Influence of Congruity in Store-Attribute Dimensions and Self-Image on Purchase Intentions in Online Stores of Multichannel Retailers

Online stores of multichannel retailers continue to lag pure internet retailers with reference to consumers\u27 shopping intentions and sales. This study develops and tests a framework in which (a) trust and attitude (conceptualized as a second-order construct with hedonic and utilitarian dimensions) influence purchase intentions, (b) congruity between the multichannel retailer\u27s land-based and online stores (conceptualized as a second-order constructs made up of seven dimensions: aesthetic appeal, navigation convenience, transaction convenience, atmosphere, service, price orientation, and security) influences trust in and attitude toward the online store, and (c) congruity between consumers\u27 self-image and perceived image of the online store influences trust in and attitude toward the online store. The findings provide robust support for the framework and have strong implications for theory and practice

Transference And Congruence Effects On Purchase Intentions In Online Stores Of Multi-Channel Retailers: Initial Evidence From The U.S. And South Korea

Drawing from research on retailing, online shopping behavior, and theories of cognitive psychology, we develop and test a framework that investigates purchase intentions in online stores of multi-channel retailers. The framework simultaneously examines the influence of transference of attitude and trust from the multi-channel retailer’s physical to online stores, image congruence between the multi-channel retailer’s physical and online stores, and image congruence between the multi-channel retailer’s online store and a prototypical online store. Further, recognizing that several retailers now operate as multi-channel retailers in different countries, we examine the influence of cultural differences in thought processes (i.e., holistic versus analytic thinking) on shoppers’ evaluation of online stores of multi-channel retailers. Toward this end, we test the framework using data collected from respondents in the U.S. (analytic thinkers) and South Korea (holistic thinkers). We conclude with a discussion of the findings, suggestions for future research, and potential limitations

Can ground truth label propagation from video help semantic segmentation?

For state-of-the-art semantic segmentation task, training convolutional neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling, which is expensive and involves extensive human effort. In this work, we study the possibility of using auxiliary ground truth, so-called \textit{pseudo ground truth} (PGT) to improve the performance. The PGT is obtained by propagating the labels of a GT frame to its subsequent frames in the video using a simple CRF-based, cue integration framework. Our main contribution is to demonstrate the use of noisy PGT along with GT to improve the performance of a CNN. We perform a systematic analysis to find the right kind of PGT that needs to be added along with the GT for training a CNN. In this regard, we explore three aspects of PGT which influence the learning of a CNN: i) the PGT labeling has to be of good quality; ii) the PGT images have to be different compared to the GT images; iii) the PGT has to be trusted differently than GT. We conclude that PGT which is diverse from GT images and has good quality of labeling can indeed help improve the performance of a CNN. Also, when PGT is multiple folds larger than GT, weighing down the trust on PGT helps in improving the accuracy. Finally, We show that using PGT along with GT, the performance of Fully Convolutional Network (FCN) on Camvid data is increased by $2.7\%$ on IoU accuracy. We believe such an approach can be used to train CNNs for semantic video segmentation where sequentially labeled image frames are needed. To this end, we provide recommendations for using PGT strategically for semantic segmentation and hence bypass the need for extensive human efforts in labeling.Comment: To appear at ECCV 2016 Workshop on Video Segmentatio

The consequence of excess configurational entropy on fragility: the case of a polymer/oligomer blend

By taking advantage of the molecular weight dependence of the glass transition of polymers and their ability to form perfectly miscible blends, we propose a way to modify the fragility of a system, from fragile to strong, keeping the same glass properties, i.e. vibrational density of states, mean-square displacement and local structure. Both slow and fast dynamics are investigated by calorimetry and neutron scattering in an athermal polystyrene/oligomer blend, and compared to those of a pure 17-mer polystyrene considered to be a reference, of same Tg. Whereas the blend and the pure 17-mer have the same heat capacity in the glass and in the liquid, their fragilities differ strongly. This difference in fragility is related to an extra configurational entropy created by the mixing process and acting at a scale much larger than the interchain distance, without affecting the fast dynamics and the structure of the glass

