QAL-BP: An Augmented Lagrangian Quantum Approach for Bin Packing Problem

The bin packing is a well-known NP-Hard problem in the domain of artificial intelligence, posing significant challenges in finding efficient solutions. Conversely, recent advancements in quantum technologies have shown promising potential for achieving substantial computational speedup, particularly in certain problem classes, such as combinatorial optimization. In this study, we introduce QAL-BP, a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation designed specifically for bin packing and suitable for quantum computation. QAL-BP utilizes the augmented Lagrangian method to incorporate the bin packing constraints into the objective function while also facilitating an analytical estimation of heuristic, but empirically robust, penalty multipliers. This approach leads to a more versatile and generalizable model that eliminates the need for empirically calculating instance-dependent Lagrangian coefficients, a requirement commonly encountered in alternative QUBO formulations for similar problems. To assess the effectiveness of our proposed approach, we conduct experiments on a set of bin-packing instances using a real Quantum Annealing device. Additionally, we compare the results with those obtained from two different classical solvers, namely simulated annealing and Gurobi. The experimental findings not only confirm the correctness of the proposed formulation but also demonstrate the potential of quantum computation in effectively solving the bin-packing problem, particularly as more reliable quantum technology becomes available.Comment: 14 pages, 4 figures, 1 tabl

New patents on topical anesthetics.

Anesthesia is defined as a total or partial loss of sensation and it may be general, local or topical, depending on the method of drug administration and area of the body affected. General anesthesia is a reversible state of unconsciousness produced by anesthetic agents, characterized by amnesia, muscle relaxation and loss of sensitivity to pain of the whole body. General anesthetic drugs can be classified into two main groups according to their predominant molecular pharmacological effects: volatile and intravenous agents. Local anesthesia produce a reversible loss of sensation in a portion of the body and it reversibly block impulse conduction along nerve axons and other excitable membrane. All local anesthetics (LA) are membrane stabilizing drugs; they reversibly decrease the rate of depolarization and repolarization of excitable membranes. They act mainly by inhibiting sodium influx through sodium-specific ion channels in the neuronal cell membrane, in particular the voltage-gated sodium channels. When the influx of sodium is interrupted, an action potential cannot arise and signal conduction is inhibited. The main local anesthetic (LA) agents for skin anesthesia are benzocaine (aminoester), prilocaine and lidocaine (aminoamides) which are commercially available as gels, ointments and creams (benzocaine and eutectic mixture of lidocaine and prilocaine) or as a bioadhesive (lidocaine) with different compositions (vehicles and excipients) for adults or pediatric use. Topical anesthetics decrease anxiety, pain and discomfort during cutaneous procedures and provide effective analgesia with rapid onset, prolonged duration and minimal side effects. This article outlines the different classes of topical anesthetics available and gives an overview of the mechanism of action, metabolism of each different class, of the possible complications that can occur because of their use and their possible treatment options and new patents. © 2014 Bentham Science Publishers

Enabling Non-Linear Quantum Operations through Variational Quantum Splines

The postulates of quantum mechanics impose only unitary transformations on quantum states, which is a severe limitation for quantum machine learning algorithms. Quantum Splines (QSplines) have recently been proposed to approximate quantum activation functions to introduce non-linearity in quantum algorithms. However, QSplines make use of the HHL as a subroutine and require a fault-tolerant quantum computer to be correctly implemented. This work proposes the Generalised QSplines (GQSplines), a novel method for approximating non-linear quantum activation functions using hybrid quantum-classical computation. The GQSplines overcome the highly demanding requirements of the original QSplines in terms of quantum hardware and can be implemented using near-term quantum computers. Furthermore, the proposed method relies on a flexible problem representation for non-linear approximation and it is suitable to be embedded in existing quantum neural network architectures. In addition, we provide a practical implementation of GQSplines using Pennylane and show that our model outperforms the original QSplines in terms of quality of fitting

A Novel Framework for Quantum Machine Learning

Quantum computation is an emerging computing paradigm with the potential to revolutionise the world of information technology. It leverages the laws of quantum mechanics to endow quantum machines with tremendous computing power, thus enabling the solution of problems impossible to address with classical devices. For this reason, the field is attracting ever-increasing attention from both academic and private sectors, and its full potential is still to be understood. This dissertation investigates how classical machine learning can benefit from quantum computing and provides several contributions to the emerging field of Quantum Machine Learning. The idea is to provide a universal and efficient framework that can reproduce the output of a plethora of classical machine learning algorithms exploiting quantum computation’s advantages. The proposed framework is named Multiple Aggregator Quantum Algorithm (MAQA) due to its capability to combine multiple functions to solve typical supervised learning tasks. Thanks to this property, in its general formulation MAQA can be potentially adopted as the quantum counterpart of all those models falling into the scheme of aggregation of multiple functions. The theoretical design of the quantum algorithm and the corresponding circuit’s implementation are presented. As a second meaningful addition, two practical applications are illustrated: the quantum version of ensemble methods and neural networks. The final contribution addresses the restriction to linear operations imposed by quantum mechanics. The idea is to exploit a quantum transposition of classical Splines to approximate non-linear functions, thus overcoming this limitation and introducing significant advantages in terms of computational complexity theory

