A Look Back To Look Forward: New Patterns In The Supply/Demand Equation In The Lodging Industry

In his dialogue entitled - A Look Back to Look Forward: New Patterns In The Supply/Demand Equation In The Lodging Industry - by Albert J. Gomes, Senior Principal, Pannell Kerr Forster, Washington, D.C. What the author intends for you to know is the following: “Factors which influence the lodging industry in the United States are changing that industry as far as where hotels are being located, what clientele is being served, and what services are being provided at different facilities. The author charts these changes and makes predictions for the future.” Gomes initially alludes to the evolution of transportation – the human, animal, mechanical progression - and how those changes, in the last 100 years or so, have had a significant impact on the hotel industry. “A look back to look forward treats the past as prologue. American hoteliers are in for some startling changes in their business,” Gomes says. “The man who said that the three most important determinants for the success of a hotel were “location, location, location” did a lot of good only in the short run.” Gomes wants to make you aware of the existence of what he calls, “locational obsolescence.” “Locational obsolescence is a fact of life, and at least in the United States bears a direct correlation to evolutionary changes in transportation technology,” he says. “…the primary business of the hospitality industry is to serve travelers or people who are being transported,” Gomes expands the point. Tied to the transportation element, the author also points out an interesting distinction between hotels and motels. In addressing, “…what clientele is being served, and what services are being provided at different facilities,” Gomes suggests that the transportation factor influences these constituents as well. Also coupled with this discussion are oil prices and shifts in transportation habits, with reference to airline travel being an ever increasing method of travel; capturing much of the inter-city travel market. Gomes refers to airline deregulation as an impetus. The point being, it’s a fluid market rather than a static one, and [successful] hospitality properties need to be cognizant of market dynamics and be able to adjust to the variables in their marketplace. Gomes provides many facts and figures to bolster his assertions. Interestingly and perceptively, at the time of this writing, Gomes alludes to America’s deteriorating road and bridge network. As of right now, in 2009, this is a major issue. Gomes rounds out this study by comparing European hospitality trends to those in the U.S

INTELLIGENT AUTOMATION IN SUPPLY CHAIN OPTIMIZATION

Intelligent automation (IA) is transforming supply chain management by integrating advanced technologies such as artificial intelligence (AI), machine learning (ML), robotics, and the Internet of Things (IoT). This paper explores how IA optimizes supply chain processes, enhances operational efficiency, and drives strategic decision-making. By analyzing the impact of intelligent automation on various supply chain functions, including inventory management, logistics, and demand forecasting, this research highlights the critical role of IA in achieving agility and responsiveness in increasingly competitive markets. Additionally, the paper discusses the challenges organizations face in implementing IA solutions and provides insights into best practices for successful integration. The findings underscore the importance of leveraging intelligent automation as a key driver of supply chain optimization in today's digital landscape

Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning

Continual learning aims to create artificial neural networks capable of accumulating knowledge and skills through incremental training on a sequence of tasks. The main challenge of continual learning is catastrophic interference, wherein new knowledge overrides or interferes with past knowledge, leading to forgetting. An associated issue is the problem of learning "cross-task knowledge," where models fail to acquire and retain knowledge that helps differentiate classes across task boundaries. A common solution to both problems is "replay," where a limited buffer of past instances is utilized to learn cross-task knowledge and mitigate catastrophic interference. However, a notable drawback of these methods is their tendency to overfit the limited replay buffer. In contrast, our proposed solution, SurpriseNet, addresses catastrophic interference by employing a parameter isolation method and learning cross-task knowledge using an auto-encoder inspired by anomaly detection. SurpriseNet is applicable to both structured and unstructured data, as it does not rely on image-specific inductive biases. We have conducted empirical experiments demonstrating the strengths of SurpriseNet on various traditional vision continual-learning benchmarks, as well as on structured data datasets. Source code made available at https://doi.org/10.5281/zenodo.8247906 and https://github.com/tachyonicClock/SurpriseNet-CIKM-2

