nils-herrmann/clean_scirepeval huggingface dataset:
The role of temporal statistics in the transfer of experience in context-dependent reinforcement learning

Reinforcement learning (RL) is an algorithmic theory for learning by experience optimal action control. Two widely discussed problems within this field are the temporal credit assignment problem and the transfer of experience. The temporal credit assignment problem postulates that deciding whether an action is good or bad may not be done upon right away because of delayed rewards. The problem of transferring experience investigates the question of how experience can be generalized and transferred from a familiar context, where it was acquired, to an unfamiliar context, where it may, nevertheless, prove helpful. We propose a controller for modelling such flexibility in a context-dependent reinforcement learning paradigm. The devised controller combines two alternatives of perfect learner algorithms. In the first alternative, rewards are predicted by individual objects presented in a temporal sequence. In the second alternative, rewards are predicted on the basis of successive pairs of objects. Simulations run on both deterministic and random temporal sequences show that only in case of deterministic sequences, a previously acquired context could be retrieved. This suggests a role of temporal sequence information in the generalization and transfer of experience.
Exploring Context with Deep Structured Models for Semantic Segmentation

We propose an approach for exploiting contextual information in semantic image segmentation, and particularly investigate the use of patch-patch context and patch-background context in deep CNNs. We formulate deep structured models by combining CNNs and Conditional Random Fields (CRFs) for learning the patch-patch context between image regions. Specifically, we formulate CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied in order to avoid repeated expensive CRF inference during the course of back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image inputs and sliding pyramid pooling is very effective for improving performance. We perform comprehensive evaluation of the proposed method. We achieve new state-of-the-art performance on a number of challenging semantic segmentation datasets.
ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks

Probabilistic generative neural networks are useful for many applications, such as image classification, speech recognition and occlusion removal. However, the power budget for hardware implementations of neural networks can be extremely tight. To address this challenge we describe a design methodology for using approximate computing methods to implement Approximate Deep Belief Networks (ApproxDBNs) by systematically exploring the use of (1) limited precision of variables; (2) criticality analysis to identify the nodes in the network which can operate with such limited precision while allowing the network to maintain target accuracy levels; and (3) a greedy search methodology with incremental retraining to determine the optimal reduction in precision to enable maximize power savings under user-specified accuracy constraints. Experimental results show that significant bit-length reduction can be achieved by our ApproxDBN with constrained accuracy loss.
On-Display Transparent Half-Diamond Pattern Capacitive Fingerprint Sensor Compatible With AMOLED Display

The need for personal security in portable devices has increased. Among several biometric sensing technologies, fingerprint sensing is the most highlighted method. However, a fingerprint sensor in electronics is stifling the demands for a larger interactive display. In this paper, the bottlenecks of placing the sensor on display is discussed, and an on-display mutual capacitive high-resolution transparent fingerprint sensor with a half-diamond pattern is proposed for adapting to the needs of personal security on a larger screen. To overcome various performance limitations on the on-display fingerprint sensor, a metal mesh is used as an electrode material, and the halfdiamond pattern is applied. The proposed pattern is compatible with the diamond-patterned active matrix organic light emitter diode display. The proposed sensor has 72 × 72 channels in a 6 mm × 6 mm area. The sensor satisfies the criteria set by the Federal Bureau of Investigation for fingerprint sensing with 322 capacitors per inch, and the measured transmittance in the visible light region is 79.7%. The proposed sensor achieved a capacitance variation 2.3 times larger than that achieved by the conventional sensor. The five-channel fingerprint sensing circuit to examine the feasibility of fingerprint detection is fabricated with the TSMC 0.18-μm process.
Performance analysis of massive MIMO networks with random unitary pilot matrices