A Networking Framework for Multi-Robot Coordination

Autonomous robots operating in real environments need to be able to interact with a dynamic world populated with objects, people, and, in general, other agents. The current generation of autonomous robots, such as the ASIMO robot by Honda or the QRIO by Sony, has showed impressive performances in mechanics and control of movements; moreover, recent literature reports encouraging results about the capability of such robots of representing themselves with respect to a dynamic external world, of planning future actions and of evaluating resulting situations in order to make new plans. However, when multiple robots are supposed to operate together, coordination and communication issues arise; while noteworthy results have been achieved with respect to the control of a single robot, novel issues arise when the actions of a robot influence another''s behavior. The increase in computational power available to systems nowadays makes it feasible, and even convenient, to organize them into a single distributed computing environment in order to exploit the synergy among different entities. This is especially true for robot teams, where cooperation is supposed to be the most natural scheme of operation, especially when robots are required to operate in highly constrained scenarios, such as inhospitable sites, remote sites, or indoor environments where strict constraints on intrusiveness must be respected. In this case, computations will be inherently network-centric, and to solve the need for communication inside robot collectives, an efficient network infrastructure must be put into place; once a proper communication channel is established, multiple robots may benefit from the interaction with each other in order to achieve a common goal. The framework presented in this paper adopts a composite networking architecture, in which a hybrid wireless network, composed by commonly available WiFi devices, and the more recently developed wireless sensor networks, operates as a whole in order both to provide a communication backbone for the robots and to extract useful information from the environment. The ad-hoc WiFi backbone allows robots to exchange coordination information among themselves, while also carrying data measurements collected from surrounding environment, and useful for localization or mere data gathering purposes. The proposed framework is called RoboNet, and extends a previously developed robotic tour guide application (Chella et al., 2007) in the context of a multi-robot application; our system allows a team of robots to enhance their perceptive capabilities through coordination obtained via a hybrid communication network; moreover, the same infrastructure allows robots to exchange information so as to coordinate their actions in order to achieve a global common goal. The working scenario considered in this paper consists of a museum setting, where guided tours are to be automatically managed. The museum is arranged both chronologically and topographically, but the sequence of findings to be visited can be rearranged depending on user queries, making a sort of dynamic virtual labyrinth with various itineraries. Therefore, the robots are able to guide visitors both in prearranged tours and in interactive tours, built in itinere depending on the interaction with the visitor: robots are able to rebuild the virtual connection between findings and, consequently, the path to be followed. This paper is organized as follows. Section 2 contains some background on multi-robot coordination, and Section 3 describes the underlying ideas and the motivation behind the proposed architecture, whose details are presented in Sections 4, 5, and 6. A realistic application scenario is described in Section 7, and finally our conclusions are drawn in Section 8

Toxic metals in Loggerhead sea turtles (Caretta caretta) stranded freshly dead along Sicilian coasts

Abstract Background: The Loggerhead sea turtle (Caretta caretta) is a marine reptile belonging to a monophyletic group of chelonians. As these animals are long-lived, they have the ability to accumulate pollutants. Aim: To collect epidemiological data on toxic metals in marine Loggerhead sea turtles. Materials and Methods: Forty Loggerhead sea turtles comprising 25 males and 15 females stranded freshly dead between 2013 and 2018 along the coasts of Sicily, Southern Italy, were examined for arsenic, cadmium, and lead accumulation in muscle and adipose tissues by means of a validated ICP-MS method. A modified K index as a growth condition factor, namely Fulton's K index, was used. Samples were tested in duplicate. A Wilcoxon rank sum test was carried out to evaluate metal contents differences between muscle and adipose tissues and between genders. Results: The Fulton's K index suggested a good body condition of the C. caretta recovered with mean values of 5.34±3.40 (n=40; ±SD). Detectable concentrations of lead were found in 70% of the samples analysed with mean values of 0.65±1.67 mg/kg wet weight and 0.51±1.29 mg/kg wet weight in muscle and adipose tissues, respectively. No significant differences in arsenic, cadmium, and lead were detected between genders. In addition, no significant correlation was found between modified K index and concentrations of arsenic, cadmium, and lead. Clinical relevance: Findings on muscle and adipose tissues suggest chronic exposure of Caretta caretta to high concentrations of especially lead which might negatively affect health and welfare of these marine turtles although body condition was good

