nils-herrmann/clean_scirepeval huggingface dataset:
Efficient region search for object detection

We propose a branch-and-cut strategy for efficient region-based object detection. Given an oversegmented image, our method determines the subset of spatially contiguous regions whose collective features will maximize a classifier's score. We formulate the objective as an instance of the prize-collecting Steiner tree problem, and show that for a family of additive classifiers this enables fast search for the optimal object region via a branch-and-cut algorithm. Unlike existing branch-and-bounddetection methods designed for bounding boxes, our approach allows scoring of irregular shapes — which is especially critical for objects that do not conform to a rectangular window. We provide results on three challenging object detection datasets, and demonstrate the advantage of rapidly seeking best-scoring regions rather than subwindow rectangles.
Stress reduction correlates with structural changes in the amygdala.

Stress has significant adverse effects on health and is a risk factor for many illnesses. Neurobiological studies have implicated the amygdala as a brain structure crucial in stress responses. Whereas hyperactive amygdala function is often observed during stress conditions, cross-sectional reports of differences in gray matter structure have been less consistent. We conducted a longitudinal MRI study to investigate the relationship between changes in perceived stress with changes in amygdala gray matter density following a stress-reduction intervention. Stressed but otherwise healthy individuals (N = 26) participated in an 8-week mindfulness-based stress reduction intervention. Perceived stress was rated on the perceived stress scale (PSS) and anatomical MR images were acquired pre- and post-intervention. PSS change was used as the predictive regressor for changes in gray matter density within the bilateral amygdalae. Following the intervention, participants reported significantly reduced perceived stress. Reductions in perceived stress correlated positively with decreases in right basolateral amygdala gray matter density. Whereas prior studies found gray matter modifications resulting from acquisition of abstract information, motor and language skills, this study demonstrates that neuroplastic changes are associated with improvements in a psychological state variable.
Apple disease classification using color, texture and shape features from images

The presence of diseases in several kinds of fruits is the major factor of production and the economic degradation of the agricultural industry worldwide. An approach for the apple disease classification using color, texture and shape based features is investigated and experimentally verified in this paper. The primary steps of the introduced image processing based method is as follows; 1) infected fruit part detection is done with the help of K-Means clustering method, 2) color, texture and shape based features are computed over the segmented image and combined to form the single descriptor, and 3) multi-class Support Vector Machine is used to classify the apples into one of the infected or healthy categories. Apple fruit is taken as the test case in this study with 3 categories of diseases, namely blotch, rot and scab as well as healthy apples. The experimentation points out that the introduced method based is better as compared to the individual features. It also point out that shape feature is not better suited for this purpose.
Design of auxetic structures via mathematical optimization.

The design of auxetic structures is traditionally based on engineering experience, see, for instance, refs. . The goal of this article is to demonstrate how an existing auxetic structure can be improved by structural optimization techniques and manufactured by a selective electron beam melting (SEBM) system. Auxetic materials possess negative Poisson’s ratios ν which is in contrast to almost all engineering materials (e. g. metals ν ≈ 0.3). The negative Poisson’s ratio translates into the unusual geometric effect that these materials expand when stretched and contract when compressed. This behavior is of interest for example for fastener systems and fi lters.  It also results in interesting mechanical behavior like increased penetration resistance for large negative Poisson’s ratios  and in the case of isotropy for higher shear strength relative to the Young’s modulus  of the structure. The auxetic behavior can always be traced back to complex mesoor microscopic geometric mechanisms, e. g. unfolding of inverted elements or rotation of star shaped rigid units relative to each other.  These geometries can be realized on vastly different scales from specifi cally designed polymers to reactor cores.
Constraint-Based Approaches for Balancing Bike Sharing Systems

In order to meet the users’ demand, bike sharing systems must be regularly rebalanced. The problem of balancing bike sharing systems (BBSS) is concerned with designing optimal tours and operating instructions for relocating bikes among stations to maximally comply with the expected future bike demands. In this paper, we tackle the BBSS by means of Constraint Programming: first, we introduce two novel constraint models for the BBSS including a smart branching strategy that focusses on the most promising routes. Second, in order to speed-up the search process, we incorporate both models in a Large Neighborhood Search (LNS) approach that is adapted to the respective CP model. Third, we perform an extensive computational evaluation on instances based on real-world data, where we see that the LNS approach outperforms the Branch & Bound approach and is competitive with other existing approaches.
Planning optimal grasps

