Characterization of order-like dependencies with formal concept analysis

Functional Dependencies (FDs) play a key role in many fields of the relational database model, one of the most widely used database systems. FDs have also been applied in data analysis, data quality, knowl- edge discovery and the like, but in a very limited scope, because of their fixed semantics. To overcome this limitation, many generalizations have been defined to relax the crisp definition of FDs. FDs and a few of their generalizations have been characterized with Formal Concept Analysis which reveals itself to be an interesting unified framework for charac- terizing dependencies, that is, understanding and computing them in a formal way. In this paper, we extend this work by taking into account order-like dependencies. Such dependencies, well defined in the database field, consider an ordering on the domain of each attribute, and not sim- ply an equality relation as with standard FDs.Peer ReviewedPostprint (published version

Titratable fixed-ratio combination of insulin glargine plus lixisenatide: A simplified approach to glycemic control in type 2 diabetes mellitus

Approximately 50% of patients with type 2 diabetes mellitus (T2DM) do not achieve glycemic targets and require treatment intensification. A fixed-ratio combination of a glucagon-like peptide-1 receptor agonist (GLP-1 RA) with basal insulin, such as lixisenatide with insulin glargine (iGlarLixi), exploits the complementary mechanisms of action of each component to address hyperglycemia while mitigating potential adverse events (AEs). The iGlarLixi dose is titrated considering the effect of basal insulin on fasting plasma glucose, and the fixed-ratio combination ensures that the lixisenatide dose never exceeds 20 μg/day. We describe the characteristics of iGlarLixi therapy, based on the LixiLan clinical program, and provide guidance on the characteristics of patients likely to benefit from such treatment in routine clinical practice. In the phase III LixiLan trials, iGlarLixi resulted in significantly greater reductions in glycated hemoglobin (HbA1c), better achievement of HbA1c targets, less glycemic variability versus insulin glargine, lixisenatide or GLP-1 RA alone, and was associated with weight control, less hypoglycemia versus insulin glargine, and fewer GI AEs versus lixisenatide. Findings were consistent regardless of age, diabetes duration, and baseline HbA1c. The efficacy, safety, and convenient once-daily administration schedule of iGlarLixi make it a valuable treatment option for patients with T2DM requiring treatment intensification

Computing Functional Dependencies with Pattern Structures

The treatment of many-valued data with FCA has been achieved by means of scaling. This method has some drawbacks, since the size of the resulting formal contexts depends usually on the number of di erent values that are present in a table, which can be very large. Pattern structures have been proved to deal with many-valued data, offering a viable and sound alternative to scaling in order to represent and analyze sets of many-valued data with FCA. Functional dependencies have already been dealt with FCA using the binarization of a table, that is, creating a formal context out of a set of data. Unfortunately, although this method is standard and simple, it has an important drawback, which is the fact that the resulting context is quadratic in number of objects w.r.t. the original set of data. In this paper, we examine how we can extract the functional dependencies that hold in a set of data using pattern structures. This allows to build an equivalent concept lattice avoiding the step of binarization, and thus comes with better concept representation and computation.Postprint (published version

The Trigger System of the ARGO-YBJ detector

The ARGO-YBJ experiment has been designed to detect air shower events over a large size scale and with an energy threshold of a few hundreds GeV. The building blocks of the ARGO-YBJ detector are single-gap Resistive Plate Counters (RPCs). The trigger logic selects the events on the basis of their hit multiplicity. Inclusive triggers as well as dedicated triggers for specific physics channels or calibration purposes have been developed. This paper describes the architecture and the main features of the trigger system.Comment: 4 pages, to be published in the Proceedings of the 28th International Cosmic Ray Conference (Tsukuba, Japan 2003

Reducing the psychological burden of isolated oncological patients by means of decision trees

This century has seen several outbreaks of epidemics caused by a common sub-family of coronaviruses such as the responsible for COVID-19 outbreak. The most ominous variants have developed a peculiar viral mechanisms that allows the virus to directly attack the pulmonary tissues often causing a set of dangerous symptoms. It made quite evident that we need a global response to prepare health systems for future epidemics. Unfortunately, during such kind of diseases’ outbreaks a large amount of time is required to the caregivers for sanitization and cleaning operations, therefore tampering with number and duration of visits to patients, especially in oncology wards. Such patients are then left alone for a long time, it follows that their perceived quality of service is greatly diminished, often determining ill-fated consequences also on the psychological side, with significant fallbacks on the recovery possibilities and speed. In this paper we explore an algorithmic approach to automatic communication interfaces that could enhance and enforce the perceived quality of care by the patients in in order to reduce predisposing factors that could potentially tamper with the patient’s ability to recover, also preventing the occurrence of precipitating factors that could lead a therapy to complete failure. The proposed interface could be used to connect the patients with a psychological support when it is most needed, and, moreover, to connect them with their physicians and families, and also to the outside world. In particular we aim to provide the psychological support that is actually excluded in pandemics such as the COVID-19 emergency, mainly in order to enforce the healthcare and sanification protocols, due to its potential unsafety related to the introduction of more personnel into the hospital