CTCF and BORIS Regulate Rb2/p130 Gene Transcription: A Novel Mechanism and a New Paradigm for Understanding the Biology of Lung Cancer

Although innumerable investigations regarding the biology of lung cancer have been carried out, many aspects thereof remain to be addressed, including the role played by the retinoblastoma-related protein Rb2/ p130 during the evolution of this disease. Here we report novel findings on the mechanisms that control Rb2/ p130 gene expression in lung fibroblasts and characterize the effects of Rb2/p130 deregulation on the proliferative features of lung cancer cells.We revealed for the first time that in lung fibroblasts the expression of Rb2/p130 gene is directly controlled by the chromatin insulator CCCTC-binding factor, CTCF, which by binding to the Rb2/p130 gene promoter induces, and/or maintains, a specific local chromatin organization that in turn governs the transcriptional activity of Rb2/p130 gene. However, in lung cancer cells the activity of CTCF in controlling Rb2/p130 gene expression is impaired by BORIS, a CTCF-paralogue, which by binding to the Rb2/p130 gene could trigger changes in the chromatin asset established by CTCF, thereby affecting CTCF regulatory activity on Rb2/p130 transcription. These studies not only provide essential basic insights into the molecular mechanisms that control Rb2/p130 gene expression in lung cancer, but also offer a potential paradigm for the actions of other activators and/or corepressors, such as CTCF and BORIS, that could be crucial in explaining how alterations in the mechanism regulating Rb2/p130 gene expression may accelerate the progression of lung tumors, or favor the onset of recurrence after cancer treatment. Mol Cancer Res; 9(2); 225–33. 2011 AACR

Chitosan-based scaffold modified with D-(+) raffinose for cartilage repair: an in vivo study

BackgroundOsteochondral defects significantly affect patients¿ quality of life and represent challenging tissue lesions, because of the poor regenerative capacity of cartilage. Tissue engineering has long sought to promote cartilage repair, by employing artificial scaffolds to enhance cell capacity to deposit new cartilage. An ideal biomaterial should closely mimic the natural environment of the tissue, to promote scaffold colonization, cell differentiation and the maintenance of a differentiated cellular phenotype. The present study evaluated chitosan scaffolds enriched with D-(+) raffinose in osteochondral defects in rabbits. Cartilage defects were created in distal femurs, both on the condyle and on the trochlea, and were left untreated or received a chitosan scaffold. The animals were sacrificed after 2 or 4 weeks, and samples were analysed microscopically.ResultsThe retrieved implants were surrounded by a fibrous capsule and contained a noticeable inflammatory infiltrate. No hyaline cartilage was formed in the defects. Although defect closure reached approximately 100% in the control group after 4 weeks, defects did not completely heal when filled with chitosan. In these samples, the lesion contained granulation tissue at 2 weeks, which was then replaced by fibrous connective tissue by week 4. Noteworthy, chitosan never appeared to be integrated in the surrounding cartilage.ConclusionsIn conclusion, the present study highlights the limits of D-(+) raffinose-enriched chitosan for cartilage regeneration and offers useful information for further development of this material for tissue repair

Peripheral blood regulatory T cell measurements correlate with serum vitamin D level in patients with psoriasis

OBJECTIVE:Vitamin D is the precursor of a hormone (1,25-dihydroxyvitamin D3), which has many biological effects in the skin. The immune modulator properties of vitamin D are mediated in part through effects on regulatory T cells (T-reg). Currently, in psoriasis, the relationship between vitamin D and T-reg has not well elucidated. We assess whether vitamin D status is correlated with circulating T-reg in patients affected by psoriasis and if there is a correlation with the severity of the disease evaluated with Psoriasis Area Severity Index (PASI) score. PATIENTS AND METHODS:For each patient we have analyzed, PASI-score, serum levels vitamin D and regulatory T cell percentages. Spearmen's coefficient was used between serum vitamin D levels and the predictors. Subsequently, the independent predictive factors were assessed by Multiple Regression. RESULTS:A total of 26 patients were included in our analysis. Using no parametric Spearman's Coefficient test between serum levels of vitamin D and the single variables, we found an association with T-reg population (p < 0.001) and with PASI-score (p = 0.04). CONCLUSIONS:While vitamin D treatment induces a cytokine profile known to favor the differentiation of T cells with suppressive activity, at the same time, several studies showed how vitamin D can prime for tolerogenic dendritic cells able to favor the differentiation of Treg from T naïve cells. Low levels of vitamin-D may decrease the number of circulatory T-reg, disrupting the immunological homeostasis in psoriatic patients and encouraging the inflammatory activity