Diagnosing weakly first-order phase transitions by coupling to order parameters

The hunt for exotic quantum phase transitions described by emergent fractionalized degrees of freedom coupled to gauge fields requires a precise determination of the fixed point structure from the field theoretical side, and an extreme sensitivity to weak first-order transitions from the numerical side. Addressing the latter, we revive the classic definition of the order parameter in the limit of a vanishing external field at the transition. We demonstrate that this widely understood, yet so far unused approach provides a diagnostic test for first-order versus continuous behavior that is distinctly more sensitive than current methods. We first apply it to the family of $Q$-state Potts models, where the nature of the transition is continuous for $Q\leq4$ and turns (weakly) first order for $Q>4$, using an infinite system matrix product state implementation. We then employ this new approach to address the unsettled question of deconfined quantum criticality in the $S=1/2$ N\'eel to valence bond solid transition in two dimensions, focusing on the square lattice $J$-$Q$ model. Our quantum Monte Carlo simulations reveal that both order parameters remain finite at the transition, directly confirming a first-order scenario with wide reaching implications in condensed matter and quantum field theory.Comment: Published versio

Asynchronous Silent Programmable Matter Achieves Leader Election and Compaction

We study models and algorithms for Programmable Matter (PM), that is matter with the ability to change its physical properties (e.g., shape or optical properties) in a programmable fashion. PM can be implemented by assembling a system of weak self-organizing computational elements, called particles, that can be programmed via distributed algorithms to collectively achieve some global task. Recent advances in the production of nanotechnologies have rendered such systems increasingly possible in practice, thus triggering research interests from many areas of computer science. The most established models for PM assume that particles: are modeled as finite state automata; are all identical, executing the same algorithm based on local observation of the surroundings; live and operate in the cells of a hexagonal grid; can move from one cell to another by repeatedly alternating between a contracted state (a particle occupies one cell) and an expanded state (a particle occupies two neighboring cells). Given these elementary features, it is rather hard to design distributed algorithms even for basic tasks and, in fact, all existing solutions to solve fundamental problems via PM have resorted to endowing PM systems with various capabilities to overcome such hardness, thus assuming quite unrealistic features. In this paper, we move toward more realistic computational models for PM. Specifically, we first introduce, a new modeling approach that relaxes several assumptions used in previous ones. Second, we present a distributed algorithm to solve, in the model, a foundational primitive for PM, namely Leader Election. This algorithm works in O(n) rounds for all initial configurations of n particles that are both connected (i.e. particles induce a connected graph) and compact (i.e. without holes, that is no empty cells surrounded by particles occur). As usual in asynchronous contexts, a round is intended as the time within which all particles have been activated at least once. Third, we show that, if the initial configuration admits holes, it is impossible to achieve leader election while preserving connectivity. Finally, by slightly empowering the robots, we design an algorithm to handle initial configurations admitting holes that in O(n2) rounds solves the leader election problem while obtaining also compaction

Testosterone decreases adiponectin levels in female to male transsexuals

Aim: To evaluate the effect of testosterone (T) on adiponectin serum levels in transsexual female patients. Methods: We measured adiponectin, leptin, luteinizing hormone and follicle stimulating hormone, T, estradiol, lipid profile, biochemical parameters and body composition in 16 transsexual female patients at baseline and after 6 months of T treatment (100 mg Testoviron Depot /10 days, i.m.). Results: Adiponectin levels were 16.9 ± 7.3 mg/mL at baseline and 13.5 ± 7.4 mg/mL at month 6 of T treatment (P < 0.05). Leptin and high-density lipoprotein cholesterol decreased significantly, whereas body mass index, waist circumference and lean body mass increased significantly after 6 months of T treatment. No changes in insulin or Homeostasis Model Assessment were detected. Conclusion: T can significantly reduce adiponectin serum levels in transsexual female patients

Antenatal automatic diagnosis of cleft lip via unsupervised clustering method relying on 3D facial soft tissue landmarks

