Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Osteoarthritis (OA) is associated with cartilage destruction, subchondral bone remodeling and inflammation of the synovial membrane, although the etiology and pathogenesis underlying this debilitating disease are poorly understood. Secreted inflammatory molecules, such as proinflammatory cytokines, are among the critical mediators of the disturbed processes implicated in OA pathophysiology. Interleukin (IL)-1β and tumor necrosis factor (TNF), in particular, control the degeneration of articular cartilage matrix, which makes them prime targets for therapeutic strategies. Animal studies provide support for this approach, although only a few clinical studies have investigated the efficacy of blocking these proinflammatory cytokines in the treatment of OA. Apart from IL-1β and TNF, several other cytokines including IL-6, IL-15, IL-17, IL-18, IL-21, leukemia inhibitory factor and IL-8 (a chemokine) have also been shown to be implicated in OA and could possibly be targeted therapeutically. This Review discusses the current knowledge regarding the role of proinflammatory cytokines in the pathophysiology of OA and addresses the potential of anticytokine therapy in the treatment of this disease.

Role of proinflammatory cytokines in the pathophysiology of osteoarthritis

The long-term ambition of the Tactile Internet is to enable a democratization of skill, and how it is being delivered globally. An integral part of this is to be able to transmit touch in perceived real-time, which is enabled by suitable robotics and haptics equipment at the edges, along with an unprecedented communications network. The fifth generation (5G) mobile communications systems will underpin this emerging Internet at the wireless edge. This paper presents the most important technology concepts, which lay at the intersection of the larger Tactile Internet and the emerging 5G systems. The paper outlines the key technical requirements and architectural approaches for the Tactile Internet, pertaining to wireless access protocols, radio resource management aspects, next generation core networking capabilities, edge-cloud, and edge-AI capabilities. The paper also highlights the economic impact of the Tactile Internet as well as a major shift in business models for the traditional telecommunications ecosystem.

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5G-Enabled Tactile Internet

In the context of OA disease, NF-kappaB transcription factors can be triggered by a host of stress-related stimuli including pro-inflammatory cytokines, excessive mechanical stress and ECM degradation products. Activated NF-kappaB regulates the expression of many cytokines and chemokines, adhesion molecules, inflammatory mediators, and several matrix degrading enzymes. NF-kappaB also influences the regulated accumulation and remodeling of ECM proteins and has indirect positive effects on downstream regulators of terminal chondrocyte differentiation (including beta-catenin and Runx2). Although driven partly by pro-inflammatory and stress-related factors, OA pathogenesis also involves a "loss of maturational arrest" that inappropriately pushes chondrocytes towards a more differentiated, hypertrophic-like state. Growing evidence points to NF-kappaB signaling as not only playing a central role in the pro-inflammatory stress-related responses of chondrocytes to extra- and intra-cellular insults, but also in the control of their differentiation program. Thus unlike other signaling pathways the NF-kappaB activating kinases are potential therapeutic OA targets for multiple reasons. Targeted strategies to prevent unwanted NF-kappaB activation in this context, which do not cause side effects on other proteins or signaling pathways, need to be focused on the use of highly specific drug modalities, siRNAs or other biological inhibitors that are targeted to the activating NF-kappaB kinases IKKalpha or IKKbeta or specific activating canonical NF-kappaB subunits. However, work remains in its infancy to evaluate the effects of efficacious, targeted NF-kappaB inhibitors in animal models of OA disease in vivo and to also target these strategies only to affected cartilage and joints to avoid other undesirable systemic effects.

