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Background Liquid biopsy is gaining increasing clinical utility in the management of cancer patients. The main components of a liquid biopsy are circulating nucleic acids, circulating tumour cells and extracellular vesicles such as exosomes. Circulating nucleic acids including cell free DNA (cfDNA) and circulating tumour DNA (ctDNA) in particular have been the focus of recent attention as they have demonstrated excellent potential in cancer screening, provision of prognostic information and in genomic profiling of a tumour without the need for repeated tissue biopsies. The aim of this review was to explore the current evidence in relation to the use of liquid biopsy in the perioperative setting and identify ways in which liquid biopsy may be applied in the future. Methods This narrative review is based on a comprehensive literature search up to the 1st of June 2020 for papers relevant to the application of liquid biopsy in surgical oncology, focusing particularly on the perioperative period. Results Recent evidence has demonstrated that perioperative liquid biopsy can accurately stratify patients’ risk of recurrence compared to conventional biomarkers. Attention to the perioperative dynamics of liquid biopsy components can potentially provide new understanding of the complex relationship between surgery and cancer outcome. In addition, careful evaluation of liquid biopsy components in the perioperative window may provide important diagnostic and therapeutic information for cancer patients. Conclusion The rapidly evolving concept of the liquid biopsy has the potential to become the cornerstone for decision making around surveillance and adjuvant therapies the era of personalised medicine.

Predicting the uncertain and dynamic future of market conditions on the supply chain, as reflected in prices, is an essential component of effective operational decision-making. We present and evaluate methods used by our agent, Deep Maize, to forecast market prices in the trading agent competition supply chain management game (TAC/SCM). We employ a variety of machine learning and representational techniques to exploit as many types of information as possible, integrating well-known methods in novel ways. We evaluate these techniques through controlled experiments as well as performance in both the main TAC/SCM tournament and supplementary Prediction Challenge. Our prediction methods demonstrate strong performance in controlled experiments and achieved the best overall score in the Prediction Challenge.

SPICE is a high resolution imaging spectrometer operating at extreme ultraviolet wavelengths, 70.4 – 79.0 nm and 97.3 - 104.9 nm. It is a facility instrument on the Solar Orbiter mission. SPICE will address the key science goals of Solar Orbiter by providing the quantitative knowledge of the physical state and composition of the plasmas in the solar atmosphere, in particular investigating the source regions of outflows and ejection processes which link the solar surface and corona to the heliosphere. By observing the intensities of selected spectral lines and line profiles, SPICE will derive temperature, density, flow and composition information for the plasmas in the temperature range from 10,000 K to 10MK. The instrument optics consists of a single-mirror telescope (off-axis paraboloid operating at near-normal incidence), feeding an imaging spectrometer. The spectrometer is also using just one optical element, a Toroidal Variable Line Space grating, which images the entrance slit from the telescope focal plane onto a pair of detector arrays, with a magnification of approximately x5. Each detector consists of a photocathode coated microchannel plate image intensifier, coupled to active-pixel-sensor (APS). Particular features of the instrument needed due to proximity to the Sun include: use of dichroic coating on the mirror to transmit and reject the majority of the solar spectrum, particle-deflector to protect the optics from the solar wind, and use of data compression due to telemetry limitations.

The recommended design approach for the 3D Tropospheric Winds mission is a hybrid Doppler lidar which combines the best elements of both a coherent aerosol Doppler lidar operating at 2 microns and a direct detection molecular Doppler lidar operating at 0.355 microns. In support of the mission, we built a novel, compact, light-weighted multi-field of view transceiver where multiple telescopes are used to cover the required four fields of view. A small mechanism sequentially selects both the "transmit" and "receive" fields of view. The four fields are combined to stimulate both the 0.355 micron receiver and the 2 micron receiver. This version is scaled (0.2 m diameter aperture) from the space-based version but still demonstrates the feasibility of the hybrid approach. The primary mirrors were conventionally light-weighted and coated with dielectric, high reflectivity coatings with high laser damage thresholds at both 2 microns and 0.355 microns. The mechanical structure and mounts were fabricated from composites to achieve dimensional stability while significantly reducing the mass. In the laboratory, we demonstrated the system level functionality at 0.355 microns and at 2 microns, raising the Technology Readiness Level (TRL) from 2 to 4.

We model the strategic decisions of web sites in content markets, where sites may reduce user search cost by aggregating content. Example aggregations include political news, technology, and other niche-topic websites. We model this market scenario as an extensive form game of complete information, where sites choose a set of content to aggregate and users associate with sites that are nearest to their interests. Thus, our scenario is a location game in which sites choose to aggregate content at a certain point in user-preference space, and our choice of distance metric, Jacquard distance, induces a lattice structure on the game. We provide two variants of this scenario: one where users associate with the first site to enter amongst sites of equal distances, and a second where users choose uniformly between sites at equal distances. We show that subgame perfect Nash equilibria exist for both games. While it appears to be computationally hard to compute equilibria in both games, we show a polynomial-time satisficing strategy called Frontier Descent for the first game. A satisficing strategy is not a best response, but ensures that earlier sites will have positive profits, assuming all subsequent sites also have positive profits. By contrast, we show that the second game has no satisficing solution.

The holy grail of online advertising is to target users with ads matched to their needs with such precision that the users respond to the ads, thereby increasing both advertisers' and users' value. The current approach to this challenge utilizes information about the users: their gender, their location, the websites they have visited before, and so on. Incorporating this data in ad auctions poses an economic challenge: can this be done in a way that the auctioneer's revenue does not decrease at least on average? This is the problem we study in this paper. Our main result is that in Myerson's optimal mechanism, for a general model of data in auctions, additional data leads to additional expected revenue. In the context of ad auctions we show that for the simple and common mechanisms, namely second price auction with reserve prices, there are instances in which additional data decreases the expected revenue, but this decrease is by at most a small constant factor under a standard regularity assumption.