In this paper we will address the problem of planning optimal grasps. Two general optimality criteria, that consider the total finger force and the maximum finger force will be introduced and discussed. Moreover their formalization, using various metrics on a space of generalized forces, will be detailed. The geometric interpretation of the two criteria will lead to an efficient planning algorithm. An example of its use in a robotic environment equipped with two-jaw and three-jaw grippers will also be shown.
Leveraging Wikipedia Table Schemas for Knowledge Graph Augmentation

General solutions to augment Knowledge Graphs (KGs) with facts extracted from Web tables aim to associate pairs of columns from the table with a KG relation based on the matches between pairs of entities in the table and facts in the KG. These approaches suffer from intrinsic limitations due to the incompleteness of the KGs. In this paper we investigate an alternative solution, which leverages the patterns that occur on the schemas of a large corpus of Wikipedia tables. Our experimental evaluation, which used DBpedia as reference KG, demonstrates the advantages of our approach over state-of-the-art solutions and reveals that we can extract more than 1.7M of facts with an estimated accuracy of 0.81 even from tables that do not expose any fact on the KG.
The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks

In the past three decades, earnings have been one of the most researched variables in accounting. Empirical research provided substantial evidence on its usefulness in the capital markets but evidence in predicting earnings has been limited, yielding inconclusive results. The purpose of this study is to validate and extend prior research in predicting earnings by examining aggregate and industry-specific data. A sample of 10,509 firm-year observations included in the Compustat database for the period 1982-91 is used in the study. The stepwise logistic regression results of the present study indicated that nine earnings and non-earnings variables can be used to predict earnings. These predictor variables are not identical to those reported in prior studies. These results are also extended to the manufacturing industry. Two new variables are identified to be significant in this industry. Moreover, an Artificial Neural Network (ANN) approach is employed to complement the logistic regression results. The ANN model's performance is at least as high as the logistic regression model's predictive ability.
Disentangling Loneliness: Differential Effects of Subjective Loneliness, Network Quality, Network Size, and Living Alone on Physical, Mental, and Cognitive Health.

To examine whether different measures of social disconnectedness-subjective loneliness, network quality, network size, living alone-have differential effects on the health of older adults.



We used a longitudinal sample of the German Aging Survey ( N = 4,184) and analyzed seven measures of health (life satisfaction, positive affect, negative affect, depression, cognitive performance, physical functioning, and pulmonary function) via regression analyses.



We found that subjective loneliness and network quality best predicted mental health; contrarily, network size and living alone best predicted physical and cognitive health.



Different measures of social disconnectedness have differential effects on health. Therefore, using only global measures or one aspect of social disconnectedness might obfuscate potential health hazards. Researchers and practitioners should be mindful of differences between these measures and should include multiple aspects of social disconnectedness in their research and practice. Future studies should explore the causes why these measures and their effects differ.
LTCC Packages With Embedded Phased-Array Antennas for 60 GHz Communications

A low-cost, fully-integrated antenna-in-package solution for 60 GHz phased-array systems is demonstrated. Sixteen patch antennas are integrated into a 28 mm × 28 mm ball grid array together with a flip-chip attached transmitter or receiver IC. The packages have been implemented using low temperature co-fired ceramic technology. 60 GHz interconnects, including flip-chip transitions and via structures, are optimized using full-wave simulation. Anechoic chamber measurement has shown ~ 5 dBi unit antenna gain across all four IEEE 802.15.3c channels, achieving excellent model-to-hardware correlation. The packaged transmitter and receiver ICs, mounted on evaluation boards, have demonstrated beam-steered, non-line-of-sight links with data rates up to 5.3 Gb/s.
Sliding-Window Superposition Coding: Two-User Interference Channels

A low-complexity coding scheme is developed to achieve the r at egion of maximum likelihood decoding for interference channels. As in the classical rate-splitting mu ltiple access scheme by Grant, Rimoldi, Urbanke, and Whitin g, the proposed coding scheme uses superposition of multiple c od words with successive cancellation decoding, which can be implemented using standard point-to-point encoders and decoders. Unlike rate-splitting multiple access, which is not rate-optimal for multiple receivers, the proposed coding s cheme transmits codewords over multiple blocks in a stagger ed manner and recovers them successively over sliding decodin g wi dows, achieving the single-stream optimal rate region as well as the more general Han–Kobayashi inner bound for the tw o-user interference channel. The feasibility of this schem e in practice is verified by implementing it using commercial c hannel codes over the two-user Gaussian interference chann el. I. I NTRODUCTION For high data rates and massive connectivity, next-generat ion cellular networks are expected to deploy many small base stations. While such dense deployment provides the ben fit of bringing radio closer to end users, it also increases the amount of interference from neighboring cells. Consequ ently, efficient and effective management of interference is expected to become one of the main challenges for high-spe ctral-efficiency, low-power, broad-coverage wireless communications. Over the past few decades, several techniques at different p otocol layers [1]–[3] have been proposed to mitigate adver se effects of interference in wireless networks. One importan t conceptual technique at the physical layer is simultaneous decoding[4, Section 6.2], . In this decoding method, each receive r attempts to recover both the intended and a subset of the interfering codewords at the same time. When the inter fer nce is strong [6], [7] and weak [8]–[11], simultaneous The material in this paper was presented in part in the IEEE In ternational Symposium on Information Theory (ISIT) 2014, H onolulu, HI, and in part in the IEEE Globecom Workshops (GC Wkshps) 2014, Austin , TX. L. Wang is jointly with the Department of Electrical Enginee ring, Stanford University, Stanford, CA 94305 USA and the De partment of Electrical Engineering Systems, Tel Aviv University, Tel Aviv, Israe l ( mail: wanglele@stanford.edu). Y.-H. Kim is with the Department of Electrical and Computer E ngineering, University of California, San Diego, La Jolla, CA 92093 USA (e-mail: yhk@ucsd.edu). C.-Y. Chen is with Broadcom Limited, 190 Mathilda Place, Sun nyvale, CA 94086 USA (email: uscychen@gmail.com). H. Park is with the School of Electronics and Computer Engine eri g, Chonnam National University, Gwangju 61186, Korea ( e-mail: hpark1@jnu.ac.kr). E. Şaşoğlu is with Intel Corporation, Santa Clara, CA 950 54 USA (e-mail: eren.sasoglu@gmail.com).
VDNet: an infrastructure-less UAV-assisted sparse VANET system with vehicle location prediction

Vehicular Ad Hoc Network (VANET) has been a hot topic in the past few years. Compared with vehicular networks where vehicles are densely distributed, sparse VANET have more realistic significance. The first challenge of a sparse VANET system is that the network suffers from frequent disconnections. The second challenge is to adapt the transmission route to the dynamic mobility pattern of the vehicles. Also, some infrastructural requirements are hard to meet when deploying a VANET widely. Facing these challenges, we devise an infrastructure-less unmanned aerial vehicle (UAV) assisted VANET system called Vehicle-Drone hybrid vehicular ad hoc Network (VDNet), which utilizes UAVs, particularly quadrotor drones, to boost vehicle-to-vehicle data message transmission under instructions conducted by our distributed vehicle location prediction algorithm. VDNet takes the geographic information into consideration. Vehicles in VDNet observe the location information of other vehicles to construct a transmission route and predict the location of a destination vehicle. Some vehicles in VDNet equips an on-board UAV, which can deliver data message directly to destination, relay messages in a multi-hop route, and collect location information while flying above the traffic. The performance evaluation shows that VDNet achieves high efficiency and low end-to-end delay with controlled communication overhead.
Semantic edge detection based on deep metric learning

Edge detection is a fundamental computer vision problem and has widely applications. Though it has recently achieved great improvement due to robust features extracted using deep convolutional neural networks(CNNs). There still exists a certain room for improvement. In this paper, we propose a novel end-to-end deep metric learning algorithm for semantic edge detection. Our deep learning network is composed of three parts. The first part is a guide module, called deep detail layer is used to suppress some low frequency information as a preprocessing layer. The second part is a deep convolutional encoder-decoder network that extracts the robust multi-scale features. And the last part is the metric learning loss, we firstly develop the deep metric learning for edge detection, the goal of the metric learning is to learn a robust metric space for the same category, i.e., edge distance space and non-edge distance space. Experiments and comparisons on BSDS500 dataset (ODS F-score is 0.788) show that the proposed algorithm is absolutely competitive to the state-of-the-art in terms of qualitative and quantitative evaluations.
Quantization Mimic: Towards Very Tiny CNN for Object Detection

