A Stronger Europe in the World: Major Challenges for EU Trade Policy. College of Europe EU Diplomacy Paper 02/2020

On 29 January 2020, Dr. Sabine Weyand, Director-General for Trade at the European Commission, gave a lecture on “‘A stronger Europe in the world’: Major challenges for EU trade policy” at the College of Europe in Bruges. She started out with the challenges posed by the rise of populism and the shift towards more power-based relations and protectionism, arguing that trade is increasingly seen as a proxy through which the battle for political supremacy is fought. Dr. Weyand then explained the trade priorities of the new European Commission: reforming the World Trade Organisation for the benefit of a predictable, rules-based multilateral system; managing the bilateral relations with major powers including the United States, China and the United Kingdom; contributing as a ‘geopolitical Commission’ to other policy fields and in particular the European Green Deal; and levelling the playing field by promoting EU standard

Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation

The task of a visual landmark recognition system is to identify photographed buildings or objects in query photos and to provide the user with relevant information on them. With their increasing coverage of the world's landmark buildings and objects, Internet photo collections are now being used as a source for building such systems in a fully automatic fashion. This process typically consists of three steps: clustering large amounts of images by the objects they depict; determining object names from user-provided tags; and building a robust, compact, and efficient recognition index. To this date, however, there is little empirical information on how well current approaches for those steps perform in a large-scale open-set mining and recognition task. Furthermore, there is little empirical information on how recognition performance varies for different types of landmark objects and where there is still potential for improvement. With this paper, we intend to fill these gaps. Using a dataset of 500k images from Paris, we analyze each component of the landmark recognition pipeline in order to answer the following questions: How many and what kinds of objects can be discovered automatically? How can we best use the resulting image clusters to recognize the object in a query? How can the object be efficiently represented in memory for recognition? How reliably can semantic information be extracted? And finally: What are the limiting factors in the resulting pipeline from query to semantics? We evaluate how different choices of methods and parameters for the individual pipeline steps affect overall system performance and examine their effects for different query categories such as buildings, paintings or sculptures

The Use of ‘Only English’ in a Learner-Centered University Classroom in Japan

　 This review of research examines Japanese students’ beliefs about when and how much a nativeEnglish conversation instructor should use the students’ mother tongue (MT) in class. Thestudents’ MT being used in class must be taken into strong consideration since the exclusiveuse of the target language [English] in the classroom is not a recent practice when introducedalongside communicative methodology. Total language immersion has been the “bedrock” ofclassroom teaching for over a hundred years (Howatt 1984, Burden 139).　 However, recently it has been argued by language instructors and pedagogical researchers thatdenying students the use of their MT is on prescriptive grounds and is without due considerationto for their educational process. Thus, the principal aim of this paper is to invite practicingteachers to address their own styles and methods of teaching while seeking students’ opinions intheir own situation (p147)

Efficiently Generating Geometric Inhomogeneous and Hyperbolic Random Graphs

Hyperbolic random graphs (HRG) and geometric inhomogeneous random graphs (GIRG) are two similar generative network models that were designed to resemble complex real world networks. In particular, they have a power-law degree distribution with controllable exponent beta, and high clustering that can be controlled via the temperature T. We present the first implementation of an efficient GIRG generator running in expected linear time. Besides varying temperatures, it also supports underlying geometries of higher dimensions. It is capable of generating graphs with ten million edges in under a second on commodity hardware. The algorithm can be adapted to HRGs. Our resulting implementation is the fastest sequential HRG generator, despite the fact that we support non-zero temperatures. Though non-zero temperatures are crucial for many applications, most existing generators are restricted to T = 0. We also support parallelization, although this is not the focus of this paper. Moreover, we note that our generators draw from the correct probability distribution, i.e., they involve no approximation. Besides the generators themselves, we also provide an efficient algorithm to determine the non-trivial dependency between the average degree of the resulting graph and the input parameters of the GIRG model. This makes it possible to specify the desired expected average degree as input. Moreover, we investigate the differences between HRGs and GIRGs, shedding new light on the nature of the relation between the two models. Although HRGs represent, in a certain sense, a special case of the GIRG model, we find that a straight-forward inclusion does not hold in practice. However, the difference is negligible for most use cases

Mild Traumatic Brain Injuries as a Possible Risk Factor for Anterior Cruciate Ligament Tears in Female High School and College Athletes