A cloud-oriented architecture for the remote assessment and follow-up of hospitalized patients

During the last months the dramatic COVID-19 outbreak has exposed the fragility of our healthcare system, as well as the need for a smart remote follow-up system for the patients, in order to less the burden on the healthcare service and reduce the average hospitalization time. In this paper we proposed a solution designed to grant maximum flexibility by means of the allocation of resources on a cloud service for the remote follow-up of patients. Such resources can be used as a remote support for the caregiver both when planning or enforcing a therapeutic path. A major explanation behind follow-up regards the location and treatment of potentially adverse reactions after treatments. Physical side effects of the different modalities of treatment might be various and crippling after chemotherapy and radiotherapy. Moreover remote follow up can be a life-changing solution also on the economical side, due to the implication of therapeutic attendances for patients as far as missed work and travel costs that must likewise be comprehended in the overall economical burden. In an investigation of patients with testicular disease, Campbell et al. Finally such a solution could effectively improve the patient's adherence to the therapeutic plan. The ability to remotely follow follow-up is therefore a monetarily alluring choice as far as investment funds, also given the improved efficiencies, reduced cost and number of missed working days for the patient. Patients with a patient-held record may also take advantage of a more conscious and motivated interest over their own wellbeing, illness and treatment, with a direct impact on patient's adherence to the therapeutic plan

Women and Domestic violence in the professional experience of Italian General Practitioners

A number of studies have investigated the roots of the destructive power acting within the couple (Welzer Lang, 1991; Walker, 1979, 2000). Danis and Lockhart (2003) highlighted how social workers’ best practices, are not frequently used, especially in regards to contact with the victims of intimate-partner violence. We investigated how Italian general practitioners (IGPs) deal with this issue within their professional practice. A snowball sample of 268 IGPs was taken, in order to collect their beliefs concerning the victim, the aggressor and the violent couple’s relationship. Furthermore the experience in coping with both suspected and actual cases of domestic violence, as well as the GP’s needs and expectations was taken in consideration

Characterizing approximate-matching dependencies in formal concept analysis with pattern structures

Functional dependencies (FDs) provide valuable knowledge on the relations between attributes of a data table. A functional dependency holds when the values of an attribute can be determined by another. It has been shown that FDs can be expressed in terms of partitions of tuples that are in agreement w.r.t. the values taken by some subsets of attributes. To extend the use of FDs, several generalizations have been proposed. In this work, we study approximatematching dependencies that generalize FDs by relaxing the constraints on the attributes, i.e. agreement is based on a similarity relation rather than on equality. Such dependencies are attracting attention in the database field since they allow uncrisping the basic notion of FDs extending its application to many different fields, such as data quality, data mining, behavior analysis, data cleaning or data partition, among others. We show that these dependencies can be formalized in the framework of Formal Concept Analysis (FCA) using a previous formalization introduced for standard FDs. Our new results state that, starting from the conceptual structure of a pattern structure, and generalizing the notion of relation between tuples, approximate-matching dependencies can be characterized as implications in a pattern concept lattice. We finally show how to use basic FCA algorithms to construct a pattern concept lattice that entails these dependencies after a slight and tractable binarization of the original data.Postprint (author's final draft

Characterizing covers of functional dependencies using FCA

Functional dependencies (FDs) can be used for various important operations on data, for instance, checking the consistency and the quality of a database (including databases that contain complex data). Consequently, a generic framework that allows mining a sound, complete, non-redundant and yet compact set of FDs is an important tool for many different applications. There are different definitions of such sets of FDs (usually called cover). In this paper, we present the characterization of two different kinds of covers for FDs in terms of pattern structures. The convenience of such a characterization is that it allows an easy implementation of efficient mining algorithms which can later be easily adapted to other kinds of similar dependencies. Finally, we present empirical evidence that the proposed approach can perform better than state-ofthe-art FD miner algorithms in large databases.Peer ReviewedPostprint (published version

Balance equations-based properties of the Rabi Hamiltonian

A stationary physical system satisfies peculiar balance conditions involving mean values of appropriate observables. In this paper we show how to deduce such quantitative links, named balance equations, demonstrating as well their usefulness in bringing to light physical properties of the system without solving the Schrodinger equation. The knowledge of such properties in the case of Rabi Hamiltonian is exploit to provide arguments to make easier the variational engineering of the ground state of this model.Comment: Accepted for publication on Journal of Russian Laser Researc