A common approach to obtain channel state information for massive MIMO networks is to use the same orthogonal training sequences in each cell. We call this the full-pilot reuse (FPR) scheme. In this paper, we study an alternative approach where each cell uses different sets of orthogonal pilot (DOP) sequences. Considering uplink communications with matched filter (MF) receivers, we first derive the SINR in the large system regime where the number of antennas at the base station, the number of users in each cell, and training duration grow large with fixed ratios. For tractability in the analysis, the orthogonal pilots are drawn from Haar distributed random unitary matrices. The resulting expression is simple and easy to compute. As shown by the numerical simulations, the asymptotic SINR approximates the finite-size systems accurately. Secondly, we derive the user capacity of the DOP scheme under a simple power control and show that it is generally better than that of the FPR scheme.
Flight Dynamics Modeling of Dual-Spin Guided Projectiles

This paper presents a complete nonlinear parameter-dependent mathematical model, as well as a procedure for computing the quasi-linear parameter-varying (q-LPV) model of a class of spin-stabilized canard-controlled guided projectiles. The proposed projectile concept possesses a so-called dual-spin configuration: the forward section contains the necessary control and guidance software and hardware, whereas the aft roll-decoupled and rapidly spinning section contains the payload. Wind-axes instead of body-axes variables, as is the case in the existing literature, are preferred for the modeling of the highly coupled pitch/yaw airframe nonlinear dynamics, since they are better suited to control synthesis. A q-LPV model, approximating these nonlinear dynamics around the system equilibrium manifold and capturing their dependence on diverse varying flight parameters, is analytically obtained. In addition, a detailed stability analysis specific to this kind of weapons is performed throughout their large flight envelope using the aforementioned q-LPV model. Furthermore, a study is conducted in order to quantify the influence of reducing the dimension of the flight parameter vector on the exactness of the q-LPV model. Finally, the critical influence on the pitch/yaw-dynamics of the nose-embedded sensor position, and of uncertainty on the various static and dynamic aerodynamic coefficients as well as the aerodynamic angles, is shown.
A Survey of the State of Cloud Computing in Healthcare

Analysts, researchers and organizations alike seem to agree that cloud computing will be a defining trend in the coming decade impacting wide range of businesses and how those businesses are practiced. Large technology companies are already investing millions of dollars in building infrastructure, services, tools and applications to facilitate cloud computing for consumers, organizations and businesses to use and take advantage. It remains to be seen how cloud computing will impact the healthcare business since it is very diverse, complex and unique and presents several challenges such as protecting members health records in addition to following HIPAA guidelines set by federal compliance regulations. In addition to these the rising cost of healthcare solutions is another major concern. Efforts are being made to reduce these costs for consumers and IT will play a big role in achieving it and also improving clinical and quality outcomes for patients. It will be very interesting to see how cloud computing will address and contribute towards these issues in the healthcare industry. The purpose of this paper is to explore the current state and trends of cloud computing in healthcare.
A Multimodal Embedded Sensor System for Scalable Robotic and Prosthetic Fingers

The development of dexterous and robust anthropomorphic hands with rich sensor feedback remains a challenging task for both humanoid robotics as well as prosthetics as of today. The design of hands that are scalable in size and equipped with integrated multimodal sensor systems is a key requirement for advanced control schemes and reactive behaviour. In this paper, we present the design of a scalable and low cost robotic finger with a soft fingertip and position, temperature as well as normal and shear force sensors. All cables and sensors are completely enclosed inside the finger to ensure an anthropometric appearance. The finger is modelled based on a 50th percentile male little finger and can be easily adapted to other dimensions in terms of size and sensor system configuration. We describe the design of the sensor system, provide an experimental analysis for the characterization of the different sensor types in terms of sensor range, resolution, creep, spatial response as well as temperature flux.
Consumer Voice Interface Patterns

Voice User Interfaces (VUI) are a relatively new concept, although voice applications have been around for several years now. The invisible and transient character of sound led to believe that there was no user interface. This misconception rapidly disappeared with the technology advances that triggered the development of novel and rich voice applications for corporate use as well as in cars, phones and everyday appliances. The new and more complex functionality became a whole area known as Voice User Interfaces to address the di erent design forces and complexities behind a voice-based user interface. In the last years we began a pattern language for designing voice applications and documenting the aforementioned design tensions and common practices. In this paper, we continue with this work focusing on more complex issues such as authentication and consumer appliances interfaces.
Daily Mindful Responding Mediates the Effect of Meditation Practice on Stress and Mood: The Role of Practice Duration and Adherence.