Introduction: Both mild traumatic brain injuries (concussions) and anterior cruciate ligament (ACL) tears are among two of the most common, and most career-ending sports medicine injuries. Concussions often result from a blow to the head that can cause headaches, difficulty concentrating, photophobia, and memory loss. Concussion recovery often involved brain rest from athletics, screen exposure, reading, lights, and if severe enough, academics. The ACL is a ligament that is detrimental in stabilization of the knee joint. It connects the femur to the tibia within the joint, and can tear suddenly if it undergoes a quick twisting motion. ACL tears often present with severe pain, inability to apply pressure to the affected limb, swelling, and possibly an audible “popping” noise that can be heard at the time of injury. ACL tears often require surgical repair, and recovery time after the operation can be as long as 9 months. If not career ending, a torn ACL will certainly completely change an individuals athletic aspirations and ability to exercise, even in as early as high school and college. Understanding the probable risk factors of sustaining an ACL tear, like concussions, is incredibly important in proactive medicine, and can help emphasize proper time-off for recovery among athletes. This paper will address the relationship that concussions have on ACL injuries, and the role that return-to-play guidelines after a concussion have in order to improve quality of life, and prevent other injuries among athletes. Methods: 7 articles were selected after a literature search was completed in September 2018 on PubMed, November 2018 on ClinicalKey, and in November 2018 on Google scholar. These articles were selected based on their relevance to the question, their methods for conducting the study, and their results. Results: Based on the literature review, there is unsettling evidence that ACL tears may occur more frequently in female athletes who have recently sustained a concussion compared to those that have not. One study showed that athletes who experienced a concussion were 2-3 times more likely to experience a lower extremity injury within the same season. Another study demonstrated that athletes who tore their ACL often had neurocognitive deficits seen on preseason MRI’s and EEGs. Overall, the studies each displayed different points of view as to why concussions could possibly be a risk factor for ACL tears, and stressed the importance of adherence to return-to-play guidelines. Discussion: Most of the studies provided evidence suggesting that female athletes with concussions had higher rates of ACL tears compared to those who did not. There were a wide variety of study designs that were used, and each of the studies was unique in that there were a number of measured outcomes. These measured outcomes included neurocognitive function, lower extremity injuries, ACL tears, unrecognized concussions, concussion resolution index, jump-landing performance, and reaction times. There were notable limitations in a couple of the studies, including lack of blinding, reporting bias, and small sample sizes. Conclusion: Concussions as a risk factor for ACL tears among female high school and college athletes should be considered, and research should continue to be done in the subject area. While none of the studies aimed to answer this direct question, all of the data collected in these studies is highly suggestive that a link between the two could exist. Females are two times more likely to experience ACL tears than men, and it is important to discover the probable controllable risk factors for this injury in order to improve quality of life, and allow athletes to continue to play

Geometric Inhomogeneous Random Graphs for Algorithm Engineering

The design and analysis of graph algorithms is heavily based on the worst case. In practice, however, many algorithms perform much better than the worst case would suggest. Furthermore, various problems can be tackled more efficiently if one assumes the input to be, in a sense, realistic. The field of network science, which studies the structure and emergence of real-world networks, identifies locality and heterogeneity as two frequently occurring properties. A popular model that captures these properties are geometric inhomogeneous random graphs (GIRGs), which is a generalization of hyperbolic random graphs (HRGs). Aside from their importance to network science, GIRGs can be an immensely valuable tool in algorithm engineering. Since they convincingly mimic real-world networks, guarantees about quality and performance of an algorithm on instances of the model can be transferred to real-world applications. They have model parameters to control the amount of heterogeneity and locality, which allows to evaluate those properties in isolation while keeping the rest fixed. Moreover, they can be efficiently generated which allows for experimental analysis. While realistic instances are often rare, generated instances are readily available. Furthermore, the underlying geometry of GIRGs helps to visualize the network, e.g.,~for debugging or to improve understanding of its structure. The aim of this work is to demonstrate the capabilities of geometric inhomogeneous random graphs in algorithm engineering and establish them as routine tools to replace previous models like the Erd\H{o}s-R{\\u27e}nyi model, where each edge exists with equal probability. We utilize geometric inhomogeneous random graphs to design, evaluate, and optimize efficient algorithms for realistic inputs. In detail, we provide the currently fastest sequential generator for GIRGs and HRGs and describe algorithms for maximum flow, directed spanning arborescence, cluster editing, and hitting set. For all four problems, our implementations beat the state-of-the-art on realistic inputs. On top of providing crucial benchmark instances, GIRGs allow us to obtain valuable insights. Most notably, our efficient generator allows us to experimentally show sublinear running time of our flow algorithm, investigate the solution structure of cluster editing, complement our benchmark set of arborescence instances with a density for which there are no real-world networks available, and generate networks with adjustable locality and heterogeneity to reveal the effects of these properties on our algorithms

A Stream of Consciousness

https://scholar.dsu.edu/research-symposium/1002/thumbnail.jp