Everyday concept detection in visual lifelogs: validation, relationships and trends

The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a user's day-to-day activities. It can capture up to 3,000 images per day, equating to almost 1 million images per year. It is used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearer's life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the novel domain of visual lifelogs. A concept detector models the correspondence between low-level visual features and high-level semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concept's presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were then evaluated on a subset of 95,907 images, to determine the precision for detection of each semantic concept. We conduct further analysis on the temporal consistency, co-occurance and trends within the detected concepts to more extensively investigate the robustness of the detectors within this novel domain. We additionally present future applications of concept detection within the domain of lifelogging

Effect of alirocumab on mortality after acute coronary syndromes. An analysis of the ODYSSEY OUTCOMES randomized clinical trial

Background: Previous trials of PCSK9 (proprotein convertase subtilisin-kexin type 9) inhibitors demonstrated reductions in major adverse cardiovascular events, but not death. We assessed the effects of alirocumab on death after index acute coronary syndrome. Methods: ODYSSEY OUTCOMES (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab) was a double-blind, randomized comparison of alirocumab or placebo in 18 924 patients who had an ACS 1 to 12 months previously and elevated atherogenic lipoproteins despite intensive statin therapy. Alirocumab dose was blindly titrated to target achieved low-density lipoprotein cholesterol (LDL-C) between 25 and 50 mg/dL. We examined the effects of treatment on all-cause death and its components, cardiovascular and noncardiovascular death, with log-rank testing. Joint semiparametric models tested associations between nonfatal cardiovascular events and cardiovascular or noncardiovascular death. Results: Median follow-up was 2.8 years. Death occurred in 334 (3.5%) and 392 (4.1%) patients, respectively, in the alirocumab and placebo groups (hazard ratio [HR], 0.85; 95% CI, 0.73 to 0.98; P=0.03, nominal P value). This resulted from nonsignificantly fewer cardiovascular (240 [2.5%] vs 271 [2.9%]; HR, 0.88; 95% CI, 0.74 to 1.05; P=0.15) and noncardiovascular (94 [1.0%] vs 121 [1.3%]; HR, 0.77; 95% CI, 0.59 to 1.01; P=0.06) deaths with alirocumab. In a prespecified analysis of 8242 patients eligible for ≥3 years follow-up, alirocumab reduced death (HR, 0.78; 95% CI, 0.65 to 0.94; P=0.01). Patients with nonfatal cardiovascular events were at increased risk for cardiovascular and noncardiovascular deaths (P<0.0001 for the associations). Alirocumab reduced total nonfatal cardiovascular events (P<0.001) and thereby may have attenuated the number of cardiovascular and noncardiovascular deaths. A post hoc analysis found that, compared to patients with lower LDL-C, patients with baseline LDL-C ≥100 mg/dL (2.59 mmol/L) had a greater absolute risk of death and a larger mortality benefit from alirocumab (HR, 0.71; 95% CI, 0.56 to 0.90; Pinteraction=0.007). In the alirocumab group, all-cause death declined wit h achieved LDL-C at 4 months of treatment, to a level of approximately 30 mg/dL (adjusted P=0.017 for linear trend). Conclusions: Alirocumab added to intensive statin therapy has the potential to reduce death after acute coronary syndrome, particularly if treatment is maintained for ≥3 years, if baseline LDL-C is ≥100 mg/dL, or if achieved LDL-C is low. Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01663402

Unleashing the Usefulness of Educational Resources through Mining of Educational Metadata

While there is a strong movement to develop new educational resources to bring students to the competencies represented by educational content standards, it is recognized that there are vast repositories of educational resources already developed that are suitable to address those competencies. However, these resources need to be indexed by national and state standards to make them accessible for teachers who are increasingly required to teach to certain educational standards (Diekema and Chen, 2005). In the early 1980s, a perceived a crisis in the American education system encouraged the creation of national standards by professional subject-area organizations such as the National Council for Teachers of Mathematics. These national standards aimed to clearly define what students in certain grade levels are expected to know in core subject areas (Ratvitch, 1995). Eventually all states followed suit and published their own educational standards, often using the national standards as a guideline. Adding state standard information of every state to each resource is a large task, especially when done completely manually. Manual standard to standard alignment efforts (e.g. Align to Achieve) proved too difficult to maintain as the standards exist in all core subject areas; on national, state and local levels; and are revised regularly. Each set of standards utilizes discrete language, differing grade bands, distinct organizational structures and different levels of specificity in the coverage of a particular standard. To remedy these problems the Center for Natural Language Processing (CNLP) at Syracuse University has created a technology (Standards Alignment Tool – SAT) for automatically aligning state standards and national standards (Diekema et al., 2007). This paper explores whether it is possible to exploit existing manual standards assignments by mining the groups of standards that have been assigned to a particular resource. In other words, rather than requiring explicit manual alignments between equivalent standards, this preliminary research is trying to use the assignment of standards as metadata to resources to determine which state standards might be equivalent. An increasing number of manual standards assignments is becoming available, possibly making this approach a viable and sustainable option. The ultimate goal of this research is to establish an automatic correlation between standards based on their shared occurrence. At the initial stage we are only considering groups of standards that were assigned to the same lesson plan. Eventually we’ll take into the co-occurrence statistics of standards across the entire corpus of lesson plans

The memory glasses: subliminal vs. overt memory support with imperfect information.

Abstract Wearables are frequently designed to support users engaged in complex &quot;real world&quot; activities, rangin