Classical and Quantum Chaos in a quantum dot in time-periodic magnetic fields

We investigate the classical and quantum dynamics of an electron confined to a circular quantum dot in the presence of homogeneous $B_{dc}+B_{ac}$ magnetic fields. The classical motion shows a transition to chaotic behavior depending on the ratio $\epsilon=B_{ac}/B_{dc}$ of field magnitudes and the cyclotron frequency ${\tilde\omega_c}$ in units of the drive frequency. We determine a phase boundary between regular and chaotic classical behavior in the $\epsilon$ vs ${\tilde\omega_c}$ plane. In the quantum regime we evaluate the quasi-energy spectrum of the time-evolution operator. We show that the nearest neighbor quasi-energy eigenvalues show a transition from level clustering to level repulsion as one moves from the regular to chaotic regime in the $(\epsilon,{\tilde\omega_c})$ plane. The $\Delta_3$ statistic confirms this transition. In the chaotic regime, the eigenfunction statistics coincides with the Porter-Thomas prediction. Finally, we explicitly establish the phase space correspondence between the classical and quantum solutions via the Husimi phase space distributions of the model. Possible experimentally feasible conditions to see these effects are discussed.Comment: 26 pages and 17 PstScript figures, two large ones can be obtained from the Author

Pauli principle and chaos in a magnetized disk

We present results of a detailed quantum mechanical study of a gas of $N$ noninteracting electrons confined to a circular boundary and subject to homogeneous dc plus ac magnetic fields $(B=B_{dc}+B_{ac}f(t)$, with $f(t+2\pi/\omega_0)=f(t)$). We earlier found a one-particle {\it classical} phase diagram of the (scaled) Larmor frequency $\tilde\omega_c=omega_c/\omega_0$ {\rm vs} $\epsilon=B_{ac}/B_{dc}$ that separates regular from chaotic regimes. We also showed that the quantum spectrum statistics changed from Poisson to Gaussian orthogonal ensembles in the transition from classically integrable to chaotic dynamics. Here we find that, as a function of $N$ and $(\epsilon,\tilde\omega_c)$, there are clear quantum signatures in the magnetic response, when going from the single-particle classically regular to chaotic regimes. In the quasi-integrable regime the magnetization non-monotonically oscillates between diamagnetic and paramagnetic as a function of $N$. We quantitatively understand this behavior from a perturbation theory analysis. In the chaotic regime, however, we find that the magnetization oscillates as a function of $N$ but it is {\it always} diamagnetic. Equivalent results are also presented for the orbital currents. We also find that the time-averaged energy grows like $N^2$ in the quasi-integrable regime but changes to a linear $N$ dependence in the chaotic regime. In contrast, the results with Bose statistics are akin to the single-particle case and thus different from the fermionic case. We also give an estimate of possible experimental parameters were our results may be seen in semiconductor quantum dot billiards.Comment: 22 pages, 7 GIF figures, Phys. Rev. E. (1999

Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy

Intra-operative automatic semantic segmentation of knee joint structures can assist surgeons during knee arthroscopy in terms of situational awareness. However, due to poor imaging conditions (e.g., low texture, overexposure, etc.), automatic semantic segmentation is a challenging scenario, which justifies the scarce literature on this topic. In this paper, we propose a novel self-supervised monocular depth estimation to regularise the training of the semantic segmentation in knee arthroscopy. To further regularise the depth estimation, we propose the use of clean training images captured by the stereo arthroscope of routine objects (presenting none of the poor imaging conditions and with rich texture information) to pre-train the model. We fine-tune such model to produce both the semantic segmentation and self-supervised monocular depth using stereo arthroscopic images taken from inside the knee. Using a data set containing 3868 arthroscopic images captured during cadaveric knee arthroscopy with semantic segmentation annotations, 2000 stereo image pairs of cadaveric knee arthroscopy, and 2150 stereo image pairs of routine objects, we show that our semantic segmentation regularised by self-supervised depth estimation produces a more accurate segmentation than a state-of-the-art semantic segmentation approach modeled exclusively with semantic segmentation annotation.Comment: 10 pages, 6 figure

SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection

Freespace detection is an essential component of visual perception for self-driving cars. The recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly improved semantic driving scene segmentation. Freespace can be hypothesized as a ground plane, on which the points have similar surface normals. Hence, in this paper, we first introduce a novel module, named surface normal estimator (SNE), which can infer surface normal information from dense depth/disparity images with high accuracy and efficiency. Furthermore, we propose a data-fusion CNN architecture, referred to as RoadSeg, which can extract and fuse features from both RGB images and the inferred surface normal information for accurate freespace detection. For research purposes, we publish a large-scale synthetic freespace detection dataset, named Ready-to-Drive (R2D) road dataset, collected under different illumination and weather conditions. The experimental results demonstrate that our proposed SNE module can benefit all the state-of-the-art CNNs for freespace detection, and our SNE-RoadSeg achieves the best overall performance among different datasets.Comment: ECCV 202

Output Compression, MPC, and iO for Turing Machines

In this work, we study the fascinating notion of output-compressing randomized encodings for Turing Machines, in a shared randomness model. In this model, the encoder and decoder have access to a shared random string, and the efficiency requirement is, the size of the encoding must be independent of the running time and output length of the Turing Machine on the given input, while the length of the shared random string is allowed to grow with the length of the output. We show how to construct output- compressing randomized encodings for Turing machines in the shared randomness model, assuming iO for circuits and any assumption in the set {LWE, DDH, Nth Residuosity}. We then show interesting implications of the above result to basic feasibility questions in the areas of secure multiparty computation (MPC) and indistinguishability obfuscation (iO): 1. Compact MPC for Turing Machines in the Random Oracle Model: In the context of MPC, we consider the following basic feasibility question: does there exist a malicious-secure MPC protocol for Turing Machines whose communication complexity is independent of the running time and output length of the Turing Machine when executed on the combined inputs of all parties? We call such a protocol as a compact MPC protocol. Hubacek and Wichs [HW15] showed via an incompressibility argument, that, even for the restricted setting of circuits, it is impossible to construct a malicious secure two party computation protocol in the plain model where the communication complexity is independent of the output length. In this work, we show how to evade this impossibility by compiling any (non-compact) MPC protocol in the plain model to a compact MPC protocol for Turing Machines in the Random Oracle Model, assuming output-compressing randomized encodings in the shared randomness model. 2. Succinct iO for Turing Machines in the Shared Randomness Model: In all existing constructions of iO for Turing Machines, the size of the obfuscated program grows with a bound on the input length. In this work, we show how to construct an iO scheme for Turing Machines in the shared randomness model where the size of the obfuscated program is independent of a bound on the input length, assuming iO for circuits and any assumption in the set {LWE, DDH, Nth Residuosity}

Deep Placental Vessel Segmentation for Fetoscopic Mosaicking

During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser ablates them to regulate blood flow in both fetuses. The procedure is challenging due to the mobility of the environment, poor visibility in amniotic fluid, occasional bleeding, and limitations in the fetoscopic field-of-view and image quality. Ideally, anastomotic placental vessels would be automatically identified, segmented and registered to create expanded vessel maps to guide laser ablation, however, such methods have yet to be clinically adopted. We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos. The obtained vessel probability maps provide sufficient cues for mosaicking alignment by registering consecutive vessel maps using the direct intensity-based technique. Experiments on 6 different in vivo fetoscopic videos demonstrate that the vessel intensity-based registration outperformed image intensity-based registration approaches showing better robustness in qualitative and quantitative comparison. We additionally reduce drift accumulation to negligible even for sequences with up to 400 frames and we incorporate a scheme for quantifying drift error in the absence of the ground-truth. Our paper provides a benchmark for fetoscopy placental vessel segmentation and registration by contributing the first in vivo vessel segmentation and fetoscopic videos dataset.Comment: Accepted at MICCAI 202