nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Shadow Detection and Removal from a Single Image

We present a framework to automatically detect and remove shadows in real world scenes from a single image. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The features are learned at the super-pixel level and along the dominant boundaries in the image. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow masks. Using the detected shadow masks, we propose a Bayesian formulation to accurately extract shadow matte and subsequently remove shadows. The Bayesian formulation is based on a novel model which accurately models the shadow generation process in the umbra and penumbra regions. The model parameters are efficiently estimated using an iterative optimization procedure. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.
Stable models and an alternative logic programming paradigm

In this paper we reexamine the place and role of stable model semantics in logic programming and contrast it with a least Herbrand model approach to Horn programs. We demonstrate that inherent features of stable model semantics naturally lead to a logic programming system that offers an interesting alternative to more traditional logic programming styles of Horn logic programming, stratified logic programming and logic programming with well-founded semantics. The proposed approach is based on the interpretation of program clauses as constraints. In this setting programs do not describe a single intended model, but a family of stable models. These stable models encode solutions to the constraint satisfaction problem described by the program. Our approach imposes restrictions on the syntax of logic programs. In particular, function symbols are eliminated from the language. We argue that the resulting logic programming system is well-attuned to problems in the class NP, has a well-defined domain of applications, and an emerging methodology of programming. We point out that what makes the whole approach viable is recent progress in implementations of algorithms to compute stable models of propositional logic programs.
Low-distortion super-GOhm subthreshold-MOS resistors for CMOS neural amplifiers

A low-distortion super-GOhm subthreshold MOS resistor is designed, fabricated and experimentally validated. The circuit is utilized as a feedback element in the body of a two-stage neural recording amplifier. Linearity is experimentally validated for 0.5 Hz to 5 kHz input frequency and over 0.3 to 0.9 V output voltage dynamic range. The implemented pseudo resistor is also tunable, making the high-pass filter pole adjustable. The circuit is fabricated in 0.13-μm CMOS process and consumes 96 nW from a 1.2 V supply to realize an over 500 GΩ resistance.
Resistive Switching by Voltage-Driven Ion Migration in Bipolar RRAM—Part II: Modeling

Resistive-switching memory (RRAM) based on transition metal oxides is a potential candidate for replacing Flash and dynamic random access memory in future generation nodes. Although very promising from the standpoints of scalability and technology, RRAM still has severe drawbacks in terms of understanding and modeling of the resistive-switching mechanism. This paper addresses the modeling of resistive switching in bipolar metal-oxide RRAMs. Reset and set processes are described in terms of voltage-driven ion migration within a conductive filament generated by electroforming. Ion migration is modeled by drift–diffusion equations with Arrhenius-activated diffusivity and mobility. The local temperature and field are derived from the self-consistent solution of carrier and heat conduction equations in a 3-D axis-symmetric geometry. The model accounts for set–reset characteristics, correctly describing the abrupt set and gradual reset transitions and allowing scaling projections for metal-oxide RRAM.
Buying or browsing ? An exploration of shopping orientations and online purchase intention

Consumer selection of retail patronage mode has been widely researched by marketing scholars. Several researchers have segmented consumers by shopping orientation. However, few have applied such methods to the Internet shopper. Despite the widespread belief that Internet shoppers are primarily motivated by convenience, the authors show empirically that consumers’ fundamental shopping orientations have no signi®cant impact on their proclivity to purchase products online. Factors that are more likely to in ̄uence purchase intention include product type, prior purchase, and, to a lesser extent, gender. Literature examining electronic commerce tends either to discuss the size and potential of the phenomenon or to indicate problems associated with it. For example, Forrester Research recently reported that worldwide Internet commerce ± both business to business (B2B) and both business to customer (B2C) ± would reach $6.8 trillion in 2004. At the same time, reports of business failures are increasing, as it is evident that the corporate sector is not satis®ed with Internet performance (Wolff, 1998). Despite these two apparently contradictory positions, many observers note an absence of research into consumer motivation to purchase via the Internet and other aspects of consumer behaviour with regard to the medium (Donthu and Garcia, 1999; Hagel and Armstrong, 1997; Korgaonkar and Wolin, 1999). Literature falls into two categories: usage ± by which we mean rate, purpose or quantity bought ± and advertising response (McDonald, 1993). Common to these two streams is the a ̄owo research of Hoffman and Novak (1996) which suggests that the Internet is a very different medium requiring new means of segmentation. The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at http://www .emeraldinsight .com/researchregister http:// www.emeraldinsigh t.com/0309-056 6.htm Research funded by Grif®th University School of Marketing.
Reinforcement learning from simultaneous human and MDP reward

