Surjective H-Colouring over reflexive digraphs

The Surjective H-Colouring problem is to test if a given graph allows a vertex-surjective homomorphism to a fixed graph H. The complexity of this problem has been well studied for undirected (partially) reflexive graphs. We introduce endo-triviality, the property of a structure that all of its endomorphisms that do not have range of size 1 are automorphisms, as a means to obtain complexity-theoretic classifications of Surjective H-Colouring in the case of reflexive digraphs. Chen (2014) proved, in the setting of constraint satisfaction problems, that Surjective H-Colouring is NP-complete if H has the property that all of its polymorphisms are essentially unary. We give the first concrete application of his result by showing that every endo-trivial reflexive digraph H has this property. We then use the concept of endo-triviality to prove, as our main result, a dichotomy for Surjective H-Colouring when H is a reflexive tournament: if H is transitive, then Surjective H-Colouring is in NL; otherwise, it is NP-complete. By combining this result with some known and new results, we obtain a complexity classification for Surjective H-Colouring when H is a partially reflexive digraph of size at most 3

A combinatorial approach to knot recognition

This is a report on our ongoing research on a combinatorial approach to knot recognition, using coloring of knots by certain algebraic objects called quandles. The aim of the paper is to summarize the mathematical theory of knot coloring in a compact, accessible manner, and to show how to use it for computational purposes. In particular, we address how to determine colorability of a knot, and propose to use SAT solving to search for colorings. The computational complexity of the problem, both in theory and in our implementation, is discussed. In the last part, we explain how coloring can be utilized in knot recognition

Prevalence of Mental Health Disorder Symptoms and Rates of Help-seeking Among University-Enrolled, Black Men

Background. Black men in college represent a subgroup of emerging adults who are at increased risk of developing mental health disorders (MHDs), such as anxiety and depression. Such risk has been attributed to disproportionate experiences with everyday racial discrimination and high levels of psychological distress. Despite being at higher risk, university-enrolled, Black men are not utilizing mental health or health resources at optimal rates. The current evidence base describing prevalence of MHDs and health services utilization among Black men in college is limited. The present study addresses this by examining mental health prevalence among university-enrolled, Black men and their rates of health services utilization. Methods. We analyzed data (N ~ 2500) from a student survey, Spit for Science, a longitudinal, ongoing, research study at a mid-Atlantic, public university. Participants are given surveys in their freshman year and follow-up surveys every spring thereafter. Measures included: mental health disorders (depression and anxiety, as measured by the Symptom Checklist 90) and campus health service utilization (counseling center, health services, wellness center, and recreational sports). We conducted descriptive analyses to determine MHD symptom prevalence and utilization rates; Mann Whitney U tests to compare prevalence rates to White men and Black women; and, Chi-squared tests to compare rates of utilization among groups. Results. During their Freshman year, greater than 60% of students from each ethnic group reported at least one anxiety symptom and greater than 80% reported at least one depressive symptom. By senior year, reporting rates decreased significantly for Black men (49.6%) but remained high for White men (69.1%) and Black women (63%); p \u3c0.000. For depression, results were similar; however, only significant differences between Black men (72.7%) and Black women (87.1%); p\u3c0.000. Black men (20.4%), though reporting high levels of symptoms, still utilized counseling services at lower rates compared to White men (37.76%); p = 0.024. Conclusion. Findings suggest that Black men underutilize available campus health resources despite reporting one or more symptoms associated with anxiety and depression. Further research and prevention efforts are needed to improve help-seeking among this vulnerable population.https://scholarscompass.vcu.edu/gradposters/1077/thumbnail.jp

QCSP on reflexive tournaments

We give a complexity dichotomy for the Quantified Constraint Satisfaction Problem QCSP(H) when H is a reflexive tournament. It is well known that reflexive tournaments can be split into a sequence of strongly connected components H1,…,Hn so that there exists an edge from every vertex of Hi to every vertex of Hj if and only if

Treatment Options for Paediatric Anaplastic Large Cell Lymphoma (ALCL): Current Standard and beyond.

Anaplastic Lymphoma Kinase (ALK)-positive Anaplastic Large Cell Lymphoma (ALCL), remains one of the most curable cancers in the paediatric setting; multi-agent chemotherapy cures approximately 65-90% of patients. Over the last two decades, major efforts have focused on improving the survival rate by intensification of combination chemotherapy regimens and employing stem cell transplantation for chemotherapy-resistant patients. More recently, several new and 'renewed' agents have offered the opportunity for a change in the paradigm for the management of both chemo-sensitive and chemo-resistant forms of ALCL. The development of ALK inhibitors following the identification of the EML4-ALK fusion gene in Non-Small Cell Lung Cancer (NSCLC) has opened new possibilities for ALK-positive ALCL. The uniform expression of CD30 on the cell surface of ALCL has given the opportunity for anti-CD30 antibody therapy. The re-evaluation of vinblastine, which has shown remarkable activity as a single agent even in the face of relapsed disease, has led to the consideration of a revised approach to frontline therapy. The advent of immune therapies such as checkpoint inhibition has provided another option for the treatment of ALCL. In fact, the number of potential new agents now presents a real challenge to the clinical community that must prioritise those thought to offer the most promise for the future. In this review, we will focus on the current status of paediatric ALCL therapy, explore how new and 'renewed' agents are re-shaping the therapeutic landscape for ALCL, and identify the strategies being employed in the next generation of clinical trials

The STAR MAPS-based PiXeL detector

The PiXeL detector (PXL) for the Heavy Flavor Tracker (HFT) of the STAR experiment at RHIC is the first application of the state-of-the-art thin Monolithic Active Pixel Sensors (MAPS) technology in a collider environment. Custom built pixel sensors, their readout electronics and the detector mechanical structure are described in detail. Selected detector design aspects and production steps are presented. The detector operations during the three years of data taking (2014-2016) and the overall performance exceeding the design specifications are discussed in the conclusive sections of this paper

CSP for binary conservative relational structures

We prove that whenever A is a 3-conservative relational structure with only binary and unary relations then the algebra of polymorphisms of A either has no Taylor operation (i.e. CSP(A) is NP-complete), or generates a congruence meet semidistributive variety (i.e. CSP(A) has bounded width).Comment: 9 pages, 3 figure

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset