nils-herrmann/clean_scirepeval huggingface dataset:
Tversky loss function for image segmentation using 3D fully convolutional deep networks

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks.
Precise 2.5D facial landmarking via an analysis by synthesis approach

3D face landmarking aims at automatic localization of 3D facial features and has a wide range of applications, including face recognition, face tracking, facial expression analysis. Methods so far developed for pure 2D texture images were shown sensitive to lighting condition changes. In this paper, we present a statistical model-based technique for accurate 3D face landmarking, thus using an ¿analysis by synthesis¿ approach. Our model learns from a training set both variations of global face shapes as well as the local ones in terms of scale-free texture and range patches around each landmark. Given a shape instance, local regions of a new face can be approximated by synthesizing texture and range instances using respectively the texture and range models. By optimizing an objective function describing the similarity of the new face and instances, we can optimize the best shape in order to locate the landmarks. Experimented on more than 1860 face models from FRGC datasets, our method achieves an average of locating errors less than 7 mm for 15 feature points. Compared with a curvature analysis-based method also developed within our team, this learning-based method enables localization of more facial landmarks with a general better accuracy at the cost of a learning step.
Visual Perception of Parallel Coordinate Visualizations

Parallel coordinates is a visualization technique that provides an unbiased representation of high-dimensional data. The parallel configuration of axes treats data dimensions uniformly and is well suited for exploratory visualization. However, first-time users of parallel coordinate visualizations can find the representation confusing and difficult to understand.We used eye tracking to study how parallel coordinate visualizations are perceived, and compared the results to the optimal visual scan path required to complete the tasks. The results indicate that even first-time users quickly learn how to use parallel coordinate visualizations, pay attention to the correct task-specific areas in the visualization, and become rapidly proficient with it.
A massively parallel architecture for a self-organizing neural pattern recognition machine

A neural network architecture for the learning of recognition categories is derived. Real-time network dynamics are completely characterized through mathematical analysis and computer simulations. The architecture self-organizes and self-stabilizes its recognition codes in response to arbitrary orderings of arbitrarily many and arbitrarily complex binary input patterns. Top-down attentional and matching mechanisms are critical in self-stabilizing the code learning process. The architecture embodies a parallel search scheme which updates itself adaptively as the learning process unfolds. After learning self-stabilizes, the search process is automatically disengaged. Thereafter input patterns directly access their recognition codes without any search. Thus recognition time does not grow as a function of code complexity. A novel input pattern can directly access a category if it shares invariant properties with the set of familiar exemplars of that category. These invariant properties emerge in the form of learned critical feature patterns, or prototypes. The architecture possesses a context-sensitive self-scaling property which enables its emergent critical feature patterns to form. They detect and remember statistically predictive configurations of featural elements which are derived ~ ..from the set of all input patterns that are ever experienced. Four types of attentional
Snakes and Strings: New Robotic Components for Rescue Operations

The Japanese government is establishing an International Rescue Complex to promote research and development of key technologies for realization of practical search-and-rescue robots, anticipating for future large-scale earthquakes and other catastrophic disasters. This paper proposes a new paradigm called “snakes and strings”, for developing practical mobile robot systems that may be useful in such situations. “Snakes” stands for snake-like robots, which can skillfully move among the debris of the collapsed buildings. “Strings”, on the other hand, means robotic systems using strings or tethers, such as proposed in the “hyper-tether” research [9]. Theters can continuously supply energy, accomplish reliable communication link, and also exhibit high traction force. This paper will present many new mechanical implementations of snake-like robots developed in our lab., and also explain in detail the new paradigm.
Design and performance analysis of radial flux C-core switched reluctance motor for in-wheel electrical vehicle application

A 12/15 pole dual air-gap single rotor configuration with C-core radial flux switched reluctance motor (RFSRM) is proposed as a potential candidate for in-wheel electrical vehicle application with removable wheel/rotor, without disturbing the stator or stator winding. The proposed topology offers effective stator and rotor contact area without increasing the diameter of electrical motor and has smaller flux path compared to the conventional RFSRM. This arrangement offers good torque with low excitation current. It is easy to wound the winding at stator core compared to toothed or segmented type axial field SRM. In this paper phase inductance and torque are calculated analytically and compared with finite element analysis (FEA) results.
Design of broadband impedance transformer using coupled microstrip transmission lines

In this paper, a novel broadband impedance transformer combining with a symmetric coupled microstrip line and an interconnecting line is presented. It has been proved that a fractional bandwidth of about 90% at −20dB reflection level can be achieved with this structure, and the insert loss is less than 2dB in the matching bandwidth. The measurement results of actual circuit are well agreed with the simulation. Its bandwidth is 2.5 times larger as compared with the traditional quarter-wavelength impedance transformer, and its length is less than half of the traditional one with the help of bending the structure.
Smart Refrigerator Based on Internet of Things (IoT): An Approach to Efficient Food Management

This paper focuses on the technology of the smart refrigerator connected through the Internet of Things (IoT) platform. It deals with the issue of food wastage and efficient food distribution through a network of connected refrigerators within a defined neighborhood. This paper defines the three-layer architecture: front-end layer, gateway layer and the back-end layer that can allow the implementation of the system. This low-cost network can be created through a microcontroller and wireless sensors at the front-end architecture. The process was discussed to determine: the stock item below a defined quantity or threshold limit, determining the product expiration date and eventually determining the purchase or exchange of food items across the network. It also includes the proof of concept technology by allowing connection of an ultrasonic sensor to an IoT platform that can allow the exchange of information on the positioning of the food items inside the refrigerator. This eventually can allow for a large-scale commercial implementation of the project to deal with the current issue of food distribution.
Generalized Canonical Time Warping

Temporal alignment of human motion has been of recent interest due to its applications in animation, tele-rehabilitation and activity recognition. This paper presents generalized canonical time warping (GCTW), an extension of dynamic time warping (DTW) and canonical correlation analysis (CCA) for temporally aligning multi-modal sequences from multiple subjects performing similar activities. GCTW extends previous work on DTW and CCA in several ways: (1) it combines CCA with DTW to align multi-modal data (e.g., video and motion capture data); (2) it extends DTW by using a linear combination of monotonic functions to represent the warping path, providing a more flexible temporal warp. Unlike exact DTW, which has quadratic complexity, we propose a linear time algorithm to minimize GCTW. (3) GCTW allows simultaneous alignment of multiple sequences. Experimental results on aligning multi-modal data, facial expressions, motion capture data and video illustrate the benefits of GCTW. The code is available athttp://humansensing.cs.cmu.edu/ctw.
Learning Framework for Robust Obstacle Detection, Recognition, and Tracking

This paper introduces a general framework for detection, recognition, and tracking preceding vehicles and pedestrians based on a deep learning approach. The proposed framework combines a novel deep learning approach with the use of multiple sources of local patterns and depth information to yield robust on-road vehicle and pedestrian detection, recognition, and tracking. The proposed system is first based on robust obstacle detection to identify obstacles appearing along the road that are likely to be vehicles and pedestrians, implemented as an efficient adaptive U-V disparity algorithm. Second, the results from the obstacle detection stage are input into a novel vehicle and pedestrian recognition system based on a deep learning model that processes multiple sources of depth information and local patterns. Finally, the results from the recognition stage are used to track detected vehicles or pedestrians in the next frame by means of a proposed tracking and validation model. The proposed framework has been thoroughly evaluated by inputting several vehicle and pedestrian data sets that were collected under various driving conditions. Experimental results show that this framework provides robust vehicle and pedestrian detection, recognition, and tracking with high accuracy, and also satisfies the real-time requirements of driver assistance systems.
A 0.6 V Low-Power Wide-Range Delay-Locked Loop in 0.18 $\mu$m CMOS

In this letter, a delay-locked loop (DLL) suitable for low-power and low-voltage operations is presented. To overcome the performance limitations, such as a restricted locking range and elevated output jitters, a novel voltage-controlled delay cell and a phase/frequency detector with a start controller are employed in the proposed DLL. Using a standard 0.18 mum CMOS process, the fabricated circuit exhibits a locking range from 85 to 550 MHz. The measured peak-to-peak and rms jitters at 550 MHz are 25.6 and 3.8 ps, respectively. Operated at a supply voltage of 0.6 V, the power consumption of the DLL circuit varies from 2.4 to 4.2 mW within the entire locking range.
Arduino Programming Language

1.  The Arduino prototyping platform is a creative outlet for any user to produce technology through easily usable hardware and software. However, the steep learning curve of the Arduino programming language holds back many of its users. Through graphical and tangible programming languages users can effectively learn how to program for the Arduino platform. Incorporating tangible human computer interaction in Arduino technology yields positive results in learning ability and enthusiasm of programming language learnt. A new language for Arduino can be developed through interactive and participatory design practices which intends to be valuable for novice programmers.
Double nerve intraneural interface implant on a human amputee for robotic hand control

The principle underlying this project is that, despite nervous reorganization following upper limb amputation, original pathways and CNS relays partially maintain their function and can be exploited for interfacing prostheses. Aim of this study is to evaluate a novel peripheral intraneural multielectrode for multi-movement prosthesis control and for sensory feed-back, while assessing cortical reorganization following the re-acquired stream of data.



Four intrafascicular longitudinal flexible multielectrodes (tf-LIFE4) were implanted in the median and ulnar nerves of an amputee; they reliably recorded output signals for 4 weeks. Artificial intelligence classifiers were used off-line to analyse LIFE signals recorded during three distinct hand movements under voluntary order.



Real-time control of motor output was achieved for the three actions. When applied off-line artificial intelligence reached >85% real-time correct classification of trials. Moreover, different types of current stimulation were determined to allow reproducible and localized hand/fingers sensations. Cortical organization was observed via TMS in parallel with partial resolution of symptoms due to the phantom-limb syndrome (PLS).



tf-LIFE4s recorded output signals in human nerves for 4 weeks, though the efficacy of sensory stimulation decayed after 10 days. Recording from a number of fibres permitted a high percentage of distinct actions to be classified correctly. Reversal of plastic changes and alleviation of PLS represent corollary findings of potential therapeutic benefit.



This study represents a breakthrough in robotic hand use in amputees.
On Diophantine Complexity and Statistical Zero-Knowledge Arguments

We show how to construct practical honest-verifier statistical zero-knowledge Diophantine arguments of knowledge (HVSZK AoK) that a committed tuple of integers belongs to an arbitrary language in bounded arithmetic. While doing this, we propose a new algorithm for computing the Lagrange representation of nonnegative integers and a new efficient representing polynomial for the exponential relation. We apply our results by constructing the most efficient known HVSZK AoK for non-negativity and the first constant-round practical HVSZK AoK for exponential relation. Finally, we propose the outsourcing model for cryptographic protocols and design communication-efficient versions of the Damg̊ard-Jurik multi-candidate voting scheme and of the LipmaaAsokan-Niemi (b + 1)st-price auction scheme that work in this model.
Timeline generation with social attention

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of any given topic. It thus attracts much attention from research communities in recent years. Nevertheless, existing work on timeline generation often ignores an important factor, the attention attracted to topics of interest (hereafter termed "social attention"). Without taking into consideration social attention, the generated timelines may not reflect users' collective interests. In this paper, we study how to incorporate social attention in the generation of timeline summaries. In particular, for a given topic, we capture social attention by learning users' collective interests in the form of word distributions from Twitter, which are subsequently incorporated into a unified framework for timeline summary generation. We construct four evaluation sets over six diverse topics. We demonstrate that our proposed approach is able to generate both informative and interesting timelines. Our work sheds light on the feasibility of incorporating social attention into traditional text mining tasks.
Evaluation of super-voxel methods for early video processing

Supervoxel segmentation has strong potential to be incorporated into early video analysis as superpixel segmentation has in image analysis. However, there are many plausible supervoxel methods and little understanding as to when and where each is most appropriate. Indeed, we are not aware of a single comparative study on supervoxel segmentation. To that end, we study five supervoxel algorithms in the context of what we consider to be a good supervoxel: namely, spatiotemporal uniformity, object/region boundary detection, region compression and parsimony. For the evaluation we propose a comprehensive suite of 3D volumetric quality metrics to measure these desirable supervoxel characteristics. We use three benchmark video data sets with a variety of content-types and varying amounts of human annotations. Our findings have led us to conclusive evidence that the hierarchical graph-based and segmentation by weighted aggregation methods perform best and almost equally-well on nearly all the metrics and are the methods of choice given our proposed assumptions.
Demystifying social cognition: a Hebbian perspective

For humans and monkeys, understanding the actions of others is central to survival. Here we review the physiological properties of three cortical areas involved in this capacity: the STS, PF and F5. Based on the anatomical connections of these areas, and the Hebbian learning rule, we propose a simple but powerful account of how the monkey brain can learn to understand the actions of others by associating them with self-produced actions, at the same time discriminating its own actions from those of others. As this system appears also to exist in man, this network model can provide a framework for understanding human social perception.
IoT security: A layered approach for attacks & defenses

Internet of Things (IoT) has been a massive advancement in the Information and Communication Technology (ICT). It is projected that over 50 billion devices will become part of the IoT in the next few years. Security of the IoT network should be the foremost priority. In this paper, we evaluate the security challenges in the four layers of the IoT architecture and their solutions proposed from 2010 to 2016. Furthermore, important security technologies like encryption are also analyzed in the IoT context. Finally, we discuss countermeasures of the security attacks on different layers of IoT and highlight the future research directions within the IoT architecture.
Monocyte emigration from bone marrow during bacterial infection requires signals mediated by chemokine receptor CCR2

Monocytes recruited to tissues mediate defense against microbes or contribute to inflammatory diseases. Regulation of the number of circulating monocytes thus has implications for disease pathogenesis. However, the mechanisms controlling monocyte emigration from the bone marrow niche where they are generated remain undefined. We demonstrate here that the chemokine receptor CCR2 was required for emigration of Ly6Chi monocytes from bone marrow. Ccr2−/− mice had fewer circulating Ly6Chi monocytes and, after infection with Listeria monocytogenes, accumulated activated monocytes in bone marrow. In blood, Ccr2−/− monocytes could traffic to sites of infection, demonstrating that CCR2 is not required for migration from the circulation into tissues. Thus, CCR2-mediated signals in bone marrow determine the frequency of Ly6Chi monocytes in the circulation.
Mathematical modelling of infectious diseases.

Mathematical models allow us to extrapolate from current information about the state and progress of an outbreak, to predict the future and, most importantly, to quantify the uncertainty in these predictions. Here, we illustrate these principles in relation to the current H1N1 epidemic.



Many sources of data are used in mathematical modelling, with some forms of model requiring vastly more data than others. However, a good estimation of the number of cases is vitally important.


AREAS OF AGREEMENT
Mathematical models, and the statistical tools that underpin them, are now a fundamental element in planning control and mitigation measures against any future epidemic of an infectious disease. Well-parameterized mathematical models allow us to test a variety of possible control strategies in computer simulations before applying them in reality.


AREAS OF CONTROVERSY
The interaction between modellers and public-health practitioners and the level of detail needed for models to be of use.



The need for stronger statistical links between models and data.


AREAS TIMELY FOR DEVELOPING RESEARCH
Greater appreciation by the medical community of the uses and limitations of models and a greater appreciation by modellers of the constraints on public-health resources.
Rotation-discriminating template matching based on Fourier coefficients of radial projections with robustness to scaling and partial occlusion

We consider brightness/contrast-invariant and rotation-discriminating template matching that searches an image to analyze A for a query image Q. We propose to use the complex coefficients of the discrete Fourier transform of the radial projections to compute new rotation-invariant local features. These coefficients can be efficiently obtained via FFT. We classify templates in “stable” and “unstable” ones and argue that any local feature-based template matching may fail to find unstable templates. We extract several stable sub-templates of Q and find them in A by comparing the features. The matchings of the sub-templates are combined using the Hough transform. As the features of A are computed only once, the algorithm can find quickly many different sub-templates in A, and it is suitable for: finding many query images in A; multi-scale searching and partial occlusion-robust template matching.
Conserving Energy Through Neural Prediction of Sensed Data

The constraint of energy consumption is a serious problem in wireless sensor networks (WSNs). In this regard, many solutions for this problem have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points making a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a NAR network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviate the need for sensed data during sensors’ idle periods and save over 65 percent of energy.