As computational agents are increasingly used beyond research labs, their success will depend on their ability to learn new skills and adapt to their dynamic, complex environments. If human users—without programming skills— can transfer their task knowledge to agents, learning can accelerate dramatically, reducing costly trials. The tamer framework guides the design of agents whose behavior can be shaped through signals of approval and disapproval, a natural form of human feedback. More recently, tamer+rl was introduced to enable human feedback to augment a traditional reinforcement learning (RL) agent that learns from a Markov decision process’s (MDP) reward signal. We address limitations of prior work on tamer and tamer+rl, contributing in two critical directions. First, the four successful techniques for combining human reward with RL from prior tamer+rl work are tested on a second task, and these techniques’ sensitivities to parameter changes are analyzed. Together, these examinations yield more general and prescriptive conclusions to guide others who wish to incorporate human knowledge into an RL algorithm. Second, tamer+rl has thus far been limited to a sequential setting, in which training occurs before learning from MDP reward. In this paper, we introduce a novel algorithm that shares the same spirit as tamer+rl but learns simultaneously from both reward sources, enabling the human feedback to come at any time during the reinforcement learning process. We call this algorithm simultaneous tamer+rl. To enable simultaneous learning, we introduce a new technique that appropriately determines the magnitude of the human model’s influence on the RL algorithm throughout time and state-action space.
Reliability Fusion of Time-of-Flight Depth and Stereo Geometry for High Quality Depth Maps

Time-of-flight range sensors have error characteristics, which are complementary to passive stereo. They provide real-time depth estimates in conditions where passive stereo does not work well, such as on white walls. In contrast, these sensors are noisy and often perform poorly on the textured scenes where stereo excels. We explore their complementary characteristics and introduce a method for combining the results from both methods that achieve better accuracy than either alone. In our fusion framework, the depth probability distribution functions from each of these sensor modalities are formulated and optimized. Robust and adaptive fusion is built on a pixel-wise reliability weighting function calculated for each method. In addition, since time-of-flight devices have primarily been used as individual sensors, they are typically poorly calibrated. We introduce a method that substantially improves upon the manufacturer's calibration. We demonstrate that our proposed techniques lead to improved accuracy and robustness on an extensive set of experimental results.
A Fast Parallel Maximum Clique Algorithm for Large Sparse Graphs and Temporal Strong Components

We propose a fast, parallel, maximum clique algorithm for large, sparse graphs that is designed to exploit characteristics of social and information networks. We observe roughly linear runtime scaling over graphs between 1000 vertices and 100M vertices. In a test with a 1.8 billion-edge social network, the algorithm finds the largest clique in about 20 minutes. For social networks, in particular, we found that using the core number of a vertex in combination with a good heuristic clique finder efficiently removes the vast majority of the search space. In addition, we parallelize the exploration of the search tree. In the algorithm, processes immediately communicate changes to upper and lower bounds on the size of maximum clique, which occasionally results in a super-linear speedup because vertices with especially large search spaces can be pruned by other processes. We use this clique finder to investigate the size of the largest temporal strong components in dynamic networks, which requires finding the largest clique in a particular temporal reachability graph.
Optimized network traffic engineering using segment routing

Segment Routing is a proposed IETF protocol to improve traffic engineering and online route selection in IP networks. The key idea in segment routing is to break up the routing path into segments in order to enable better network utilization. Segment routing also enables finer control of the routing paths and can be used to route traffic through middle boxes. This paper considers the problem of determining the optimal parameters for segment routing in the offline and online cases. We develop a traffic matrix oblivious algorithm for robust segment routing in the offline case and a competitive algorithm for online segment routing. We also show that both these algorithms work well in practice.
A 3-RSR Haptic Wearable Device for Rendering Fingertip Contact Forces

A novel wearable haptic device for modulating contact forces at the fingertip is presented. Rendering of forces by skin deformation in three degrees of freedom (DoF), with contact—no contact capabilities, was implemented through rigid parallel kinematics. The novel asymmetrical three revolute-spherical-revolute (3-RSR) configuration allowed compact dimensions with minimum encumbrance of the hand workspace. The device was designed to render constant to low frequency deformation of the fingerpad in three DoF, combining light weight with relatively high output forces. A differential method for solving the non-trivial inverse kinematics is proposed and implemented in real time for controlling the device. The first experimental activity evaluated discrimination of different fingerpad stretch directions in a group of five subjects. The second experiment, enrolling 19 subjects, evaluated cutaneous feedback provided in a virtual pick-and-place manipulation task. Stiffness of the fingerpad plus device was measured and used to calibrate the physics of the virtual environment. The third experiment with 10 subjects evaluated interaction forces in a virtual lift-and-hold task. Although with different performance in the two manipulation experiments, overall results show that participants better controlled interaction forces when the cutaneous feedback was active, with significant differences between the visual and visuo-haptic experimental conditions.
Real-Time Bidding by Reinforcement Learning in Display Advertising

The majority of online display ads are served through real-time bidding (RTB) --- each ad display impression is auctioned off in real-time when it is just being generated from a user visit. To place an ad automatically and optimally, it is critical for advertisers to devise a learning algorithm to cleverly bid an ad impression in real-time. Most previous works consider the bid decision as a static optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeatedly happen during its life span before the budget runs out. As such, each bid is strategically correlated by the constrained budget and the overall effectiveness of the campaign (e.g., the rewards from generated clicks), which is only observed after the campaign has completed. Thus, it is of great interest to devise an optimal bidding strategy sequentially so that the campaign budget can be dynamically allocated across all the available impressions on the basis of both the immediate and future rewards. In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set. By modeling the state transition via auction competition, we build a Markov Decision Process framework for learning the optimal bidding policy to optimize the advertising performance in the dynamic real-time bidding environment. Furthermore, the scalability problem from the large real-world auction volume and campaign budget is well handled by state value approximation using neural networks. The empirical study on two large-scale real-world datasets and the live A/B testing on a commercial platform have demonstrated the superior performance and high efficiency compared to state-of-the-art methods.
Reliability of eWLB (Embedded Wafer Level BGA) for Automotive Radar Applications

With shrinking of chip sizes, Wafer Level Chip Scale Packaging (WLCSP) becomes an attractive and holistic packaging solutions with various advantages in comparison to conventional packages, such as Ball Grid Array (BGA) with flipchip or wirebonding. With the advancement of various fan-out (FO) WLPs, it has been proven to be a more optimal, low cost, integrated and reliable solution compared to fan-in WLP because of the greater design flexibility in having more input/output (I/O), improved mechanical and thermal performance. In addition, FO-WLP shows superior high-frequency electrical performance due to its shorter, finer and simpler interconnection scheme compared to flipchip packaging. eWLB (embedded wafer level BGA) is a type of FO-WLP that enables applications requiring smaller form factor, excellent thermal performance and thin package profiles and it has the potential to evolve into various configurations with proven yields and manufacturing experience based on more than 8 years of high volume manufacturing of over 1.5B unit shipment. This paper discusses the recent advancements in robust board level reliability performance of eWLB for automotive application, where a review of a Design of Experiment (DOE) study will demonstrate improved Temperature Cycle on Board (TCoB) reliability. Several DOE studies were planned and test vehicles were prepared with various variables, such as materials, redistribution layer (RDL) design, copper (Cu) and under bump metallurgy (UBM) thickness, and a printed circuit board (PCB) pad design. The final test vehicle passed over 1000 times temperature cycles (TC) with optimized design factors of these parametric studies and reliability tests. To investigate potential structural defects after the reliability test, both destructive analyses were performed under industry standard test conditions, Daisy chain test vehicles were used for TCoB reliability performance.
Classification of noun-noun compound semantics in Dutch and Afrikaans

This article presents initial results on a supervised machine learning approach to determine the semantics of noun compounds in Dutch and Afrikaans. After a discussion of previous research on the topic, we present our annotation methods used to provide a training set of compounds with the appropriate semantic class. The support vector machine method used for this classification experiment utilizes a distributional lexical semantics representation of the compound’s constituents to make its classification decision. The collection of words that occur in the near context of the constituent are considered an implicit representation of the semantics of this constituent. Fscores were reached of 47.8% for Dutch and 51.1% for Afrikaans. compound semantics; Afrikaans; Dutch; machine learning; distributional methods
Stepped Circular Waveguide Dual-Mode Filters for Broadband Contiguous Multiplexers

