nils-herrmann/clean_scirepeval huggingface dataset:
Speeding up Semantic Segmentation for Autonomous Driving

Deep learning has considerably improved semantic image segmentation. However, its high accuracy is traded against larger computational costs which makes it unsuitable for embedded devices in self-driving cars. We propose a novel deep network architecture for image segmentation that keeps the high accuracy while being efficient enough for embedded devices. The architecture consists of ELU activation functions, a SqueezeNet-like encoder, followed by parallel dilated convolutions, and a decoder with SharpMask-like refinement modules. On the Cityscapes dataset, the new network achieves higher segmentation accuracy than other networks that are tailored to embedded devices. Simultaneously the frame-rate is still sufficiently high for the deployment in autonomous vehicles.
Decision-Based Transcription of Jazz Guitar Solos Using a Harmonic Bident Analysis Filter Bank and Spectral Distribution Weighting

Jazz guitar solos are improvised melody lines played on one instrument on top of a chordal accompaniment (comping). As the improvisation happens spontaneously, a reference score is non-existent, only a lead sheet. There are situations, however, when one would like to have the original melody lines in the form of notated music, see the Real Book. The motivation is either for the purpose of practice and imitation or for musical analysis. In this work, an automatic transcriber for jazz guitar solos is developed. It resorts to a very intuitive representation of tonal music signals: the pitchgram. No instrument-specific modeling is involved, so the transcriber should be applicable to other pitched instruments as well. Neither is there the need to learn any note profiles prior to or during the transcription. Essentially, the proposed transcriber is a decision tree, thus a classifier, with a depth of 3. It has a (very) low computational complexity and can be run on-line. The decision rules can be refined or extended with no or little musical education. The transcriber’s performance is evaluated on a set of ten jazz solo excerpts and compared with a state-of-the-art transcription system for the guitar plus PYIN. We achieve an improvement of 34 % w.r.t. the reference system and 19 % w.r.t. PYIN in terms of the F-measure. Another measure of accuracy, the error score, attests that the number of erroneous pitch detections is reduced by more than 50 % w.r.t. the reference system and by 45 % w.r.t. PYIN.
5G roadmap: 10 key enabling technologies

The fifth generation (5G) mobile communication networks will require a major paradigm shift to satisfy the increasing demand for higher data rates, lower network latencies, better energy efficiency, and reliable ubiquitous connectivity. With prediction of the advent of 5G systems in the near future, many efforts and revolutionary ideas have been proposed and explored around the world. The major technological breakthroughs that will bring renaissance to wireless communication networks include (1) a wireless software-defined network, (2) network function virtualization, (3) millimeter wave spectrum, (4) massive MIMO, (5) network ultra-densification, (6) big data and mobile cloud computing, (7) scalable Internet of Things, (8) device-to-device connectivity with high mobility, (9) green communications, and (10) new radio access techniques. In this paper, the state-of-the-art and the potentials of these ten enabling technologies are extensively surveyed. Furthermore, the challenges and limitations for each technology are treated in depth, while the possible solutions are highlighted.
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection

We address the problem of activity detection in continuous, untrimmed video streams. This is a difficult task that requires extracting meaningful spatio-temporal features to capture activities, accurately localizing the start and end times of each activity. We introduce a new model, Region Convolutional 3D Network (R-C3D), which encodes the video streams using a three-dimensional fully convolutional network, then generates candidate temporal regions containing activities, and finally classifies selected regions into specific activities. Computation is saved due to the sharing of convolutional features between the proposal and the classification pipelines. The entire model is trained end-to-end with jointly optimized localization and classification losses. R-C3D is faster than existing methods (569 frames per second on a single Titan X Maxwell GPU) and achieves state-of-the-art results on THUMOS’14. We further demonstrate that our model is a general activity detection framework that does not rely on assumptions about particular dataset properties by evaluating our approach on ActivityNet and Charades. Our code is available at http://ai.bu.edu/r-c3d/
Deep Stacked Networks with Residual Polishing for Image Inpainting

Deep neural networks have shown promising results in image inpainting even if the missing area is relatively large. However, most of the existing inpainting networks introduce undesired artifacts and noise to the repaired regions. To solve this problem, we present a novel framework which consists of two stacked convolutional neural networks that inpaint the image and remove the artifacts, respectively. The first network considers the global structure of the damaged image and coarsely fills the blank area. Then the second network modifies the repaired image to cancel the noise introduced by the first network. The proposed framework splits the problem into two distinct partitions that can be optimized separately, therefore it can be applied to any inpainting algorithm by changing the first network. Second stage in our framework which aims at polishing the inpainted images can be treated as a denoising problem where a wide range of algorithms can be employed. Our results demonstrate that the proposed framework achieves significant improvement on both visual and quantitative evaluations.
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a category-agnostic affordance prediction algorithm to select and execute among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017
Pulse position modulation for FSO systems: Capacity and channel coding

For terrestrial free space optical (FSO) systems, we investigate the use of pulse position modulation (PPM) which has the interesting property of being average-energy efficient. We first discuss the upper bound on the information transmission rate for a Gaussian channel. Next, we consider the more practical aspect of channel coding and look for a suitable solution for the case of Q-ary PPM. Instead of using a non-binary channel code, we suggest to use a simple binary convolutional code and to perform iterative soft demodulation (demapping) and channel decoding at the receiver. We show that the proposed scheme is quite efficient against demodulation errors due to the receiver noise. Moreover, we propose a simple soft-demapping method of low complexity for the general case of Q-ary PPM. The receiver complexity remains then reasonable in view of implementation in a terrestrial FSO system.
Analysing Political Discourse : Toward a Cognitive Approach 1

The critical study of political discourse has up until very recently rested solely within the domain of the social sciences. Working within a linguistics framework, Critical Discourse Analysis (CDA), in particular Fairclough (Fairclough 1989, 1995a,  2001; Fairclough and Wodak 1997), has been heavily influenced by Foucault. 2 The linguistic theory that CDA and critical linguistics especially (which CDA subsumes) has traditionally drawn upon is Halliday‟s Systemic-Functional Grammar, which is largely concerned with the function of language in the social structure 3 (Fowler et al. 1979; Fowler 1991; Kress and Hodge 1979).
Lower eyelid anatomy: an update.

The gross anatomy of the lower eyelid is analogous to that of the upper eyelid, however, the lower eyelid has a more simplified structure with less dynamic movement. Common malpositions of the lower eyelid include entropion and ectropion, rehabilitative surgery of which requires a thorough understanding of lower eyelid anatomy. Furthermore, precise anatomic knowledge is a prerequisite for both reconstructive and cosmetic lower eyelid surgery in order for it to be performed appropriately. In this review, we present the clinical anatomy of the structures of the lower eyelid, as well as highlighting relevant surgical implications. Featured here are the structure of the different eyelid lamellae, the lower eyelid retractors and their relations, the orbital septum, fat pad compartments, and Lockwood ligament.
Systematic Literature Review on Opinion Mining of Big Data for Government Intelligence

With the advent of new technology paradigm, SMAC (Social media, Mobile, Analytics and Cloud) the information network generates an infinite ocean of data spreading faster and larger than earlier. A high quality information extracted from this massive volume of data, named as big data, urges the development of an efficient and effective decision support system and powerful strategic tools in the area of government intelligence. This pool of information can be explored for the benefit of an organization/system and to better understand its stakeholder needs by collecting, mining opinions about every point or subject of interest. Digitally intelligent and smart governance has been identified as a dynamic field with new studies being reported at various research avenues. The need to review, analyze and evaluate research studies across literature is thus fostered motivating us to identify existing trends, research gaps and potential directions of future work within this domain. This paper intends to provide a systematic literature review within the promising area of opinion mining and its application to the area of government
Thermodynamical Approach to the Traveling Salesman Problem : An Efficient Simulation Algorithm

We present a Monte Carlo algorithm to find approximate solutions of the traveling salesman problem. The algorithm generates randomly the permutations of the stations of the traveling salesman trip, with probability depending on the length of the corresponding route. Reasoning by analogy with statistical thermodynamics, we use the probability given by the Boltzmann-Gibbs distribution. Surprisingly enough, using this simple algorithm, one can get very close to the optimal solution of the problem or even find the true optimum. We demonstrate this on several examples. We conjecture that the analogy with thermodynamics can offer a new insight into optimization problems and can suggest efficient algorithms for solving them.
Transferrable Representations for Visual Recognition

Transferrable Representations for Visual Recognition by Jeffrey Donahue Doctor of Philosophy in Computer Science University of California, Berkeley Professor Trevor Darrell, Chair The rapid progress in visual recognition capabilities over the past several years can be attributed largely to improvements in generic and transferrable feature representations, particularly learned representations based on convolutional networks (convnets) trained “end-to-end” to predict visual semantics given raw pixel intensity values. In this thesis, we analyze the structure of these convnet representations and their generality and transferability to other tasks and settings. We begin in Chapter 2 by examining the hierarchical semantic structure that naturally emerges in convnet representations from large-scale supervised training, even when this structure is unobserved in the training set. Empirically, the resulting representations generalize surprisingly well to classification in related yet distinct settings. Chapters 3 and 4 showcase the flexibility of convnet-based representations for prediction tasks where the inputs or targets have more complex structure. Chapter 3 focuses on representation transfer to the object detection and semantic segmentation tasks in which objects must be localized within an image, as well as labeled. Chapter 4 augments convnets with recurrent structure to handle recognition problems with sequential inputs (e.g., video activity recognition) or outputs (e.g., image captioning). Across each of these domains, end-to-end fine-tuning of the representation for the target task provides a substantial additional performance benefit. Finally, we address the necessity of label supervision for representation learning. In Chapter 5 we propose an unsupervised learning approach based on generative models, demonstrating that some of the transferrable semantic structure learned by supervised convnets can be learned from images alone.
Using Grounded Theory as a Method of Inquiry : Advantages and Disadvantages

There are many challenges and criticisms attached to the conduct of research, none the least of which is a notion that much of the research undertaken in professional disciplines such as nursing may not have clinical and/or practical relevance. While there are a plethora of qualitative research methods that individuals must consider when designing research studies, one method stands out Grounded Theory (GT). Grounded theory was developed in the early 1960’s by Glaser and Strauss. With its theoretical orientation based in sociology, GT strives to understand and explain human behavior through inductive reasoning processes (Elliott & Lazenbatt, 2005). Because of its emphasis on the utilization of a variety of data sources that are grounded in particular contexts, GT provides a natural theoretical fit when designing nursing research studies. In this article, the authors provide an overview of GT and then describe the appropriateness, advantages, and disadvantages of applying it as part of the research design process. Additionally, the authors highlight the importance of taking a reflexive position to stay engaged while interacting with the data, and explore how to apply GT theory to particular research questions and studies. Finally, the strengths and limitations of this method of inquiry as applied to nursing research using a brief case study approach is presented.
System-Level Optimization of Accelerator Local Memory for Heterogeneous Systems-on-Chip

In modern system-on-chip architectures, specialized accelerators are increasingly used to improve performance and energy efficiency. The growing complexity of these systems requires the use of system-level design methodologies featuring high-level synthesis (HLS) for generating these components efficiently. Existing HLS tools, however, have limited support for the system-level optimization of memory elements, which typically occupy most of the accelerator area. We present a complete methodology for designing the private local memories (PLMs) of multiple accelerators. Based on the memory requirements of each accelerator, our methodology automatically determines an area-efficient architecture for the PLMs to guarantee performance and reduce the memory cost based on technology-related information. We implemented a prototype tool, called Mnemosyne, that embodies our methodology within a commercial HLS flow. We designed 13 complex accelerators for selected applications from two recently-released benchmark suites (Perfect and CortexSuite). With our approach we are able to reduce the memory cost of single accelerators by up to 45%. Moreover, when reusing memory IPs across accelerators, we achieve area savings that range between 17% and 55% compared to the case where the PLMs are designed separately.
Fast training of support vector machines using sequential minimal optimization, advances in kernel methods

This chapter describes a new algorithm for training Support Vector Machines: Sequential Minimal Optimization, or SMO. Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. Because large matrix computation is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while a standard projected conjugate gradient (PCG) chunking algorithm scales somewhere between linear and cubic in the training set size. SMO's computation time is dominated by SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. For the MNIST database, SMO is as fast as PCG chunking; while for the UCI Adult database and linear SVMs, SMO can be more than 1000 times faster than the PCG chunking algorithm.
Adverse drug events and medication errors: detection and classification methods.

Investigating the incidence, type, and preventability of adverse drug events (ADEs) and medication errors is crucial to improving the quality of health care delivery. ADEs, potential ADEs, and medication errors can be collected by extraction from practice data, solicitation of incidents from health professionals, and patient surveys. Practice data include charts, laboratory, prescription data, and administrative databases, and can be reviewed manually or screened by computer systems to identify signals. Research nurses, pharmacists, or research assistants review these signals, and those that are likely to represent an ADE or medication error are presented to reviewers who independently categorize them into ADEs, potential ADEs, medication errors, or exclusions. These incidents are also classified according to preventability, ameliorability, disability, severity, stage, and responsible person. These classifications, as well as the initial selection of incidents, have been evaluated for agreement between reviewers and the level of agreement found ranged from satisfactory to excellent (kappa = 0.32-0.98). The method of ADE and medication error detection and classification described is feasible and has good reliability. It can be used in various clinical settings to measure and improve medication safety.
Sex steroid hormone receptors, their ligands, and nuclear and non-nuclear pathways

The ability of a cell to respond to a particular hormone depends on the presence of specific receptors for those hormones. Once the hormone has bound to its receptor, and following structural and biochemical modifications to the receptor, it separates from cytoplasmic chaperone proteins, thereby exposing the nuclear localization sequences that result in the activation of the receptor and initiation of the biological actions of the hormone on the target cell. In addition, recent work has demonstrated new pathways of steroid signaling through orphan and cell surface receptors that contribute to more rapid, “non-nuclear” or non-transcriptional effects of steroid hormones, often involving G-protein-mediated pathways. This review will summarize some of these studies for estrogens, androgens and progestins.
Fighting against phishing attacks: state of the art and future challenges

In the last few years, phishing scams have rapidly grown posing huge threat to global Internet security. Today, phishing attack is one of the most common and serious threats over Internet where cyber attackers try to steal user’s personal or financial credentials by using either malwares or social engineering. Detection of phishing attacks with high accuracy has always been an issue of great interest. Recent developments in phishing detection techniques have led to various new techniques, specially designed for phishing detection where accuracy is extremely important. Phishing problem is widely present as there are several ways to carry out such an attack, which implies that one solution is not adequate to address it. Two main issues are addressed in our paper. First, we discuss in detail phishing attacks, history of phishing attacks and motivation of attacker behind performing this attack. In addition, we also provide taxonomy of various types of phishing attacks. Second, we provide taxonomy of various solutions proposed in the literature to detect and defend from phishing attacks. In addition, we also discuss various issues and challenges faced in dealing with phishing attacks and spear phishing and how phishing is now targeting the emerging domain of IoT. We discuss various tools and datasets that are used by the researchers for the evaluation of their approaches. This provides better understanding of the problem, current solution space and future research scope to efficiently deal with such attacks.
CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces

In this paper, we formalize the idea behind capsule nets of using a capsule vector rather than a neuron activation to predict the label of samples. To this end, we propose to learn a group of capsule subspaces onto which an input feature vector is projected. Then the lengths of resultant capsules are used to score the probability of belonging to different classes. We train such a Capsule Projection Network (CapProNet) by learning an orthogonal projection matrix for each capsule subspace, and show that each capsule subspace is updated until it contains input feature vectors corresponding to the associated class. We will also show that the capsule projection can be viewed as normalizing the multiple columns of the weight matrix simultaneously to form an orthogonal basis, which makes it more effective in incorporating novel components of input features to update capsule representations. In other words, the capsule projection can be viewed as a multi-dimensional weight normalization in capsule subspaces, where the conventional weight normalization is simply a special case of the capsule projection onto 1D lines. Only a small negligible computing overhead is incurred to train the network in low-dimensional capsule subspaces or through an alternative hyper-power iteration to estimate the normalization matrix. Experiment results on image datasets show the presented model can greatly improve the performance of the state-of-the-art ResNet backbones by 10− 20% and that of the Densenet by 5− 7% respectively at the same level of computing and memory expenses. The CapProNet establishes the competitive state-of-the-art performance for the family of capsule nets by significantly reducing test errors on the benchmark datasets.