Physics of a microsystem starting from non-equilibrium quantum statistical mechanics

In this paper we address the problem to give a concrete support to the idea, originally stemming from Niels Bohr, that quantum mechanics must be rooted inside the physics of macroscopic systems. It is shown that, starting from the formalism of the non-equilibrium statistical operator, which is now a consolidated part of quantum statistical mechanics, particular correlations between two isolated systems can be singled out and interpreted as microsystems. In this way also a new framework is established in which questions of decoherence can be naturally addressed.Comment: 14 pages, latex, no figures, contribution to the Proceedings of the XXXIII Symposium on Mathematical Physics (Torun, Poland

Description of isolated macroscopic systems inside quantum mechanics

For an isolated macrosystem classical state parameters $\zeta(t)$ are introduced inside a quantum mechanical treatment. By a suitable mathematical representation of the actual preparation procedure in the time interval $[T,t_0]$ a statistical operator is constructed as a solution of the Liouville von Neumann equation, exhibiting at time $t$ the state parameters $\zeta(t')$, $t_0\leq t' \leq t$, and {\it preparation parameters} related to times $T \leq t'\leq t_0$. Relation with Zubarev's non-equilibrium statistical operator is discussed. A mechanism for memory loss is investigated and time evolution by a semigroup is obtained for a restricted set of relevant observables, slowly varying on a suitable time scale.Comment: 13 pages, latex, romp31 style, no figures, to appear in the Proceedings of the XXXI Symposium on Mathematical Physics (Torun, Poland), to be published in Rep. Math. Phy

Subdynamics through Time Scales and Scattering Maps in Quantum Field Theory

It is argued that the dynamics of an isolated system, due to the concrete procedure by which it is separated from the environment, has a non-Hamiltonian contribution. By a unified quantum field theoretical treatment of typical subdynamics, e.g., hydrodynamics, kinetic theory, master equation for a particle interacting with matter, we look for the structure of this more general dynamics.Comment: 16 pages, latex, no figures, to appear in the Proceedings of the Third International Conference on Quantum Communication & Measurement 1996 (Hakone, Japan

Subdynamics as a mechanism for objective description

The relationship between microsystems and macrosystems is considered in the context of quantum field formulation of statistical mechanics: it is argued that problems on foundations of quantum mechanics can be solved relying on this relationship. This discussion requires some improvement of non-equilibrium statistical mechanics that is briefly presented.Comment: latex, 15 pages. Paper submitted to Proc. Conference "Mysteries, Puzzles And Paradoxes In Quantum Mechanics, Workshop on Entanglement And Decoherence, Palazzo Feltrinelli, Gargnano, Garda Lake, Italy, 20-25 September, 199

Why Customers Value Mass-customized Products: The Importance of Process Effort and Enjoyment

We test our hypotheses on 186 participants designing their own scarves with an MC toolkit. After completing the process, they submitted binding bids for "their" products in Vickrey auctions. We therefore observe real buying behavior, not merely stated intentions. We find that the subjective value of a self-designed product (i.e., one's bid in the course of the auction) is indeed not only impacted by the preference fit the customer expects it to deliver, but also by (1) the process enjoyment the customer reports, (2) the interaction of preference fit and process enjoyment, and (3) the interaction of preference fit and perceived process effort. In addition to its main effect, we interpret preference fit as a moderator of the valuegenerating effect of process evaluation: In cases where the outcome of the process is perceived as positive (high preference fit), the customer also interprets process effort as a positive accomplishment, and this positive affect adds (further) value to the product. It appears that the perception of the self-design process as a good or bad experience is partly constructed on the basis of the outcome of the process. In the opposite case (low preference fit), effort creates a negative affect which further reduces the subjective value of the product. Likewise, process enjoyment is amplified by preference fit, although enjoyment also has a significant main effect, which means that regardless of the outcome, customers attribute higher value to a self-designed product if they enjoy the process. The importance of the self-design process found in this study bears clear relevance for companies which offer or plan to offer MC systems. It is not sufficient to design MC toolkits in such a way that they allow customers to design products according to their preferences. The affect caused by this process is also highly important. Toolkits should therefore stimulate positive affective reactions and at the same time keep negative affect to a minimum. (authors' abstract

Video question answering supported by a multi-task learning objective

Video Question Answering (VideoQA) concerns the realization of models able to analyze a video, and produce a meaningful answer to visual content-related questions. To encode the given question, word embedding techniques are used to compute a representation of the tokens suitable for neural networks. Yet almost all the works in the literature use the same technique, although recent advancements in NLP brought better solutions. This lack of analysis is a major shortcoming. To address it, in this paper we present a twofold contribution about this inquiry and its relation with question encoding. First of all, we integrate four of the most popular word embedding techniques in three recent VideoQA architectures, and investigate how they influence the performance on two public datasets: EgoVQA and PororoQA. Thanks to the learning process, we show that embeddings carry question type-dependent characteristics. Secondly, to leverage this result, we propose a simple yet effective multi-task learning protocol which uses an auxiliary task defined on the question types. By using the proposed learning strategy, significant improvements are observed in most of the combinations of network architecture and embedding under analysis

Learning Video Retrieval Models with Relevance-Aware Online Mining

Due to the amount of videos and related captions uploaded every hour, deep learning-based solutions for cross-modal video retrieval are attracting more and more attention. A typical approach consists in learning a joint text-video embedding space, where the similarity of a video and its associated caption is maximized, whereas a lower similarity is enforced with all the other captions, called negatives. This approach assumes that only the video and caption pairs in the dataset are valid, but different captions - positives - may also describe its visual contents, hence some of them may be wrongly penalized. To address this shortcoming, we propose the Relevance-Aware Negatives and Positives mining (RANP) which, based on the semantics of the negatives, improves their selection while also increasing the similarity of other valid positives. We explore the influence of these techniques on two video-text datasets: EPIC-Kitchens-100 and MSR-VTT. By using the proposed techniques, we achieve considerable improvements in terms of nDCG and mAP, leading to state-of-the-art results, e.g. +5.3% nDCG and +3.0% mAP on EPIC-Kitchens-100. We share code and pretrained models at https://github.com/aranciokov/ranp

A Spatio-Temporal Multi-Scale Binary Descriptor

Binary descriptors are widely used for multi-view matching and robotic navigation. However, their matching performance decreases considerably under severe scale and viewpoint changes in non-planar scenes. To overcome this problem, we propose to encode the varying appearance of selected 3D scene points tracked by a moving camera with compact spatio-temporal descriptors. To this end, we first track interest points and capture their temporal variations at multiple scales. Then, we validate feature tracks through 3D reconstruction and compress the temporal sequence of descriptors by encoding the most frequent and stable binary values. Finally, we determine multi-scale correspondences across views with a matching strategy that handles severe scale differences. The proposed spatio-temporal multi-scale approach is generic and can be used with a variety of binary descriptors. We show the effectiveness of the joint multi-scale extraction and temporal reduction through comparisons of different temporal reduction strategies and the application to several binary descriptors

Spatiotemporal Modeling Encounters 3D Medical Image Analysis: Slice-Shift UNet with Multi-View Fusion

As a fundamental part of computational healthcare, Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) provide volumetric data, making the development of algorithms for 3D image analysis a necessity. Despite being computationally cheap, 2D Convolutional Neural Networks can only extract spatial information. In contrast, 3D CNNs can extract three-dimensional features, but they have higher computational costs and latency, which is a limitation for clinical practice that requires fast and efficient models. Inspired by the field of video action recognition we propose a new 2D-based model dubbed Slice SHift UNet (SSH-UNet) which encodes three-dimensional features at 2D CNN's complexity. More precisely multi-view features are collaboratively learned by performing 2D convolutions along the three orthogonal planes of a volume and imposing a weights-sharing mechanism. The third dimension, which is neglected by the 2D convolution, is reincorporated by shifting a portion of the feature maps along the slices' axis. The effectiveness of our approach is validated in Multi-Modality Abdominal Multi-Organ Segmentation (AMOS) and Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) datasets, showing that SSH-UNet is more efficient while on par in performance with state-of-the-art architectures

LEARNABLE MASKS FOR POSE-GUIDED VIEW SYNTHESIS

Pose-guided human view synthesis uses a target pose to generate the appearance of a new view of a person. The input view and the target pose can be processed separately with UNet architectures that combine the results in a late fusion stage. UNet architectures link their encoder and decoder with skip connections that preserve the location of spatial features by injecting input information in the decoding process. However, direct skip connections may transfer irrelevant information to the decoder. We overcome this limitation with learnable masks for skip connections that encourage the decoder to use only relevant information from the encoder. We show that adding the proposed mask to UNet architectures improves the performance of view synthesis with only a slight increase in inference time