International audience; The Sample Analysis at Mars (SAM) investigation of the Mars Science Laboratory (MSL) addresses the chemical and isotopic composition of the atmosphere and volatiles extracted from solid samples. The SAM investigation is designed to contribute substantially to the mission goal of quantitatively assessing the habitability of Mars as an essential step in the search for past or present life on Mars. SAM is a 40 kg instrument suite located in the interior of MSL's Curiosity rover. The SAM instruments are a quadrupole mass spectrometer, a tunable laser spectrometer, and a 6-column gas chromatograph all coupled through solid and gas processing systems to provide complementary information on the same samples. The SAM suite is able to measure a suite of light isotopes and to analyze volatiles directly from the atmosphere or thermally released from solid samples. In addition to measurements of simple inorganic compounds and noble gases SAM will conduct a sensitive search for organic compounds with either thermal or chemical extraction from sieved samples delivered by the sample processing system on the Curiosity rover's robotic arm.

In recent years the online advertising industry has witnessed a shift from the more traditional pay-per-impression model to the payper-click and more recently to the pay-per-conversion model. Such models require the ad allocation engine to translate the advertiser's value per click/conversion to value per impression. This is often done through simple models that assume that each impression of the ad stochastically leads to a click/conversion independent of other impressions of the same ad, and therefore any click/conversion can be attributed to the last impression of the ad. However, this assumption is unrealistic, especially in the context of pay-per-conversion advertising, where it is well known in the marketing literature that the consumer often goes through a purchasing funnel before they make a purchase. Decisions to buy are rarely spontaneous, and therefore are not likely to be triggered by just the last ad impression. In this paper, we observe how the current method of attribution leads to inefficiency in the allocation mechanism. We develop a fairly general model to capture how a sequence of impressions can lead to a conversion, and solve the optimal ad allocation problem in this model. We will show that this allocation can be supplemented with a payment scheme to obtain a mechanism that is incentive compatible for the advertiser and fair for the publishers.

We introduce the TAC Ad Auctions game (TAC/AA), a new game for the Trading Agent Competition. The Ad Auctions game investigates complex strategic issues found in real sponsored search auctions that are not captured in current analytical models. We provide an overview of TAC/AA, introducing its key features and design rationale. TAC/AA debuted in summer 2009, with the final tournament commencing in conjunction with the TADA-09 workshop.

The TAC SCM tournament is moving into its fourth year. In an effort to track agent progress, we present a benchmark market efficiency comparison for the tournament, in addition to prior measures of agent competency through customer bidding. Using these benchmarks we find statistically significant increases in intratournament market efficiency, whereas agents are generally decreasing in manufacturer market power. We find that agent market share and bid efficiency have increased while the variance of average sales prices has been significantly reduced. Additionally, we test for a statistical relationship between agent profits and the bullwhip effect.

Future market conditions can be a pivotal factor in making business decisions. We present and evaluate methods used by our agent, Deep Maize, to forecast market prices in the Trading Agent Competition Supply Chain Management Game. As a guiding principle we seek to exploit as many sources of available information as possible to inform predictions. Since information comes in several different forms, we integrate well-known methods in a novel way to make predictions. The core of our predictor is a nearest-neighbors machine learning algorithm that identifies historical instances with similar economic indicators. We augment this with an online learning procedure that transforms the predictions by optimizing a scoring rule. This allows us to select more relevant historical contexts using additional information available during individual games. We also explore the advantages of two different representations for predicting price distributions. One uses absolute prices, and the other uses statistics of price time series to exploit local stability. We evaluate these methods using both data from the 2005 tournament final round and additional simulations. We compare several variations of our predictor to one another and a baseline predictor similar to those used by many other tournament agents. We show substantial improvements over the baseline predictor, and demonstrate that each element of our predictor contributes to improved performance.

The TAC Supply Chain Management (TAC/SCM) game presents a challenging dynamic environment for autonomous decision-making in a salient application domain. Strategic interactions complicate the analysis of games such as TAC/SCM. since the effectiveness of a given strategy depends on the strategies played by other agents on the supply chain. The TAC tournament generates results from one particular path of combinations, and success in the tournament is rightly regarded as evidence for agent quality. Such results along with post-competition controlled experiments provide useful evaluations of novel techniques employed in the game. We argue that a broader game-theoretic analysis framework can provide a firmer foundation for choice of experimental contexts. Exploiting a repository of agents from the 2005 and 2006 TAC/SCM tournaments, we demonstrate an empirical game-theoretic methodology based on extensive simulation and careful measurement. Our analysis of agents from TAC-05 reveals interesting interactions not seen in the tournament. Extending the analysis to TAC-06 enables us to measure progress from year-to-year, and generates a candidate empirical equilibrium among the best known strategies. We use this equilibrium as a stable background population for comparing relative performance of the 2006 agents, yielding insights complementing the tournament results.

This interactive presentation describes LexNet, a graphical environment for graph-based NLP developed at the University of Michigan. LexNet includes LexRank (for text summarization), biased LexRank (for passage retrieval), and TUMBL (for binary classification). All tools in the collection are based on random walks on lexical graphs, that is graphs where different NLP objects (e.g., sentences or phrases) are represented as nodes linked by edges proportional to the lexical similarity between the two nodes. We will demonstrate these tools on a variety of NLP tasks including summarization, question answering, and prepositional phrase attachment.