In this paper, we propose a simple and general framework for training very tiny CNNs (e.g. VGG with the number of channels reduced to 1 32 ) for object detection. Due to limited representation ability, it is challenging to train very tiny networks for complicated tasks like detection. To the best of our knowledge, our method, called Quantization Mimic, is the first one focusing on very tiny networks. We utilize two types of acceleration methods: mimic and quantization. Mimic improves the performance of a student network by transfering knowledge from a teacher network. Quantization converts a full-precision network to a quantized one without large degradation of performance. If the teacher network is quantized, the search scope of the student network will be smaller. Using this feature of the quantization, we propose Quantization Mimic. It first quantizes the large network, then mimic a quantized small network. The quantization operation can help student network to better match the feature maps from teacher network. To evaluate our approach, we carry out experiments on various popular CNNs including VGG and Resnet, as well as different detection frameworks including Faster R-CNN and R-FCN. Experiments on Pascal VOC and WIDER FACE verify that our Quantization Mimic algorithm can be applied on various settings and outperforms state-of-the-art model acceleration methods given limited computing resouces.
Adversarial synthesis learning enables segmentation without target modality ground truth

A lack of generalizability is one key limitation of deep learning based segmentation. Typically, one manually labels new training images when segmenting organs in different imaging modalities or segmenting abnormal organs from distinct disease cohorts. The manual efforts can be alleviated if one is able to reuse manual labels from one modality (e.g., MRI) to train a segmentation network for a new modality (e.g., CT). Previously, two stage methods have been proposed to use cycle generative adversarial networks (CycleGAN) to synthesize training images for a target modality. Then, these efforts trained a segmentation network independently using synthetic images. However, these two independent stages did not use the complementary information between synthesis and segmentation. Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT. The end-to-end EssNet achieved significantly higher median Dice similarity coefficient (0.9188) than the two stages strategy (0.8801), and even higher than canonical multi-atlas segmentation (0.9125) and ResNet method (0.9107), which used the CT manual labels.
Associations between online friendship and Internet addiction among adolescents and emerging adults.

The past decades have witnessed a dramatic increase in the number of youths using the Internet, especially for communicating with peers. Online activity can widen and strengthen the social networks of adolescents and emerging adults (Subrahmanyam & Smahel, 2011), but it also increases the risk of Internet addiction. Using a framework derived from Griffiths (2000a), this study examined associations between online friendship and Internet addiction in a representative sample (n = 394) of Czech youths ages 12-26 years (M = 18.58). Three different approaches to friendship were identified: exclusively offline, face-to-face oriented, Internet oriented, on the basis of the relative percentages of online and offline associates in participants' friendship networks. The rate of Internet addiction did not differ by age or gender but was associated with communication styles, hours spent online, and friendship approaches. The study revealed that effects between Internet addiction and approaches to friendship may be reciprocal: Being oriented toward having more online friends, preferring online communication, and spending more time online were related to increased risk of Internet addiction; on the other hand, there is an alternative causal explanation that Internet addiction and preference for online communication conditions young people's tendency to seek friendship from people met online.
Using Case Studies in Research

Case study as a research strategy often emerges as an obvious option for students and other new researchers who are seeking to undertake a modest scale research project based on their workplace or the comparison of a limited number of organisations. The most challenging aspect of the application of case study research in this context is to lift the investigation from a descriptive account of ‘what happens’ to a piece of research that can lay claim to being a worthwhile, if modest addition to knowledge. This article draws heavily on established textbooks on case study research and related areas, such as Yin, 1994, Hamel et al., 1993, Eaton, 1992, Gomm, 2000, Perry, 1998, and Saunders et al., 2000 but seeks to distil key aspects of case study research in such a way as to encourage new researchers to grapple with and apply some of the key principles of this research approach. The article explains when case study research can be used, research design, data collection, and data analysis, and finally offers suggestions for drawing on the evidence in writing up a report or dissertation.
AssocGEN: Engine for analyzing metadata based associations in digital evidence

Traditionally, sources of digital evidence are analyzed by individually examining the various artifacts contained therein and using the artifact metadata to validate authenticity and sequence them. However, when artifacts from forensic images, folders, log files, and network packet dumps have to be analyzed, the examination of the artifacts and the metadata in isolation presents a significant challenge. Ideally, when a source is examined, it is a valuable task to determine correlations between the artifacts and group the related artifacts. Such a grouping can simplify the task of analysis by minimizing the need for human intervention. By virtue of the value that metadata bring to an investigation and its ubiquitous nature, metadata based associations is the first step in realizing such correlations automatically during analysis. In this paper, we present the AssocGEN analysis engine which uses the metadata to determine associations between artifacts that belong to files, logs and network packet dumps, and identifies metadata associations to group the related artifacts. A metadata association can represent any type of value match1 or relationship that is deemed relevant in the context of an investigation. We have conducted preliminary evaluation of AssocGEN on the classical ownership problem to highlight the benefits of incorporating this approach in existing forensic tools.