Objectives Ultrasound (US) is the first-choice device to detect different types of facial dysmorphisms. Anyway, at present no standard protocol has been defined for automatic nor semi-automatic diagnosis. Even though the practitioner's contribution is core, steps towards automatism are to be undertaken. We propose a methodology for diagnosing cleft lip on 3D US scans. Methods A bounded Depth Minimum Steiner Trees (D-MST) clustering algorithm is proposed for discriminating groups of 3D US faces relying on the presence/absence of a cleft lip. The analysis of 3D facial surfaces via Differential Geometry is adopted to extract landmarks. Thus, the extracted geometrical information is elaborated to feed the unsupervised clustering algorithm and produce the classification. The clustering returns the probability of being affected by the pathology, allowing physicians to focus their attention on risky individuals for further analysis. Results The feasibility is tested upon the available 3D US scans data and then deeply investigated for a large dataset of adult individuals. 3D facial Bosphorus database is chosen for the testing, which seven cleft lip-affected individuals are added to, by artificially creating the defect. The algorithm correctly separates left and right-sided cleft lips, while healthy individuals create a unique cluster; thus, the method shows accurate diagnosis results. Conclusions Even if further testing is to be performed on tailored datasets made exclusively of fetal images, this techniques gives hefty hints for a future tailored algorithm. This method also fosters the investigation of the scientific formalisation of the "normotype", which is the representative face of a class of individuals, collecting all the principal anthropometric facial measurements, in order to recognise a normal or syndromic fetus

Second-line chemotherapy for patients with advanced gastric cancer: who may benefit?

No established second-line chemotherapy is available for patients with advanced gastric cancer failing to respond or progressing to first-line chemotherapy. However, 20–40% of these patients commonly receive second-line chemotherapy. We evaluated the influence of clinico-pathologic factors on the survival of 175 advanced gastric cancer patients, who received second-line chemotherapy at three oncology departments. Univariate and multivariate analyses found five factors which were independently associated with poor overall survival: performance status 2 (hazard ratio (HR), 1.79; 95% CI, 1.16–2.77; P=0.008), haemoglobin ⩽11.5 g l−1 (HR, 1.48; 95% CI, 1.06–2.05; P=0.019), CEA level >50 ng ml−1 (HR, 1.86; 95% CI, 1.21–2.88; P=0.004), the presence of greater than or equal to three metastatic sites of disease (HR, 1.72; 95% CI, 1.16–2.53; P=0.006), and time-to-progression under first-line chemotherapy ⩽6 months (HR, 1.97; 95% CI, 1.39–2.80; P<0.0001). A prognostic index was constructed dividing patients into low- (no risk factor), intermediate- (one to two risk factors), or high- (three to five risk factors) risk groups, and median survival times for each group were 12.7 months, 7.1 months, and 3.3 months, respectively (P<0.001). In the absence of data deriving from randomised trials, this analysis suggests that some easily available clinical factors may help to select patients with advanced gastric cancer who could derive more benefit from second-line chemotherapy

Mucinous histology predicts for poor response rate and overall survival of patients with colorectal cancer and treated with first-line oxaliplatin- and/or irinotecan-based chemotherapy

The objective of this study was to investigate the efficacy of first-line chemotherapy containing irinotecan and/or oxaliplatin in patients with advanced mucinous colorectal cancer. Prognostic factors associated with response rate and survival were identified using univariate and multivariate logistic and/or Cox proportional hazards analyses. The population included 255 patients, of whom 49 (19%) had mucinous and 206 (81%) had non-mucinous colorectal cancer. The overall response rates for mucinous and non-mucinous tumours were 18.4 (95% CI, 7.5–29.2%) and 49% (95% CI, 42.2–55.8%), respectively (P=0.0002). After a median follow-up of 45 months, median overall survival for the mucinous patients was 14.0 months compared with 23.4 months for the non-mucinous group (hazard ratio (HR), 1.74; CI 95%, 1.27–3.31; P=0.0034). After adjustment for significant features by multivariate Cox regression analysis, mucinous histology was associated with poor overall survival (HR, 1.593, 95% CI, 1.05–2.40; P=0.0267), together with performance status ECOG 2, number of metastatic sites ⩾2, and peritoneal metastases. This retrospective analysis shows that patients with mucinous colorectal cancer have poor responsiveness to oxaliplatin/irinotecan-based first-line combination chemotherapy and an unfavourable prognosis compared with non-mucinous colorectal cancer patients