NF-κB Signaling: Multiple Angles to Target OA

The NF-κB signalling pathway in osteoarthritis

Spatial ventriloquism refers to the phenomenon that a visual stimulus such as a flash can attract the perceived location of a spatially discordant but temporally synchronous sound. An analogous example of mutual attraction between audition and vision has been found in the temporal domain, where temporal aspects of a visual event, such as its onset, frequency, or duration, can be biased by a slightly asynchronous sound. In this review, we examine various manifestations of spatial and temporal attraction between the senses (both direct effects and aftereffects), and we discuss important constraints on the occurrence of these effects. Factors that potentially modulate ventriloquism—such as attention, synesthetic correspondence, and other cognitive factors—are described. We trace theories and models of spatial and temporal ventriloquism, from the traditional unity assumption and modality appropriateness hypothesis to more recent Bayesian and neural network approaches. Finally, we summarize recent evidence probing the underlying neural mechanisms of spatial and temporal ventriloquism.

/pdf/intersensory-binding-across-space-and-time-a-tutorial-review-45zwjm3vav.pdf

Intersensory binding across space and time: A tutorial review

Suppression of early experimental osteoarthritis by in vivo delivery of the adenoviral vector-mediated NF-κBp65-specific siRNA

Research on multisensory interactions has shown that the perceived timing of a visual event can be captured by a temporally proximal sound. This effect has been termed 'temporal ventriloquism effect.' Using the Ternus display, we systematically investigated how auditory configurations modulate the visual apparent-motion percepts. The Ternus display involves a multielement stimulus that can induce either of two different percepts of apparent motion: 'element motion' or 'group motion'. We found that two sounds presented in temporal proximity to, or synchronously with, the two visual frames, respectively, can shift the transitional threshold for visual apparent motion (Experiments 1 and 3). However, such effects were not evident with single-sound configurations (Experiment 2). A further experiment (Experiment 4) provided evidence that time interval information is an important factor for crossmodal interaction of audiovisual Ternus effect. The auditory interval was perceived as longer than the same physical visual interval in the sub-second range. Furthermore, the perceived audiovisual interval could be predicted by optimal integration of the visual and auditory intervals.

Auditory temporal modulation of the visual Ternus effect: the influence of time interval.

Temporal discontinuities and delay caused by packet loss or communication latency often occur in multimodal telepresence systems. It is known that such artifacts can influence the feeling of presence [1]. However, it is largely unknown how the packet loss and communication latency affect the temporal perception of multisensory events. In this article, we simulated random packet dropouts and communication latency in the visual modality and investigated the effects on the temporal discrimination of visual-haptic collisions. Our results demonstrated that the synchronous perception of crossmodal events was very sensitive to the packet loss rate. The packet loss caused the impression of time delay and influenced the perception of the subsequent events. The perceived time of the visual event increased linearly, and the temporal discrimination deteriorated, with increasing packet loss rate. The perceived time was also influenced by the communication delay, which caused time to be slightly overestimated.

Effects of Packet Loss and Latency on the Temporal Discrimination of Visual-Haptic Events

A wide range of web corpora are in the form of short text, such as QA queries, search queries and news titles. Entity linking for these short texts is quite important. Most of supervised approaches are not effective for short text entity linking. The training data for supervised approaches are not suitable for short text and insufficient for low-resourced languages. Previous unsupervised methods are incapable of handling the sparsity and noisy problem of short text. We try to solve the problem by mapping the sparse short text to a topic space. We notice that the concepts of entities have rich topic information and characterize entities in a very fine-grained granularity. Hence, we use the concepts of entities as topics to explicitly represent the context, which helps improve the performance of entity linking for short text. We leverage our linking approach to segment the short text semantically, and build a system for short entity text recognition and linking. Our entity linking approach exhibits the state-of-the-art performance on several datasets for the realistic short text entity linking problem.

Lihan Chen

Papers

Intersensory binding across space and time: A tutorial review

Suppression of early experimental osteoarthritis by in vivo delivery of the adenoviral vector-mediated NF-κBp65-specific siRNA

Auditory temporal modulation of the visual Ternus effect: the influence of time interval.

Effects of Packet Loss and Latency on the Temporal Discrimination of Visual-Haptic Events

Short Text Entity Linking with Fine-grained Topics