A stepped circular waveguide dual-mode (SCWDM) filter is fully investigated in this paper, from its basic characteristic to design formula. As compared to a conventional circular waveguide dual-mode (CWDM) filter, it provides more freedoms for shifting and suppressing the spurious modes in a wide frequency band. This useful attribute can be used for a broadband waveguide contiguous output multiplexer (OMUX) in satellite payloads. The scaling factor for relating coupling value M to its corresponding impedance inverter K in a stepped cavity is derived for full-wave EM design. To validate the design technique, four design examples are presented. One challenging example is a wideband 17-channel Ku-band contiguous multiplexer with two SCWDM channel filters. A triplexer hardware covering the same included bandwidth is also designed and measured. The measurement results show excellent agreement with those of the theoretical EM designs, justifying the effectiveness of full-wave EM modal analysis. Comparing to the best possible design of conventional CWDM filters, at least 30% more spurious-free range in both Ku-band and C-band can be achieved by using SCWDM filters.
Language style matching in writing: synchrony in essays, correspondence, and poetry.

Each relationship has its own personality. Almost immediately after a social interaction begins, verbal and nonverbal behaviors become synchronized. Even in asocial contexts, individuals tend to produce utterances that match the grammatical structure of sentences they have recently heard or read. Three projects explore language style matching (LSM) in everyday writing tasks and professional writing. LSM is the relative use of 9 function word categories (e.g., articles, personal pronouns) between any 2 texts. In the first project, 2 samples totaling 1,744 college students answered 4 essay questions written in very different styles. Students automatically matched the language style of the target questions. Overall, the LSM metric was internally consistent and reliable across writing tasks. Women, participants of higher socioeconomic status, and students who earned higher test grades matched with targets more than others did. In the second project, 74 participants completed cliffhanger excerpts from popular fiction. Judges' ratings of excerpt-response similarity were related to content matching but not function word matching, as indexed by LSM. Further, participants were not able to intentionally increase style or content matching. In the final project, an archival study tracked the professional writing and personal correspondence of 3 pairs of famous writers across their relationships. Language matching in poetry and letters reflected fluctuations in the relationships of 3 couples: Sigmund Freud and Carl Jung, Elizabeth Barrett and Robert Browning, and Sylvia Plath and Ted Hughes. Implications for using LSM as an implicit marker of social engagement and influence are discussed.
Pose-Invariant Face Recognition Using Markov Random Fields

One of the key challenges for current face recognition techniques is how to handle pose variations between the probe and gallery face images. In this paper, we present a method for reconstructing the virtual frontal view from a given nonfrontal face image using Markov random fields (MRFs) and an efficient variant of the belief propagation algorithm. In the proposed approach, the input face image is divided into a grid of overlapping patches, and a globally optimal set of local warps is estimated to synthesize the patches at the frontal view. A set of possible warps for each patch is obtained by aligning it with images from a training database of frontal faces. The alignments are performed efficiently in the Fourier domain using an extension of the Lucas-Kanade algorithm that can handle illumination variations. The problem of finding the optimal warps is then formulated as a discrete labeling problem using an MRF. The reconstructed frontal face image can then be used with any face recognition technique. The two main advantages of our method are that it does not require manually selected facial landmarks or head pose estimation. In order to improve the performance of our pose normalization method in face recognition, we also present an algorithm for classifying whether a given face image is at a frontal or nonfrontal pose. Experimental results on different datasets are presented to demonstrate the effectiveness of the proposed approach.
Power Control and Management in a Hybrid AC/DC Microgrid

Hybrid AC/DC microgrids have been planned for the better interconnection of different distributed generation systems (DG) to the power grid, and exploiting the prominent features of both ac and dc microgrids. Connecting these microgrids requires an interlinking AC/DC converter (IC) with a proper power management and control strategy. During the islanding operation of the hybrid AC/DC microgrid, the IC is intended to take the role of supplier to one microgrid and at the same time acts as a load to the other microgrid and the power management system should be able to share the power demand between the existing AC and dc sources in both microgrids. This paper considers the power flow control and management issues amongst multiple sources dispersed throughout both ac and dc microgrids. The paper proposes a decentralized power sharing method in order to eliminate the need for any communication between DGs or microgrids. This hybrid microgrid architecture allows different ac or dc loads and sources to be flexibly located in order to decrease the required power conversions stages and hence the system cost and efficiency. The performance of the proposed power control strategy is validated for different operating conditions, using simulation studies in the PSCAD/EMTDC software environment.