nils-herrmann/clean_scirepeval huggingface dataset:
Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery

Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognition and pose estimation of surgical instruments that generalizes to different surgical applications. At its core, we propose a novel scene model in order to simultaneously recognize multiple instruments as well as their parts. We use a Convolutional Neural Network architecture to embody our model and show that the cross-entropy loss is well suited to optimize its parameters which can be trained in an end-to-end fashion. An additional advantage of our approach is that instrument detection at test time is achieved while avoiding the need for scale-dependent sliding window evaluation. This allows our approach to be relatively parameter free at test time and shows good performance for both instrument detection and tracking. We show that our approach surpasses state-of-the-art results on in-vivo retinal microsurgery image data, as well as ex-vivo laparoscopic sequences.
14 Requirements Engineering for Agile Methods

Collecting, understanding, and managing requirements is a critical aspect in all development methods. This is true for Agile Methods as well. In particular, several agile practices deal with requirements in order to implement them correctly and satisfy the needs of the customer. These practices focus on a continuous interaction with the customer to address the requirements evolution over time, prioritize them, and deliver the most valuable functionalities first. This chapter introduces Agile Methods as the implementation of the principles of the lean production in software development. Therefore, Agile Methods focus on continuous process improvement through the identification and the removal of waste, whatever does not add value for the customer.
Compositionality of optimal control laws

We present a theory of compositionality in stochastic optimal control, showing how task-optimal controllers can be constructed from certain primitives. The primitives are themselves feedback controllers pursuing their own agendas. They are mixed in proportion to how much progress they are making towards their agendas and how compatible their agendas are with the present task. The resulting composite control law is provably optimal when the problem belongs to a certain class. This class is rather general and yet has a number of unique properties – one of which is that the Bellman equation can be made linear even for non-linear or discrete dynamics. This gives rise to the compositionality developed here. In the special case of linear dynamics and Gaussian noise our framework yields analytical solutions (i.e. non-linear mixtures of LQG controllers) without requiring the final cost to be quadratic. More generally, a natural set of control primitives can be constructed by applying SVD to Green’s function of the Bellman equation. We illustrate the theory in the context of human arm movements. The ideas of optimality and compositionality are both very prominent in the field of motor control, yet they have been difficult to reconcile. Our work makes this possible.
Design, Analysis, and Prototyping of a New Wound-Rotor Axial Flux Brushless Resolver

Resolvers are electromagnetic position sensors that are widely used in industrial applications. In this study, a new axial flux brushless resolver (AFBR) with a wound rotor is introduced. In the proposed resolver, the core of the rotary transformer (RT) is omitted, and an innovative design is used to supply the excitation winding of the resolver. The proposed resolver can be built with smaller dimensions, and its thermal stability and mechanical strength are increased compared with conventional AFBRs. The performance of the proposed structure is simulated and optimized by using a three-dimensional time-stepping finite element method. The effect of the leakage flux of the RT on the excitation and signal windings is also discussed for both the proposed and conventional structures. A prototype based on the optimized topology is constructed and tested. Good agreement is obtained between the simulation and experimental results, validating the success of the proposed resolver.
Incremental free-space carving for real-time 3D reconstruction

Almost all current multi-view methods are slow, and thus suited to offline reconstruction. This paper presents a set o f heuristic space-carving algorithms with a focus on speed over detail. The algorithms discretize space via the 3D Delaunay triangulation, and they carve away the volumes that violate free-space or visibility constraints. Whereas sim ilar methods exist, our algorithms are fast and fully incrementa l. They encompass a dynamic event-driven approach to reconstruction that is suitable for integration with online SLAM or Structure-from-Motion. We integrate our algorithms with PTAM , and we realize a complete system that reconstructs 3D geometry from video in real-time. Experiments on typical real-world inpu ts demonstrate online performance with modest hardware. We provide run-time complexity analysis and show that the perevent processing time is independent of the number of images previously processed: a requirement for real-time operation on lengthy image sequences.
Stochastic Geometry for Wireless Networks

Covering point process theory, random geometric graphs, and coverage processes, this rigorous introduction to stochastic geometry will enable you to obtain powerful, general estimates and bounds of wireless network performance, and make good design choices for future wireless architectures and protocols that efficiently manage interference effects. Practical engineering applications are integrated with mathematical theory, with an understanding of probability the only prerequisite. At the same time, stochastic geometry is connected to percolation theory and the theory of random geometric graphs, and is accompanied by a brief introduction to the R statistical computing language. Combining theory and hands-on analytical techniques, this is a comprehensive guide to the spatial stochastic models essential for modeling and analysis of wireless network performance.
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data

We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added l1-norm penalty term. The problem as formulated is convex but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes. We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case. Our first algorithm uses block coordinate descent, and can be interpreted as recursive l1-norm penalized regression. Our second algorithm, based on Nesterov’s first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright and Jordan [2006]), we show that these same algorithms can be used to solve an approximate sparse maximum likelihood problem for the binary case. We test our algorithms on synthetic data, as well as on gene expression and senate voting records data.
IKP: Turning a PKI Around with Decentralized Automated Incentives

Despite a great deal of work to improve the TLS PKI, CA misbehavior continues to occur, resulting in unauthorized certificates that can be used to mount man-in-the-middle attacks against HTTPS sites. CAs lack the incentives to invest in higher security, and the manual effort required to report a rogue certificate deters many from contributing to the security of the TLS PKI. In this paper, we present IKP, a platform that automates responses to unauthorized certificates and provides incentives for CAs to behave correctly and for others to report potentially unauthorized certificates. Domains in IKP specify criteria for their certificates, and CAs specify reactions such as financial penalties that execute in case of unauthorized certificate issuance. By leveraging smart contracts and blockchain-based consensus, we can decentralize IKP while still providing automated incentives. We describe a theoretical model for payment flows and implement IKP in Ethereum to show that decentralizing and automating PKIs with financial incentives is both economically sound and technically viable.
mobileSpiro: accurate mobile spirometry for self-management of asthma

Effective management of asthma and other respiratory diseases requires constant monitoring and frequent data collection using a spirometer and longitudinal analysis. However, even after three decades of clinical use, there are very few personalized spirometers available on the market, especially those connecting to smartphones. To address this problem, we have developed mobileSpiro, a portable, low-cost spirometer intended for patient self-monitoring. The mobileSpiro API, and the accompanying Android application, interfaces with the spirometer hardware to capture, process and analyze the data. Our key contributions are automated algorithms on the smartphone which play a technician's role in detecting erroneous patient maneuvers, ensuring data quality, and coaching patients with easy-to-understand feedback, all packaged as an Android app. We demonstrate that mobileSpiro is as accurate as a commercial ISO13485 device, with an inter-device deviation in flow reading of less than 8%, and detects more than 95% of erroneous cough maneuvers in a public CDC dataset.
BigBIRD: A large-scale 3D database of object instances

The state of the art in computer vision has rapidly advanced over the past decade largely aided by shared image datasets. However, most of these datasets tend to consist of assorted collections of images from the web that do not include 3D information or pose information. Furthermore, they target the problem of object category recognition - whereas solving the problem of object instance recognition might be sufficient for many robotic tasks. To address these issues, we present a high-quality, large-scale dataset of 3D object instances, with accurate calibration information for every image. We anticipate that “solving” this dataset will effectively remove many perception-related problems for mobile, sensing-based robots. The contributions of this work consist of: (1) BigBIRD, a dataset of 100 objects (and growing), composed of, for each object, 600 3D point clouds and 600 high-resolution (12 MP) images spanning all views, (2) a method for jointly calibrating a multi-camera system, (3) details of our data collection system, which collects all required data for a single object in under 6 minutes with minimal human effort, and (4) multiple software components (made available in open source), used to automate multi-sensor calibration and the data collection process. All code and data are available at http://rll.eecs.berkeley.edu/bigbird.
noWorkflow: Capturing and Analyzing Provenance of Scripts

We propose noWorkflow, a tool that transparently captures provenance of scripts and enables reproducibility. Unlike existing approaches, noWorkflow is non-intrusive and does not require users to change the way they work – users need not wrap their experiments in scientific workflow systems, install version control systems, or instrument their scripts. The tool leverages Software Engineering techniques, such as abstract syntax tree analysis, reflection, and profiling, to collect different types of provenance, including detailed information about the underlying libraries. We describe how noWorkflow captures multiple kinds of provenance and the different classes of analyses it supports: graph-based visualization; differencing over provenance trails; and inference queries.
The GuideCane-applying mobile robot technologies to assist the visually impaired

The GuideCane is a novel device designed to help blind or visually impaired users navigate safely and quickly among obstacles and other hazards. During operation, the user pushes the lightweight GuideCane forward. When the GuideCane’s ultrasonic sensors detect an obstacle, the embedded computer determines a suitable direction of motion that steers the GuideCane and the user around it. The steering action results in a very noticeable force felt in the handle, which easily guides the user without any conscious effort on his/her part.
Device-to-Device Discovery for Proximity-Based Service in LTE-Advanced System

In this paper, we propose a device-to-device (D2D) discovery scheme as a key enabler for a proximity-based service in the Long-Term Evolution Advanced (LTE-A) system. The proximity-based service includes a variety of services exploiting the location information of user equipment (UE), for example, the mobile social network and the mobile marketing. To realize the proximity-based service in the LTE-A system, it is necessary to design a D2D discovery scheme by which UE can discover another UE in its proximity. We design a D2D discovery scheme based on the random access procedure in the LTE-A system. The proposed random-access-based D2D discovery scheme is advantageous in that 1) the proposed scheme can be readily applied to the current LTE-A system without significant modification; 2) the proposed scheme discovers pairs of UE in a centralized manner, which enables the access or core network to centrally control the formation of D2D communication networks; and 3) the proposed scheme adaptively allocates resource blocks for the D2D discovery to prevent underutilization of radio resources. We analyze the performance of the proposed D2D discovery scheme. A closed-form formula for the performance is derived by means of the stochastic geometry-based approach. We show that the analysis results accurately match the simulation results.
Effect of meditation on stress-induced changes in cognitive functions.

The objective of this study was to study the effects of meditation on stress-induced changes in cognitive functions.



The study was conducted on 32 healthy adult male student volunteers who had never practiced meditation before the study. The study consisted of practicing 20 minutes of guided meditation and administration of psychologic stress to the subjects. The psychologic stress was administered to the subjects by asking them to play a (preselected) stressful computer game. The subjects were asked to meditate either before or after the administration of psychologic stress. For the control group measurements, the subjects were asked to wait quietly for an equivalent period of meditation time.



The outcome measures were galvanic skin response (GSR), heart rate (HR), electromyography (EMG), sympathetic reactivity (QTc/QS2 ratio), cortisol, and acute psychologic stress scores. The central nervous system functions were assessed using Wechsler memory scale and visual-choice reaction time (VCRT). These parameters were measured both at the beginning and at the end of the intervention, using a pre-post experimental test design.



Computer game stress was associated with a significant increase in physiologic (GSR, EMG, HR, QTc/QS2) and psychologic (acute stress questionnaire scores) markers of stress. Meditation was associated with relaxation (significant decrease in GSR, EMG, QTc/QS2, and acute stress questionnaire scores). Meditation, if practiced before the stressful event, reduced the adverse effects of stress. Memory quotient significantly increased, whereas cortisol level decreased after both stress and meditation. VCRT showed no significant change.



Practice of meditation produced a relaxation response even in the young adult subjects who had never practiced meditation before. The practice of meditation reduced the physiologic stress responses without taking away the beneficial effect of stress, namely, improved memory scores.
Histogram Matching and Global Initialization for Laser-only SLAM in Large Unstructured Environments

This paper presents an enhanced algorithm for matching laser scan maps using histogram correlations. The histogram representation effectively summarizes a map's salient features such that pairs of maps can be matched efficiently without any prior guess as to their alignment. The histogram matching algorithm has been enhanced in order to work well in outdoor unstructured environments by using entropy metrics, weighted histograms and proper thresholding of quality metrics. Thus our large-scale scan-matching SLAM implementation has a vastly improved ability to close large loops in real-time even when odometry is not available. Our experimental results have demonstrated a successful mapping of the largest area ever mapped to date using only a single laser scanner. We also demonstrate our ability to solve the lost robot problem by localizing a robot to a previously built map without any prior initialization.
Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature

We have developed Textpresso, a new text-mining system for scientific literature whose capabilities go far beyond those of a simple keyword search engine. Textpresso's two major elements are a collection of the full text of scientific articles split into individual sentences, and the implementation of categories of terms for which a database of articles and individual sentences can be searched. The categories are classes of biological concepts (e.g., gene, allele, cell or cell group, phenotype, etc.) and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e.g., biological process, etc.). Together they form a catalog of types of objects and concepts called an ontology. After this ontology is populated with terms, the whole corpus of articles and abstracts is marked up to identify terms of these categories. The current ontology comprises 33 categories of terms. A search engine enables the user to search for one or a combination of these tags and/or keywords within a sentence or document, and as the ontology allows word meaning to be queried, it is possible to formulate semantic queries. Full text access increases recall of biological data types from 45% to 95%. Extraction of particular biological facts, such as gene-gene interactions, can be accelerated significantly by ontologies, with Textpresso automatically performing nearly as well as expert curators to identify sentences; in searches for two uniquely named genes and an interaction term, the ontology confers a 3-fold increase of search efficiency. Textpresso currently focuses on Caenorhabditis elegans literature, with 3,800 full text articles and 16,000 abstracts. The lexicon of the ontology contains 14,500 entries, each of which includes all versions of a specific word or phrase, and it includes all categories of the Gene Ontology database. Textpresso is a useful curation tool, as well as search engine for researchers, and can readily be extended to other organism-specific corpora of text. Textpresso can be accessed at http://www.textpresso.org or via WormBase at http://www.wormbase.org.
Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape

The high rate of global urbanization has resulted in a rapid increase in informal settlements, which can be defined as unplanned, unauthorized, and/or unstructured housing. Techniques for efficiently mapping these settlement boundaries can benefit various decision making bodies. From a remote sensing perspective, informal settlements share unique spatial characteristics that distinguish them from other types of structures (e.g., industrial, commercial, and formal residential). These spatial characteristics are often captured in high spatial resolution satellite imagery. We analyzed the role of spatial, structural, and contextual features (e.g., GLCM, Histogram of Oriented Gradients, Line Support Regions, Lacunarity) for urban neighborhood mapping, and computed several low-level image features at multiple scales to characterize local neighborhoods. The decision parameters to classify formal-, informal-, and non-settlement classes were learned under Decision Trees and a supervised classification framework. Experiments were conducted on high-resolution satellite imagery from the CitySphere collection, and four different cities (i.e., Caracas, Kabul, Kandahar, and La Paz) with varying spatial characteristics were represented. Overall accuracy ranged from 85% in La Paz, Bolivia, to 92% in Kandahar, Afghanistan. While the disparities between formal and informal neighborhoods varied greatly, many of the image statistics tested proved robust.
You are What you Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food & Drink Habits in Foursquare

Food and drink are two of the most basic needs of human beings. However, as society evolved, food and drink became also a strong cultural aspect, being able to describe strong differences among people. Traditional methods used to analyze cross-cultural differences are mainly based on surveys and, for this reason, they are very difficult to represent a significant statistical sample at a global scale. In this paper, we propose a new methodology to identify cultural boundaries and similarities across populations at different scales based on the analysis of Foursquare check-ins. This approach might be useful not only for economic purposes, but also to support existing and novel marketing and social applications. Our methodology consists of the following steps. First, we map food and drink related check-ins extracted from Foursquare into users’ cultural preferences. Second, we identify particular individual preferences, such as the taste for a certain type of food or drink, e.g., pizza or sake, as well as temporal habits, such as the time and day of the week when an individual goes to a restaurant or a bar. Third, we show how to analyze this information to assess the cultural distance between two countries, cities or even areas of a city. Fourth, we apply a simple clustering technique, using this cultural distance measure, to draw cultural boundaries across countries, cities and regions.
Compact Wide-Beam Circularly-Polarized Microstrip Antenna With a Parasitic Ring for CNSS Application

A compact single-feed circularly-polarized (CP) wide beam microstrip antenna is proposed for CNSS application. The antenna is designed with a double-layer structure, comprising a circular patch with two rectangular stubs along the diameter direction and a parasitic ring right above it. The resonance frequency and the CP characteristics are mainly controlled by the circular patch and the rectangular stubs, respectively. The vertical HPBW (half power beam width) could be widened by the parasitic ring. Experimental results show that the measured vertical HPBW is approximately 140° and the measured out-of-roundness for the horizontal radiation pattern is only 1.1 dB. Besides, it could maintain good low-profile characteristics.