nils-herrmann/clean_scirepeval huggingface dataset:
Towards visual-inertial SLAM for mobile augmented reality

The basic idea of augmented reality is to augment the view of a user or camera with virtual objects. Real-time camera tracking is an enabling technology for augmented reality. Besides the high estimation precision that is needed to allow for pixel accurate augmentations, mobile augmented reality applications impose further requirements on the tracking method. One important aspect is robustness in the presence of quick and erratic camera motions, which are typical for a handheld or head-mounted camera. This thesis investigates robustness and accuracy of real-time markerless camera tracking for mobile augmented reality applications considering both known and unknown environments of different complexity. The major solution strategies are: visual-inertial sensor fusion, decoupling of pose and structure estimation and unification of computer vision and recursive filtering techniques. First, a model-based camera tracking system that fuses visual and inertial measurements in the extended Kalman filter is developed. It uses an affine illumination invariant image processing method that exploits the pose prediction obtained from the sensor fusion algorithm and a textured CAD model of the environment to predict the appearances of corner features in the camera images. In several experiments, the system is demonstrated to work robustly in realistic environments of different complexity, under varying light conditions, fast and erratic camera motions and even short periods without visible features. Compared to vision-only tracking, the system shows less jitter and the computational costs are reduced both due to an accurate prediction of the feature appearances and locations in the images and a reduced demand on features in general. Tracking in partially known environments is addressed by developing a vision-only system, which requires minimal pre-knowledge about the structure of the target scene and derives 3D information online. The system combines robust and efficient sequential structure from motion methods with a simplified stochastic model and recursive localisation of 3D point features. It scales with the size of the map, since pose and structure estimation are decoupled. Compared to an ordinary sequential structure from motion system, the developed method provides higher accuracy in both camera and feature localisation and significantly less drift. This is demonstrated in different experiments based on simulated data and in realistic mid-scale environments. Based on these results, a conceptual solution for visual-inertial simultaneous localisation and mapping in large-scale environments is developed. The idea is to combine the marginalised particle filter for visual-inertial pose estimation with undelayed feature initialisation and localisation on a per-particle basis. The essential algorithms for pose and structure estimation are developed and a proof-of-concept implementation is evaluated in a simple test environment, demonstrating that the novel tracking strategy unifies and enhances the previous developments.
Dual-Band and Polarization-Flexible Cavity Antenna Based on Substrate Integrated Waveguide

In this letter, a novel dual-band and polarization-flexible substrate integrated waveguide (SIW) cavity antenna is proposed. The SIW cavity used for the antenna is excited by a conventional TE120 mode for its first resonance. With the intervention of the slot, a second resonance excited by a modified- TE120 mode is also generated, thereby providing a broadside radiation pattern at two resonant frequencies. In addition, the proposed antenna has two orthogonal feeding lines. Therefore, it is possible to provide any six major polarization states. In this letter, three major polarization cases are simulated and compared to measured results. Since modern communication systems require multifunctional antennas, the proposed antenna concept is a promising candidate.
The Tri-Wheel: A novel wheel-leg mobility concept

The Tri-Wheel is a novel wheel-leg locomotion concept inspired by work with first responders. Through its two modes of operation-Driving Mode and Tumbling Mode- this mechanism is able to both drive quickly on smooth surfaces at roughly 1.7 times desired speed and climb objects as tall as 67% of the diameter of the mechanism. The unique gearing configuration that facilitates these dual capabilities is described, and testing quantifies that this nonprecision gearing system is roughly 81% efficient in a worst-case scenario of loading. This work introduces the Tri-Wheel concept and provides preliminary testing to validate its predicted operating characteristics.
Pixel-Wise Attentional Gating for Scene Parsing

To achieve dynamic inference in pixel labeling tasks, we propose Pixel-wise Attentional Gating (PAG), which learns to selectively process a subset of spatial locations at each layer of a deep convolutional network. PAG is a generic, architecture-independent, problem-agnostic mechanism that can be readily "plugged in" to an existing model with fine-tuning. We utilize PAG in two ways: 1) learning spatially varying pooling fields that improve model performance without the extra computation cost associated with multi-scale pooling, and 2) learning a dynamic computation policy for each pixel to decrease total computation (FLOPs) while maintaining accuracy. We extensively evaluate PAG on a variety of per-pixel labeling tasks, including semantic segmentation, boundary detection, monocular depth and surface normal estimation. We demonstrate that PAG allows competitive or state-of-the-art performance on these tasks. Our experiments show that PAG learns dynamic spatial allocation of computation over the input image which provides better performance trade-offs compared to related approaches (e.g., truncating deep models or dynamically skipping whole layers). Generally, we observe PAG can reduce computation by 10% without noticeable loss in accuracy and performance degrades gracefully when imposing stronger computational constraints.
The role of visual attention in saccadic eye movements.

The relationship between saccadic eye movements and covert orienting or visual spatial attention was investigated in two experiments. In the first experiment, subjects were required to make a saccade to a specified location while also detecting a visual target presented just prior to the eye movement. Detection accuracy was highest when the location of the target coincided with the location of the saccade, suggesting that subjects use spatial attention in the programming and/or execution of saccadic eye movements. In the second experiment, subjects were explicitly directed to attend to a particular location and to make a saccade to the same location or to a different one. Superior target detection occurred at the saccade location regardless of attention instructions. This finding shows that subjects cannot move their eyes to one location and attend to a different one. The result of these experiments suggest that visuospatial attention is an important mechanism in generating voluntary saccadic eye movements.
Stock Market Forecasting Using Machine Learning Algorithms

Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend with the aid of SVM. Numerical results indicate a prediction accuracy of 74.4% in NASDAQ, 76% in S&P500 and 77.6% in DJIA. The same algorithm is also applied with different regression algorithms to trace the actual increment in the markets. Finally, a simple trading model is established to study the performance of the proposed prediction algorithm against other benchmarks.
Powered Ankle-Foot Prosthesis for the Improvement of Amputee Ambulation

This paper presents the mechanical design, control scheme, and clinical evaluation of a novel, motorized ankle-foot prosthesis, called MIT Powered Ankle-Foot Prosthesis. Unlike a conventional passive-elastic ankle-foot prosthesis, this prosthesis can provide active mechanical power during the stance period of walking. The basic architecture of the prosthesis is a unidirectional spring, configured in parallel with a force-controllable actuator with series elasticity. With this architecture, the ankle-foot prosthesis matches the size and weight of the human ankle, and is also capable of delivering high mechanical power and torque observed in normal human walking. We also propose a biomimetic control scheme that allows the prosthesis to mimic the normal human ankle behavior during walking. To evaluate the performance of the prosthesis, we measured the rate of oxygen consumption of three unilateral transtibial amputees walking at self-selected speeds to estimate the metabolic walking economy. We find that the powered prosthesis improves amputee metabolic economy from 7% to 20% compared to the conventional passive-elastic prostheses (Flex-Foot Ceterus and Freedom Innovations Sierra), even though the powered system is twofold heavier than the conventional devices. This result highlights the benefit of performing net positive work at the ankle joint to amputee ambulation and also suggests a new direction for further advancement of an ankle-foot prosthesis.
Special thanks to the reviewers

In this paper we simulate an auction system including an auctioneer selling goods to a set of bidders. Bidders are agents simulating human behaviours. Bids must be increasing and the winner is the bidder with the highest bid at the end of the auction. This highest bid has to be paid by the winner to obtain the goods. This paper focuses on bidders with three types of behaviour: the last-bidders, who bid at the last moment; the classic-bidders, who normally bid during the whole auction; and the incremental-bidders, who also bid during the whole auction, but incremental-bidders only add the smallest possible amount to increase the price of the bids. The simulation permits to analyse the behaviours of incremental-bidders when interacting with the classic and last-bidders. The simulation results show that incremental-bidders win less often than last or classic bidders, but when they win, they obtain the goods for a lower price on average. When incremental-bidders interact with last-bidders (and not with classic-bidders), there is a lot of activity in the auctions as incremental-bidders tend to bid often between each other. There is less activity when they interact with classic bidders (and not with last-bidders), and this activity decreases with the time.
Virtual reality exposure therapy for Vietnam veterans with posttraumatic stress disorder.

Virtual reality (VR) integrates real-time computer graphics, body-tracking devices, visual displays, and other sensory input devices to immerse a participant in a computer-generated virtual environment that changes in a natural way with head and body motion. VR exposure (VRE) is proposed as an alternative to typical imaginal exposure treatment for Vietnam combat veterans with posttraumatic stress disorder (PTSD).



This report presents the results of an open clinical trial using VRE to treat Vietnam combat veterans who have DSM-IV PTSD. In 8 to 16 sessions, 10 male patients were exposed to 2 virtual environments: a virtual Huey helicopter flying over a virtual Vietnam and a clearing surrounded by jungle.



