Cyber Security and the Internet of Things : vulnerabilities and Security requirements

The Internet of Things (IoT) enables billions of embedded computing devices to connect to each other. It includes various kinds of devices, e.g., sensors, actuators, RFID tags, or smartphones, which are very different in terms of size, weight, functionality and capabilities. Their success is very noticed and the number of threats and attacks against IoT devices and services are on the increase as well. In IoT, The objects can be discovered, controlled and managed from the Internet. This articulation, which represents a strong point of the IoT, also inherit all the problematic of the security already present in the Internet. The latter rests even with renewed acuteness in this new environment, because of its characteristics special. It is important to analyze how conventional security requirements (CIA, AAA, etc.) as well as those related to respect for privacy can be broken down in this new environment. This paper is an attempt to classify different vulnerabilities, besides analyze and characterize security requirements

Water Purification by Shock Electrodialysis: Deionization, Filtration, Separation, and Disinfection

The development of energy and infrastructure efficient water purification systems are among the most critical engineering challenges facing our society. Water purification is often a multi-step process involving filtration, desalination, and disinfection of a feedstream. Shock electrodialysis (shock ED) is a newly developed technique for water desalination, leveraging the formation of ion concentration polarization (ICP) zones and deionization shock waves in microscale pores near to an ion selective element. While shock ED has been demonstrated as an effective water desalination tool, we here present evidence of other simultaneous functionalities. We show that, unlike electrodialysis, shock ED can thoroughly filter micron-scale particles and aggregates of nanoparticles present in the feedwater. We also demonstrate that shock ED can enable disinfection of feedwaters, as approximately $99\%$ of viable bacteria (here \textit{E. coli}) in the inflow were killed or removed by our prototype. Shock ED also separates positive from negative particles, contrary to claims that ICP acts as a virtual barrier for all charged particles. By combining these functionalities (filtration, separation and disinfection) with deionization, shock ED has the potential to enable more compact and efficient water purification systems

Tumor dormancy: EMT beyond invasion and metastasis.

More than two-thirds of cancer-related deaths are attributable to metastases. In some tumor types metastasis can occur up to 20 years after diagnosis and successful treatment of the primary tumor, a phenomenon termed late recurrence. Metastases arise from disseminated tumor cells (DTCs) that leave the primary tumor early on in tumor development, either as single cells or clusters, adapt to new environments, and reduce or shut down their proliferation entering a state of dormancy for prolonged periods of time. Dormancy has been difficult to track clinically and study experimentally. Recent advances in technology and disease modeling have provided new insights into the molecular mechanisms orchestrating dormancy and the switch to a proliferative state. A new role for epithelial-mesenchymal transition (EMT) in inducing plasticity and maintaining a dormant state in several cancer models has been revealed. In this review, we summarize the major findings linking EMT to dormancy control and highlight the importance of pre-clinical models and tumor/tissue context when designing studies. Understanding of the cellular and molecular mechanisms controlling dormant DTCs is pivotal in developing new therapeutic agents that prevent distant recurrence by maintaining a dormant state

Dynamic Stochastic Matching Under Limited Time

In centralized matching markets such as car-pooling platforms and kidney exchange schemes, new participants constantly enter the market and remain available for potential matches during a limited period of time. To reach an efficient allocation, the “timing” of the matching decisions is a critical aspect of the platform’s operations. There is a fundamental trade-off between increasing market thickness and mitigating the risk that participants abandon the market. Nonetheless, the dynamic properties of matching markets have been mostly overlooked in the algorithmic literature. In this paper, we introduce a general dynamic matching model over edge-weighted graphs, where the agents’ arrivals and abandonments are stochastic and heterogeneous. Our main contribution is to design simple matching algorithms that admit strong worst-case performance guarantees for a broad class of graphs. In contrast, we show that the performance of widely used batching algorithms can be arbitrarily bad on certain graph-theoretic structures motivated by car-pooling services. Our approach involves the development of a host of new techniques, including linear programming benchmarks, value function approximations, and proxies for continuous-time Markov chains, which may be of broader interest. In extensive experiments, we simulate the matching operations of a car-pooling platform using real-world taxi demand data. The newly developed algorithms can significantly improve cost efficiency against batching algorithms

Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Preferences

We introduce a new optimization model, dubbed the display optimization problem, that captures a common aspect of choice behavior, known as the framing bias. In this setting, the objective is to optimize how distinct items (corresponding to products, web links, ads, etc.) are being displayed to a heterogeneous audience, whose choice preferences are influenced by the relative locations of items. Once items are assigned to vertically differentiated locations, customers consider a subset of the items displayed in the most favorable locations, before picking an alternative through Multinomial Logit choice probabilities. The main contribution of this paper is to derive a polynomial-time approximation scheme for the display optimization problem. Our algorithm is based on an approximate dynamic programming formulation that exploits various structural properties to derive a compact state space representation of provably near-optimal item-to-position assignment decisions. As a by-product, our results improve on existing constant-factor approximations for closely-related models, and apply to general distributions over consideration sets. We develop the notion of approximate assortments, that may be of independent interest and applicable in additional revenue management settings. Lastly, we conduct extensive numerical studies to validate the proposed modeling approach and algorithm. Experiments on a public hotel booking data set demonstrate the superior predictive accuracy of our choice model vis-a-vis the Multinomial Logit choice model with location bias, proposed in earlier literature. In synthetic computational experiments, our approximation scheme dominates various benchmarks, including natural heuristics -- greedy methods, local-search, priority rules -- as well as state-of-the-art algorithms developed for closely-related models

The ordered k-median problem: surrogate models and approximation algorithms

In the last two decades, a steady stream of research has been devoted to studying various computational aspects of the ordered k-median problem, which subsumes traditional facility location problems (such as median, center, p-centrum, etc.) through a unified modeling approach. Given a finite metric space, the objective is to locate k facilities in order to minimize the ordered median cost function. In its general form, this function penalizes the coverage distance of each vertex by a multiplicative weight, depending on its ranking (or percentile) in the ordered list of all coverage distances. While antecedent literature has focused on mathematical properties of ordered median functions, integer programming methods, various heuristics, and special cases, this problem was not studied thus far through the lens of approximation algorithms. In particular, even on simple network topologies, such as trees or line graphs, obtaining non-trivial approximation guarantees is an open question. The main contribution of this paper is to devise the first provably-good approximation algorithms for the ordered k-median problem. We develop a novel approach that relies primarily on a surrogate model, where the ordered median function is replaced by a simplified ranking-invariant functional form, via efficient enumeration. Surprisingly, while this surrogate model is Ω(nΩ(1)) -hard to approximate on general metrics, we obtain an O(logn) -approximation for our original problem by employing local search methods on a smooth variant of the surrogate function. In addition, an improved guarantee of 2+ϵ is obtained on tree metrics by optimally solving the surrogate model through dynamic programming. Finally, we show that the latter optimality gap is tight up to an O(ϵ) term

Online Assortment Optimization for Two-sided Matching Platforms

Motivated by online labor markets, we consider the online assortment optimization problem faced by a two-sided matching platform that hosts a set of suppliers waiting to match with a customer. Arriving customers are shown an assortment of suppliers, and may choose to issue a match request to one of them. After spending some time on the platform, each supplier reviews all the match requests she has received and, based on her preferences, she chooses whether to match with a customer or to leave unmatched. We study how platforms should design online assortment algorithms to maximize the expected number of matches in such two-sided settings. We establish that a simple greedy algorithm is 1/2-competitive against an optimal clairvoyant algorithm that knows in advance the full sequence of customers’ arrivals. However, unlike related online assortment problems, no randomized algorithm can achieve a better competitive ratio, even in asymptotic regimes. To advance beyond this general impossibility, we consider structured settings where suppliers’ preferences are described by the Multinomial Logit and Nested Logit choice models. We develop new forms of balancing algorithms, which we call preference-aware, that leverage structural information about suppliers’ choice models to design the associated discount function. In certain settings, these algorithms attain competitive ratios provably larger than the standard “barrier” of 1 − 1/e in the adversarial arrival model. Our results suggest that the shape and timing of suppliers’ choices play critical roles in designing online assortment algorithms for two-sided matching platforms