China Market Entry Strategy Of Paris Baguette

This case study analyzes the global strategy of Paris Baguette, a leading bakery franchise in Korea. Because of stricter regulations in the local market, Paris Baguette has encouraged franchises to target overseas markets. The company made first inroads into the Chinese market in 2004 with a bakery cafe in Shanghai. The main point of Paris Baguette’s global strategy is summarized by high quality, style, diversification, and localization. Also, Paris Baguette directly operates its flagship store from headquarters, due to the poor legal environment in China. In this study, we analyze strategies of China market and suggest considerations for future business expansion

When Do Organizations Behave Opportunistically? The Effects Of Collectivistic Organizational Culture

Transaction cost theory posits that firms behave opportunistically if they are given the chance. In reality, however, some firms act opportunistically, whereas others do not. This raises the question of under what circumstances firms tend to behave opportunistically. Previous studies provided no clear explanation of when opportunism occurs and what its antecedents are. This study identifies the circumstances under which firms behave opportunistically by empirically testing the following two causal factors in opportunistic behaviors: transaction-specific investment (TSI) and collectivistic organizational culture. The survey results indicate that a firm’s TSI is an important factor influencing opportunistic behaviors and that collectivism moderates the relationship between TSI and opportunism

Knowledge Unlearning for Mitigating Privacy Risks in Language Models

Pretrained Language Models (LMs) memorize a vast amount of knowledge during initial pretraining, including information that may violate the privacy of personal lives and identities. Previous work addressing privacy issues for language models has mostly focused on data preprocessing and differential privacy methods, both requiring re-training the underlying LM. We propose knowledge unlearning as an alternative method to reduce privacy risks for LMs post hoc. We show that simply applying the unlikelihood training objective to target token sequences is effective at forgetting them with little to no degradation of general language modeling performances; it sometimes even substantially improves the underlying LM with just a few iterations. We also find that sequential unlearning is better than trying to unlearn all the data at once and that unlearning is highly dependent on which kind of data (domain) is forgotten. By showing comparisons with a previous data preprocessing method known to mitigate privacy risks for LMs, we show that unlearning can give a stronger empirical privacy guarantee in scenarios where the data vulnerable to extraction attacks are known a priori while being orders of magnitude more computationally efficient. We release the code and dataset needed to replicate our results at https://github.com/joeljang/knowledge-unlearning

One-ninth magnetization plateau stabilized by spin entanglement in a kagome antiferromagnet

The spin-1/2 antiferromagnetic Heisenberg model on a Kagome lattice is geometrically frustrated, which is expected to promote the formation of many-body quantum entangled states. The most sought-after among these is the quantum spin liquid phase, but magnetic analogs of liquid, solid, and supersolid phases may also occur, producing fractional plateaus in the magnetization. Here, we investigate the experimental realization of these predicted phases in the Kagome material YCu3(OD)6+xBr3-x (x=0.5). By combining thermodynamic and Raman spectroscopic techniques, we provide evidence for fractionalized spinon excitations and observe the emergence of a 1/9 magnetization plateau. These observations establish YCu3(OD)6+xBr3-x as a model material for exploring the 1/9 plateau phase.Comment: to appear in Nature Physics, 33 pagses, 15 figure

Intracellular Water Exchange for Measuring the Dry Mass, Water Mass and Changes in Chemical Composition of Living Cells

We present a method for direct non-optical quantification of dry mass, dry density and water mass of single living cells in suspension. Dry mass and dry density are obtained simultaneously by measuring a cell’s buoyant mass sequentially in an H[subscript 2]O-based fluid and a D[subscript 2]O-based fluid. Rapid exchange of intracellular H[subscript 2]O for D[subscript 2]O renders the cell’s water content neutrally buoyant in both measurements, and thus the paired measurements yield the mass and density of the cell’s dry material alone. Utilizing this same property of rapid water exchange, we also demonstrate the quantification of intracellular water mass. In a population of E. coli, we paired these measurements to estimate the percent dry weight by mass and volume. We then focused on cellular dry density – the average density of all cellular biomolecules, weighted by their relative abundances. Given that densities vary across biomolecule types (RNA, DNA, protein), we investigated whether we could detect changes in biomolecular composition in bacteria, fungi, and mammalian cells. In E. coli, and S. cerevisiae, dry density increases from stationary to exponential phase, consistent with previously known increases in the RNA/protein ratio from up-regulated ribosome production. For mammalian cells, changes in growth conditions cause substantial shifts in dry density, suggesting concurrent changes in the protein, nucleic acid and lipid content of the cell.National Cancer Institute (U.S.). Physical Sciences-Oncology Center (U54CA143874)National Institutes of Health (U.S.) (Center for Cell Division Process Grant P50GM6876)National Institutes of Health (U.S.) (Contract R01CA170592)United States. Army Research Office (Institute for Collaborate Biotechnologies Contract W911NF-09-D-0001

A Scheme to Smooth Aggregated Traffic from Sensors with Periodic Reports

The possibility of smoothing aggregated traffic from sensors with varying reporting periods and frame sizes to be carried on an access link is investigated. A straightforward optimization would take O(pn) time, whereas our heuristic scheme takes O(np) time where n, p denote the number of sensors and size of periods, respectively. Our heuristic scheme performs local optimization sensor by sensor, starting with the smallest to largest periods. This is based on an observation that sensors with large offsets have more choices in offsets to avoid traffic peaks than the sensors with smaller periods. A MATLAB simulation shows that our scheme excels the known scheme by M. Grenier et al. in a similar situation (aggregating periodic traffic in a controller area network) for almost all possible permutations. The performance of our scheme is very close to the straightforward optimization, which compares all possible permutations. We expect that our scheme would greatly contribute in smoothing the traffic from an ever-increasing number of IoT sensors to the gateway, reducing the burden on the access link to the Internet