarity among proteins. Considering proteins as 3D structures, we propose a novel algorithm to identify geometry-based features to retrieve similar proteins without having to deal with complex chemical characteristics and biological properties. A Web-based Dali server, which performs well in three-dimensional structure matching, is used as the ground truth to evaluate our algorithm. Our system performs close to the ground truth with much simplicity and efficiency.
Extracting adaptive contextual cues from unlabeled regions

Existing approaches to contextual reasoning for enhanced object detection typically utilize other labeled categories in the images to provide contextual information. As a consequence, they inadvertently commit to the granularity of information implicit in the labels. Moreover, large portions of the images may not belong to any of the manually-chosen categories, and these unlabeled regions are typically neglected. In this paper, we overcome both these drawbacks and propose a contextual cue that exploits unlabeled regions in images. Our approach adaptively determines the granularity (scene, inter-object, intra-object, etc.) at which contextual information is captured. In order to extract the proposed contextual cue, we consider a scene to be a structured configuration of objects and regions; just as an object is a composition of parts. We thus learn our proposed “contextual meta-objects” using any off-the-shelf object detector, which makes our proposed cue widely accessible to the community. Our results show that incorporating our proposed cue provides a relative improvement of 12% over a state-of-the-art object detector on the challenging PASCAL dataset.
Joint geometry/texture progressive coding of 3D models

Files of 3D models are often large and hence time-consuming to retrieve from a storage device or to download from the network. Most 3D viewing applications need to obtain the entire file of a 3D model in order to display it, even when the user is interested only in a small part, or a low-resolution version, of the model. Therefore, progressive coding that enables multiresolution transmission of 3D models is desired. However, existing research in progressive coding of 3D models either focuses on progressive coding of the geometry only, or progressive coding of the texture only. In this paper, we propose a joint geometry/texture coding scheme for 3D models. With this technique, geometry and texture are coded jointly and transmitted progressively to the users. Bits that represent perceptually more important information, in terms of both geometry and texture, are transmitted before the less important bits, allowing the user to obtain the best available visual quality at any time during the transmission.