Although meditation practice is an important component of many mindfulness-based interventions (MBIs), empirical findings of its effects on psychological functioning are mixed and the mechanisms for the effects remain unclear. Responding with mindfulness (i.e., returning one's attention back to a nonjudgmental, present-oriented awareness) is a fundamental skill practiced in meditations. With repeated meditation practice, this skill is thought to become internalized and be applied to one's daily life. We thus hypothesized that the extent to which individuals responded to daily events with mindfulness would mediate the effects of meditation practice (instance, duration, and adherence to instructions) on psychological well-being.



Using a daily diary methodology, we tracked the meditation practice, use of mindful responding during the day, and psychological outcomes (perceived stress, negative and positive affect) of 117 mindfulness-based stress reduction program participants.



We found that on days when participants meditated, they responded with greater mindfulness to daily events, which accounted for the beneficial effects of meditating on psychological outcomes. Furthermore, findings suggest that on meditation days, longer and more closely adhered meditation practices were independently associated with increases in mindful responding, which in turn were associated with better psychological outcomes.



These results suggest that regular, longer, and more closely adhered meditation practice is an important component of MBIs, in part because it leads to responding more mindfully in daily life, which promotes well-being.
Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications.
Iranian Face Database with age, pose and expression

This paper introduces a database of over 3,600 color images which we collected from 616 different human faces. It includes facial images of people between ages 2-85. Face images of persons with different ages is needed to generate a reliable age classification algorithm. Four different poses, two different expressions, and one image with glasses were taken if the subjects were interested. Information such as career, kind of skin, fingerprint, and cosmetic points (surgical points, fracture or laceration on face) is taken from participants. The background was a white frame and the only illumination was the daylight. We named this database the Iranian face database (IFDB). To evaluate the database the experimental result of a new feature detection algorithm is reported.
Lightweight container-based OpenEPC deployment and its evaluation

The Network Function Virtualization (NFV) paradigm has become mature enough for network operators to start migrating their core architectures to it. NFV presents numerous advantages over the traditional hardware-centric approach in terms of cost, versatility, time-to-market and ease of deployment. Another technology that is experiencing rapid adoption is virtualization using containers. This technology allows the implementation of a virtualized environment which is very close to that obtained from a virtual machine (VM) hypervisor but without the overhead of running multiple operating systems (OSs) on a single machine, resulting in a significant performance boost. Given this background it is natural to seek solutions combining these two approaches. In our case we are interested in deploying OpenEPC, an EPC implementation developed by Core Network Dynamics, using containers. This is, as far as we know, the first container-based deployment of a virtualized LTE core network. In this paper we give a step-by-step description of the process we followed, perform some tests of the resulting platform and analyse its performance.
SHREC ’ 16 Track : 3 D Sketch-Based 3 D Shape Retrieval

Sketch-based 3D shape retrieval has unique representation availability o f the queries and vast applications. Therefore, it has received more and more attentions in the research community o f content-based 3D object retrieval. However, sketch-based 3D shape retrieval is a challenging research topic due to the semantic gap existing between the inaccurate representation of sketches and accurate represe ntation of 3D models. In order to enrich and advance the study of sketch-based 3D shape retrieval, we initialize the res earch on 3D sketch-based 3D model retrieval and collect a 3D sketch dataset based on a developed 3D sketc hing interface which facilitates us to draw 3D sketches in the air while standing in front of a Microsoft Kinect. The objective of this track is to evaluate the performance of different 3D ske tch-based 3D model retrieval algorithms using the hand-drawn 3D sketch query dataset and a generic 3D m odel target dataset. The benchmark contains 300 sketches that are evenly divided into 30 classes, as well as 1 258 3D models that are classified into 90 classes. In this track, nine runs have been submitted by five groups a nd their retrieval performance has been evaluated using seven commonly used retrieval performance metrics. W e wish this benchmark, the comparative evaluation results and the corresponding evaluation code will further prom ote sketch-based 3D shape retrieval and its applications.
“The magic finger technique” a simplified approach for more symmetric results in alar base resection