A hybrid method for quantum dynamics simulation

We propose a hybrid approach to simulate quantum many body dynamics by combining Trotter based quantum algorithm with classical dynamic mode decomposition. The interest often lies in estimating observables rather than explicitly obtaining the wave function's form. Our method predicts observables of a quantum state in the long time by using data from a set of short time measurements from a quantum computer. The upper bound for the global error of our method scales as $O(t^{3/2})$ with a fixed set of the measurement. We apply our method to quench dynamics in Hubbard model and nearest neighbor spin systems and show that the observable properties can be predicted up to a reasonable error by controlling the number of data points obtained from the quantum measurements.Comment: 9 pages, 4 figure

Evaluation of lidocaine and mepivacaine for inferior third molar surgery

Objective: The aim of this study was to compare 2% lidocaine and 2% mepivacaine with 1:100,000 epinephrine for postoperative pain control. Study design: A group of 35 patients, both genders were recruited, whose had ages ranged from 13 to 27 years-old and had two inferior third molars in similar positions to be extracted. The cartridges were distributed to the patients according to a randomised pattern, where lidocaine was in the control group and mepivacaine in the experimental group. Results: Results showed no significant association between the anesthetics and postoperative pain, pulp sensibility after one hour, gender, tooth position and duration of the surgical procedure. Conclusions: It was shown that lidocaine and mepivacaine have similar time of anesthesia, they are adequate for surgical procedures that last one hour, and there was no difference between the two anesthetics in relation to the severety of post-operative pain

A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams

Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare. To address the learning problems associated with such data, one can ignore the unlabelled data and focus only on the labelled data (supervised learning); use the labelled data and attempt to leverage the unlabelled data (semi-supervised learning); or assume some labels will be available on request (active learning). The first approach is the simplest, yet the amount of labelled data available will limit the predictive performance. The second relies on finding and exploiting the underlying characteristics of the data distribution. The third depends on an external agent to provide the required labels in a timely fashion. This survey pays special attention to methods that leverage unlabelled data in a semi-supervised setting. We also discuss the delayed labelling issue, which impacts both fully supervised and semi-supervised methods. We propose a unified problem setting, discuss the learning guarantees and existing methods, explain the differences between related problem settings. Finally, we review the current benchmarking practices and propose adaptations to enhance them

Preferential survival in models of complex ad hoc networks

There has been a rich interplay in recent years between (i) empirical investigations of real world dynamic networks, (ii) analytical modeling of the microscopic mechanisms that drive the emergence of such networks, and (iii) harnessing of these mechanisms to either manipulate existing networks, or engineer new networks for specific tasks. We continue in this vein, and study the deletion phenomenon in the web by following two different sets of web-sites (each comprising more than 150,000 pages) over a one-year period. Empirical data show that there is a significant deletion component in the underlying web networks, but the deletion process is not uniform. This motivates us to introduce a new mechanism of preferential survival (PS), where nodes are removed according to a degree-dependent deletion kernel. We use the mean-field rate equation approach to study a general dynamic model driven by Preferential Attachment (PA), Double PA (DPA), and a tunable PS, where c nodes (c<1) are deleted per node added to the network, and verify our predictions via large-scale simulations. One of our results shows that, unlike in the case of uniform deletion, the PS kernel when coupled with the standard PA mechanism, can lead to heavy-tailed power law networks even in the presence of extreme turnover in the network. Moreover, a weak DPA mechanism, coupled with PS, can help make the network even more heavy-tailed, especially in the limit when deletion and insertion rates are almost equal, and the overall network growth is minimal. The dynamics reported in this work can be used to design and engineer stable ad hoc networks and explain the stability of the power law exponents observed in real-world networks.Comment: 9 pages, 6 figure

Lorentz-CPT violation, radiative corrections and finite temperature

In this work we investigate the radiatively induced Chern-Simons-like terms in four-dimensions at zero and finite temperature. We use the approach of rationalizing the fermion propagator up to the leading order in the CPT-violating coupling $b_\mu$. In this approach, we have shown that although the coefficient of Chern-Simons term can be found unambiguously in different regularization schemes at zero or finite temperature, it remains undetermined. We observe a correspondence among results obtained at finite and zero temperature.Comment: To appear in JHEP, 10 pages, 1 eps figure, minor changes and references adde

Adaptive random forests for evolving data stream classification

Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources