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Quantum-enhanced screened dark energy detection
Get PDFWe propose an experiment based on a Bose–Einstein condensate interferometer for strongly constraining fifth-force models. Additional scalar fields from modified gravity or higher dimensional theories may account for dark energy and the accelerating expansion of the Universe. These theories have led to proposed screening mechanisms to fit within the tight experimental bounds on fifth-force searches. We show that our proposed experiment would greatly improve the existing constraints on these screening models by many orders of magnitude
Prevalence, treatment and correlates of depression in multiple sclerosis
Get PDFBackgroundThe prevalence of depression in Multiple Sclerosis (MS) is often assessed by administering patient reported outcome measures (PROMs) examining depressive symptomatology to population cohorts; a recent review summarised 12 such studies, eight of which used the Hospital Anxiety and Depression Scale-Depression (HADS-D). In clinical practice, depression is diagnosed by an individual structured clinical interview; diagnosis often leads to treatment options including antidepressant medication. It follows that an MS population will include those whose current depressive symptoms meet threshold for depression diagnosis, plus those who previously met diagnostic criteria for depression and have been treated such that depressive symptoms have improved below that threshold. We examined a large MS population to establish a multi-attribute estimate of depression, taking into account probable depression on HADS-D, as well as anti-depressant medication use and co-morbidity data reporting current treatment for depression. We then studied associations with demographic and health status measures and the trajectories of depressive symptoms over time.MethodsParticipants were recruited into the UK-wide Trajectories of Outcome in Neurological Conditions-MS (TONiC-MS) study, with demographic and disease data from clinical records, PROMs collected at intervals of at least 9 months, as well as co-morbidities and medication. Interval level conversions of PROM data followed Rasch analysis. Logistic regression examined associations of demographic characteristics and symptoms with depression. Finally, a group-based trajectory model was applied to those with depression.ResultsBaseline data in 5633 participants showed the prevalence of depression to be 25.3% (CI: 24.2-26.5). There were significant differences in prevalence by MS subtype: relapsing 23.2% (CI: 21.8- 24.5), primary progressive 25.8% (CI: 22.5-29.3), secondary progressive 31.5% (CI: 29.0-34.0); disability: EDSS 0-4 19.2% (CI: 17.8-20.6), EDSS ≥4.5 31.9% (CI: 30.2-33.6); and age: 42-57 years 27.7% (CI: 26.0-29.3), above or below this range 23.1% (CI: 21.6-24.7). Fatigue, disability, self-efficacy and self esteem correlated with depression with a large effect size (>.8) whereas sleep, spasticity pain, vision and bladder had an effect size >.5. The logistic regression model (N=4938) correctly classified 80% with 93% specificity: risk of depression was increased with disability, fatigue, anxiety, more comorbidities or current smoking. Higher self-efficacy or self esteem and marriage reduced depression. Trajectory analysis of depressive symptoms over 40 months in those with depression (N=1096) showed three groups: 19.1% with low symptoms, 49.2% with greater symptoms between the threshold of possible and probable depression, and 31.7% with high depressive symptoms. 29.9% (CI: 27.6-32.3) of depressed subjects were untreated, conversely of those treated, 26.1% still had a symptom level consistent with a probable case (CI: 23.5-28.9).ConclusionA multi-attribute estimate of depression in MS is essential because using only screening questionnaires, diagnoses or antidepressant medication all under-estimate the true prevalence. Depression affects 25.3% of those with MS, almost half of those with depression were either untreated or still had symptoms indicating probable depression despite treatment. Services for depression in MS must be pro-active and flexible, recognising the heterogeneity of outcomes and reaching out to those with ongoing symptoms
Using Social Media to Develop the Capacity and Diversity of the Runnymede Neighbourhood Watch Scheme
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Behavior change approaches for cyber security and the need for ethics
Get PDFHumans are reportedly exploited as the main attack vector for security breaches. In order to minimize the susceptibility of humans to security at-tacks, it is not sufficient for individuals to just be aware, but they need to change their behavior as well. Such behavior change, that is, the modification of user behavior, can occur via targeted interventions, which are gradually being introduced in cyber security. In this paper, we identify and categorize the main approaches used to change user behavior and portray the main limitations of these approaches. Other fields, like health sciences, psychology and economics, have been traditionally more mature in ethics-related considerations. We suggest that although individual behavior change is increasingly being embraced by security practitioners and professionals, ethical aspects of the accompanied interventions are by large neglected in the field. We explore the ethical traditions of utilitarian, deontological and virtue ethics and their relations with security. We posit that ethical frameworks are needed for cyber behavior change interventions as a means to enhance security hygiene on both an individual and an organizational level
The death of exams? Grade inflation and student satisfaction when coursework replaces exams
Get PDFA mid-sized advanced undergraduate economics module has undergone an assessment reform, where a typical 60% final exam was replaced by four scaffolded coursework assignments, each carrying a 15% weight in the final grade. As a result, student satisfaction with assessment and feedback went up at the expense of higher staff workload. In addition, the reform raised the final grades over and above the increase typically associated with an improvement in learning and engagement, effectively triggering grade inflation. The paper identifies the coursework assignment that inflated the grades. It then proceeds with grade simulations, whose purpose is to formulate an assessment reform agenda aiming to maintain high student satisfaction, disinflate grades, and stabilise staff workload. Finally, the paper evaluates the scalability of assessment reforms where final exams are targeted for replacement by coursework assessments. The evaluation suggests caution with such reforms, if they are necessary in the first place. They may both be feasible and desirable in small classes, but compelling arguments caution against a premature wholesale rollout
QUEST-DMC superfluid <sup>3</sup>He detector for sub-GeV dark matter
Get PDFThe focus of dark matter searches to date has been on Weakly Interacting Massive Particles (WIMPs) in the GeV/c2-TeV/c2 mass range. The direct, indirect and collider searches in this mass range have been extensive but ultimately unsuccessful, providing a strong motivation for widening the search outside this range. Here we describe a new concept for a dark matter experiment, employing superfluid 3He as a detector for dark matter that is close to the mass of the proton, of order 1 GeV/c2. The QUEST-DMC detector concept is based on quasiparticle detection in a bolometer cell by a nanomechanical resonator. In this paper we develop the energy measurement methodology and detector response model, simulate candidate dark matter signals and expected background interactions, and calculate the sensitivity of such a detector. We project that such a detector can reach sub-eV nuclear recoil energy threshold, opening up new windows on the parameter space of both spin-dependent and spin-independent interactions of light dark matter candidates
Out of Sight, Out of Mind:Better Automatic Vulnerability Repair by Broadening Input Ranges and Sources
Get PDFThe advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fixed code. Nevertheless, existing DL-based vulnerability repair methods face notable limitations: 1) they struggle to handle lengthy vulnerable code, 2) they treat code as natural language texts, neglecting its inherent structure, and 3) they do not tap into the valuable expert knowledge present in the expert system. To address this, we propose VulMaster, a Transformer-based neural network model that excels at generating vulnerability repairs by comprehensively understanding the entire vulnerable code, irrespective of its length. This model also integrates diverse information, encompassing vulnerable code structures and expert knowledge from the CWE system. We evaluated VulMaster on a real-world C/C++ vulnerability repair dataset comprising 1,754 projects with 5,800 vulnerable functions. The experimental results demonstrated that VulMaster exhibits substantial improvements compared to the learning-based state-of-the-art vulnerability repair approach. Specifically, VulMaster improves the EM, BLEU, and CodeBLEU scores from 10.2% to 20.0%, 21.3% to 29.3%, and 32.5% to 40.9%, respectively
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