Clinician-rated PTSD symptoms as measured by the Clinician Administered PTSD Scale, the primary outcome measure, at 6-month follow-up indicated an overall statistically significant reduction from baseline (p = .0021) in symptoms associated with specific reported traumatic experiences. All 8 participants interviewed at the 6-month follow-up reported reductions in PTSD symptoms ranging from 15% to 67%. Significant decreases were seen in all 3 symptom clusters (p < .02). Patient self-reported intrusion symptoms as measured by the Impact of Event Scale were significantly lower (p < .05) at 3 months than at baseline but not at 6 months, although there was a clear trend toward fewer intrusive thoughts and somewhat less avoidance.



Virtual reality exposure therapy holds promise for treating PTSD in Vietnam veterans.
Personality Consistency in Dogs: A Meta-Analysis

Personality, or consistent individual differences in behavior, is well established in studies of dogs. Such consistency implies predictability of behavior, but some recent research suggests that predictability cannot be assumed. In addition, anecdotally, many dog experts believe that 'puppy tests' measuring behavior during the first year of a dog's life are not accurate indicators of subsequent adult behavior. Personality consistency in dogs is an important aspect of human-dog relationships (e.g., when selecting dogs suitable for substance-detection work or placement in a family). Here we perform the first comprehensive meta-analysis of studies reporting estimates of temporal consistency of dog personality. A thorough literature search identified 31 studies suitable for inclusion in our meta-analysis. Overall, we found evidence to suggest substantial consistency (r = 0.43). Furthermore, personality consistency was higher in older dogs, when behavioral assessment intervals were shorter, and when the measurement tool was exactly the same in both assessments. In puppies, aggression and submissiveness were the most consistent dimensions, while responsiveness to training, fearfulness, and sociability were the least consistent dimensions. In adult dogs, there were no dimension-based differences in consistency. There was no difference in personality consistency in dogs tested first as puppies and later as adults (e.g., 'puppy tests') versus dogs tested first as puppies and later again as puppies. Finally, there were no differences in consistency between working versus non-working dogs, between behavioral codings versus behavioral ratings, and between aggregate versus single measures. Implications for theory, practice, and future research are discussed.
Investigation of Time–Frequency Features for GPR Landmine Discrimination

Ground-penetrating radar (GPR) is capable to detect plastic antipersonnel landmines as well as other subsurface targets. In order to reduce false alarms, an option of automatic landmine discrimination from neutral minelike targets would be very useful. This paper presents a possibility for such discrimination and analyzes it experimentally. The authors investigate time-frequency features of an ultrawideband (UWB) target response for the discrimination between buried landmines and other objects. The discrimination method includes the extraction of an early-time target impulse response, its time-frequency transformation, and the extraction of time-frequency features based on a singular value decomposition of the transformed image. In order to take into account the changes in the UWB target signals, the experimental conditions are completely controlled to focus on the behavior of the target's response with respect to its depth and the horizontal position of the GPR above it. The dependence of the features on the GPR bandwidth is analyzed as well. The Mahalanobis distance is used as a criterion for optimal discrimination. The obtained results define the best features and conditions when the landmine discrimination is successful. For comparison, the discriminant power of the proposed features has been tested on a dataset, acquired during a field campaign in Angola
Low-Voltage Wilson Current Mirrors in CMOS

In this paper, we describe three simple low-voltage CMOS analogs of the Wilson current mirror that function well at all current levels, ranging from weak inversion to strong inversion. Each of these current mirrors can operate on a low power-supply voltage of a diode drop plus two saturation voltages and features a wide output-voltage swing with a cascode-type incremental output impedance. Two of the circuits requires an input voltage of a diode drop plus a saturation voltage while the third one features a low input voltage of a saturation voltage. We present experimental results from versions of these three current mirrors that were fabricated in a 0.5-mum CMOS process through MOSIS, comparing them with CMOS implementations of the conventional Wilson and super-Wilson current mirrors.
Unsupervised Image-to-Image Translation Networks

Most of the existing image-to-image translation frameworks—mapping an image in one domain to a corresponding image in another—are based on supervised learning, i.e., pairs of corresponding images in two domains are required for learning the translation function. This largely limits their applications, because capturing corresponding images in two different domains is often a difficult task. To address the issue, we propose the UNsupervised Image-to-image Translation (UNIT) framework, which is based on variational autoencoders and generative adversarial networks. The proposed framework can learn the translation function without any corresponding images in two domains. We enable this learning capability by combining a weight-sharing constraint and an adversarial training objective. Through visualization results from various unsupervised image translation tasks, we verify the effectiveness of the proposed framework. An ablation study further reveals the critical design choices. Moreover, we apply the UNIT framework to the unsupervised domain adaptation task and achieve better results than competing algorithms do in benchmark datasets.
An Investigation of Recurrent Neural Architectures for Drug Name Recognition

Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical texts and classify them into predefined categories. State-of-the-art DNR approaches heavily rely on hand-crafted features and domain-specific resources which are difficult to collect and tune. For this reason, this paper investigates the effectiveness of contemporary recurrent neural architectures the Elman and Jordan networks and the bidirectional LSTM with CRF decoding at performing DNR straight from the text. The experimental results achieved on the authoritative SemEval-2013 Task 9.1 benchmarks show that the bidirectional LSTM-CRF ranks closely to highly-dedicated, hand-crafted systems.
Neural Response Generation With Dynamic Vocabularies

We study response generation for open domain conversation in chatbots. Existing methods assume that words in responses are generated from an identical vocabulary regardless of their inputs, which not only makes them vulnerable to generic patterns and irrelevant noise, but also causes a high cost in decoding. We propose a dynamic vocabulary sequence-tosequence (DVS2S) model which allows each input to possess their own vocabulary in decoding. In training, vocabulary construction and response generation are jointly learned by maximizing a lower bound of the true objective with a Monte Carlo sampling method. In inference, the model dynamically allocates a small vocabulary for an input with the word prediction model, and conducts decoding only with the small vocabulary. Because of the dynamic vocabulary mechanism, DVS2S eludes many generic patterns and irrelevant words in generation, and enjoys efficient decoding at the same time. Experimental results on both automatic metrics and human annotations show that DVS2S can significantly outperform state-of-the-art methods in terms of response quality, but only requires 60% decoding time compared to the most efficient baseline.
Backlobe and Sidelobe Suppression of a Q-Band Patch Antenna Array by Using Substrate Integrated Coaxial Line Feeding Technique

A low profile aperture-coupled patch antenna array with high front-to-back-ratio (FTBR) is designed at <italic>Q </italic>-band by using substrate integrated coaxial line technique, the intrinsic shielded enclosure of which is capable of constructing an equal-phase and amplitude-tapering feeding network with backlobe suppression. An illustrative example of a 6 <inline-formula><tex-math notation="LaTeX">$\times$</tex-math></inline-formula> 6 patch antenna array is designed and fabricated with a 2 <inline-formula><tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> 2 prior verification module by using multilayer liquid crystal polymer process. Measured results indicate that the array can achieve a gain of 17.6 dBi at 41.5 GHz and the first sidelobe levels is <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>24.1 dB in the <italic>E</italic>-plane and <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>20.4 dB in the <italic>H</italic>-plane. The holistic FTBR is better than 27 dB in both planes.
Ultra-wideband compact ring Sector printed monopole antenna

A novel compact ultra-wideband circular ring sector printed monopole antenna with decreasing Sectoral angle and modified feeding structure is proposed. A detailed parametric study for Sectoral angle decreasing from equivalent circular monopole patch to 330° ring Sector up till 60° ring Sectoral patch is presented. With decreasing angle ultra-wide band response with increasing lower frequency of the realized bandwidth is obtained in all the Sectoral patches. For Sectoral angle of 90°, an optimum response with offset feeding structure is realized. All the proposed Sectoral configurations yield bandwidth from less than 3 GHz to more than 10 GHz, thereby covering entire ultra-wide band frequency range. The proposed configurations yields gain of more than 1 to 2 dBi over the entire ultra-wide band frequency range.
Facilitating Object Detection and Recognition through Eye Gaze

When compared to image recognition, object detection is a much more challenging task because it requires the accurate real-time localization of an object in the target image. In interaction scenarios, this pipeline can be simplified by incorporating the users’ point of regard. Wearable eye trackers can estimate the gaze direction, but lack own processing capabilities. We enable mobile gaze-aware applications by developing an open-source platform which supports mobile eye tracking based on the Pupil headset and a smartphone running Android OS. Through our platform, we offer researchers and developers a rapid prototyping environment for gaze-enabled applications. We describe the concept, our current progress, and research implications.
Towards Cooperative Driving: Involving the Driver in an Autonomous Vehicle's Decision Making

Although there are already fully autonomous vehicles on the roads for testing purposes, a rollout is far away. Autonomous vehicles are still not able to handle everyday driving and remain reliant on the driver when they reach their system limitations. One suggested approach to this problem is handing over the control entirely to the driver, which might become annoying when such situations occur frequently. In contrast, we suggest the usage of cooperative interfaces to avoid full handovers in situations in which the system needs the driver, for instance to approve or monitor a specific maneuver. A driving simulator study with 32 participants revealed that they felt comfortable choosing how the system should handle a situation. They reportedly assessed the situations first instead of relying blindly on the system and were able to handle every situation safely. We report lessons learned regarding cooperative interaction and interfaces, and their in-lab evaluation.