Dynamic Public Transit Labeling

We study the journey planning problem in transit networks which, given the timetable of a schedule-based transit system, asks to answer to queries such as, e.g., “seek a journey that arrives at a given destination as early as possible”. The state-of-the-art solution to such problem, in terms of query time, is Public Transit Labeling (ptl), proposed in [Delling et al., SEA 2015], that consists of three main ingredients: (i) a graph data structure for storing transit networks; (ii) a compact labeling-based representation of the transitive closure of such graph, computed via a time-consuming preprocessing routine; (iii) an efficient query algorithm exploiting both graph and precomputed data to answer quickly to queries of interest at runtime. The major drawback of ptl is not being practical in dynamic scenarios, when the network’s timetable can undergo updates (e.g. delays). In fact, even after a single change, precomputed data become outdated and queries can return incorrect results. Recomputing the labeling-based representation from scratch, after a modification, is not a viable option as it yields unsustainable time overheads. Since transit networks are inherently dynamic, the above represents a major limitation of ptl. In this paper, we overcome such limit by introducing a dynamic algorithm, called d-ptl, able to update the preprocessed data whenever a delay affects the network, without recomputing it from scratch. We demonstrate the effectiveness of d-ptl through a rigorous experimental evaluation showing that its update times are orders of magnitude smaller than the time for recomputing the preprocessed data from scratch

Collision-free allocation of temporally constrained tasks in multi-robot systems

Multi-robot systems (mRs) are a reference solution for many prominent real-world applications, e.g. management of warehouses or exploration of unknown environments. One of the most fundamental computational problems in MRS is that of planning the assignment of tasks to robots when such tasks have deadlines, i.e. constraints on when the execution must take place.The problem, when multiple objective functions of interest need to be optimized, is both NP-Hard and hard to approximate, and few heuristics are known in the literature to handle it. Unfortunately, none of them guarantees that the trajectories used by the robots when moving between tasks' locations are collision-free at planning time. Rather, they implement a reactive behavior, i.e. they abort the execution of a planned task whenever something goes wrong, e.g. trajectories of robots intersect or a deadline is missed due to some obstacle. This approach induces negative effects on the global performance of the system in the form of waste of energy, due to high distances traveled by the fleet members, or in the form of high convergence time to execute tasks. Therefore, planning the assignments of temporally constrained tasks with the guarantee of avoiding collisions can be a desirable feature for multi-robot systems.In this paper, we present CFAT-D (Collision-Free Allocation of Tasks having Deadlines), a new algorithm that can allocate temporally constrained tasks while guaranteeing that used trajectories are collision-free at planning time. We prove CFAT-D to be correct and showcase its effectiveness through an extensive experimental evaluation. Finally, we provide a roadmap toward the practical implementation of the new strategy in real-world environments. (C) 2019 Elsevier B.V. All rights reserved

Creating an Holistic Emergency Alert Management Platform

Extreme natural events require effective emergency procedures to minimize adverse effects on a region’s population and economy. Such procedures typically involve the effort of several different teams of first responders (e.g., fire fighters, public administrations, police departments, utility companies), hence coordination is fundamental to the effectiveness of the response to the emergency that must be supported with adequate infrastructures. Nonetheless, first responders often rely on manual processes, in the life cycle of extreme events, which do not change consistently with the type of shock or affected population. The aim of this paper is to present a technology transfer process to improve both the emergency alert process and the knowledge of disaster-type safety procedures through the implementation of a proposed platform. We also highlight a pilot application on a post-disaster case study—the province of L’Aquila (Abruzzi) in Italy