Alar base surgery is one of the most important and challenging steps of aesthetic rhinoplasty. While an ideally shaped alar base is the goal in a desired nose, nearly all patients have asymmetric nostrils preoperatively. Ethnicity, trauma, cocaine use, or previous rhinoplasties are some factors affecting the width and shape of the nasal base. After the conclusion of all planned rhinoplasty sequences and closure of the mid-columellarincision, we mark the midline inferior to the columella at the nasolabial junction and use acaliper to measure an equal distance from the mid-columellar point to the alar creases on eachside, and mark the medial points of the alar creases. Next we draw on the natural creasesbilaterally extending to 3 o’clock on the right side and 9 o‘clock on the left side as the limit ofthe lateral excisions to avoid scarring. We then gently depress the alae and alar-facial grooveswith the index finger and allow the formation of new creases superior to the original alarcreases in order to detect excess skin to remove. After marking, the resection was performed with a no. 15 blade. The excision was closed using 6-0 Prolene sutures. We aimed to describe a simple technique for making asymmetric resections in which theapplication of pressure by a finger reveals excess skin in both nostril sill and nostril flareindependently for each alar base. With these asymmetric excisions from the right and left alar bases, a more symmetric nostrils and nasal base can be achieved.
Follow me cloud: interworking federated clouds and distributed mobile networks

This article introduces the Follow-Me Cloud concept and proposes its framework. The proposed framework is aimed at smooth migration of all or only a required portion of an ongoing IP service between a data center and user equipment of a 3GPP mobile network to another optimal DC with no service disruption. The service migration and continuity is supported by replacing IP addressing with service identification. Indeed, an FMC service/application is identified, upon establishment, by a session/service ID, dynamically changing along with the service being delivered over the session; it consists of a unique identifier of UE within the 3GPP mobile network, an identifier of the cloud service, and dynamically changing characteristics of the cloud service. Service migration in FMC is triggered by change in the IP address of the UE due to a change of data anchor gateway in the mobile network, in turn due to UE mobility and/or for load balancing. An optimal DC is then selected based on the features of the new data anchor gateway. Smooth service migration and continuity are supported thanks to logic installed at UE and DCs that maps features of IP flows to the session/service ID.
Promotional Chat on the Internet

C rooms, recommendation sites, and customer review sections allow consumers to overcome geographic boundaries and to communicate based on mutual interests. However, marketers also have incentives to supply promotional chat or reviews in order to influence the consumers’ evaluation of their products. Moreover, firms can disguise their promotion as consumer recommendations due to the anonymity afforded by online communities. We explore this new setting where advertising and word of mouth become perfect substitutes because they appear indistinguishable to the consumer. Specifically, we investigate here whether word of mouth remains credible and whether firms choose to devote more resources promoting their inferior or superior products. We develop a game theoretic model in which two products are differentiated in their value to the consumer. Unlike the firms, the consumers are uncertain about the products’ quality. The consumers read messages online that help them decide on the identity of the superior product. We find a unique equilibrium where online word of mouth is persuasive despite the promotional chat activity by competing firms. In this equilibrium, firms spend more resources promoting inferior products, in striking contrast to existing advertising literature. In addition, we discuss consumer welfare implications and how other marketing strategies might interact with promotional chat.
Attention-Aware Generative Adversarial Networks (ATA-GANs)

In this work, we present a novel approach for training Generative Adversarial Networks (GANs). Using the attention maps produced by a Teacher Network we are able to improve the quality of the generated images as well as perform weakly supervised object localization on the generated images. To this end, we generate images of HEp-2 cells captured with Indirect Imunofluoresence (IIF) and study the ability of our network to perform a weakly localization of the cell. First, we demonstrate that whilst GANs can learn the mapping between the input domain and the target distribution efficiently, the discriminator network is not able to detect the regions of interest. Second, we present a novel attention transfer mechanism which allows us to enforce the discriminator to put emphasis on the regions of interest via transfer learning. Third, we show that this leads to more realistic images, as the discriminator learns to put emphasis on the area of interest. Fourth, the proposed method allows one to generate both images as well as attention maps which can be useful for data annotation e.g in object detection.