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Which consumers will pay for greener products?
Reading, UK – Tuesday, 19 June 2007
According to survey results released by Canalys today, consumers in other Western European countries are more willing than those in the UK to pay a premium for electronic goods produced in a more environmentally conscious way. The online survey was conducted in April among more than 2,000 employed, adult mobile phone and PC users in France, Germany, Italy, Spain and the UK.
The independent survey asked a series of questions relating to consumer attitudes, opinions and behaviour in relation to a number of technology products and services. One of the key issues facing the technology industry is how it contributes to reducing the threat of global warming. Technology can help businesses reduce their carbon footprints, for example by replacing paper forms with digital equivalents or reducing employees' road travel through the use of mobile devices and GPS systems. But as a global industry manufacturing and distributing billions of electronic products each year, its own emissions and the commercial implications of reducing them must be considered.
"Slightly more than half – 55% – of consumers agreed or strongly agreed with the statement: 'I would pay up to a 10% premium for electronic products that were manufactured in a more environmentally conscious way.' You should expect more people to say this than would actually do it, but 11% strongly agreed with the statement and the reality is most likely somewhere between the two today," said Canalys senior analyst Pete Cunningham. "We will be monitoring this issue closely in future surveys to see the effect growing awareness of environmental concerns is having on consumer buying behaviour."
Widespread media coverage of global warming has helped raise awareness across all social groups. The Canalys survey shows that there was very little variation in attitude across gender or income groups. There was also only slight variation among respondents of different education levels. Two areas where there were bigger differences in attitude were across age groups and countries. The willingness to pay such a premium decreased with the age of respondent, from 67% of 15–17 year olds down to 49% of those aged 50 or over.
"More than two-thirds of respondents in Spain indicated they would be willing to pay a premium for greener electronic products, compared to 55% in Italy, Germany and France. The UK came out badly in comparison to the other countries," Cunningham continued. "It was bottom of the five by some way with only 40% suggesting they were prepared to pay more. It also had the highest proportion, at 22%, who disagreed strongly with the idea."
Reinforcing these results, in Italy, 66% of respondents said they already only bought kitchen appliances with the best energy efficiency ratings. In France it was 61%, and over 50% in both Germany and Spain. In the UK the proportion was just 41%. In areas where the behaviour does not require the consumer to make or change a purchase decision, such as turning off the TV at night or turning lights off when leaving a room, the UK is far more in line with the other countries. In fact, across all countries, energy-saving measures that did not involve a purchase decision were more commonly undertaken than those that did.
"Technology vendors exploring green initiatives in relation to the products they are building and marketing must be aware of these differences in attitude," Cunningham said in closing. "Current survey results suggest that they will see greater acceptance of those initiatives among younger buyers and that the response will vary by country. Overall there is good awareness of the need to conserve energy, and willingness on the part of consumers to do their bit. At the point of purchase they will often be influenced by other factors, but vendors that can boast genuine green credentials should expect to see a positive response from many customers."
About the Consumer Mobility Analysis service
The research results discussed in this release come from the new Canalys Consumer Mobility Analysis EMEA service, which provides clients with analysis of results from a continuous programme of large-scale consumer surveys. Each quarter, different mobility topics and country markets within EMEA are covered by the survey to provide monitoring of trends and reaction to new initiatives, products and services. In addition to analysis of the survey results, clients receive regular reports, supporting databases and direct access to Canalys analysts.
Analyst photos
Photographs of the analyst(s) quoted in this release are available in the biography section and may be re-used by the press to accompany a relevant article.
Canalys specialises in delivering high quality market data, analysis and advice to the world's leading technology vendors. It is recognised as a key provider of continuous advisory services and confidential custom projects for marketing managers and strategists within blue-chip IT, telecoms, navigation and consumer electronics companies. It has unrivalled expertise in European routes to market for all kinds of high technology products and services in the consumer, SMB and large enterprise segments, and provides worldwide market data and trends analysis.
To receive future Canalys research releases directly, please e-mail a request with your details to press@canalys.comor complete this form specifying the technology market topics in which you are most interested.
Consumers warm to the touch screen phone


At the Amman Chamber of Industry (ACI) an information session about best practices on Dual Studies at GJU and in Germany took place on the 26th of November 2018. Invited and organized by the Dual Studies Advisors of the Office for Industrial Links at GJU and the ACI, a deeply involved group of company representatives and GJU academic staff joined the impressing presentation and discussion ran by the German expert Prof. Dr. Edda Pulst.
Since the Dual Study track in Logistics at GJU has been officially kicked-off by the President of the University Prof. Manar Fayyad last month, a growing number of company representatives and GJU Professors care about the quality of a powerful and sustainable implementation process. Therefore, Prof. Edda Pulst - who also runs the successful adapt2job-2win training at GJU - gave an overview and detailed information about the challenges and opportunities for companies in Germany, which are cooperating with universities in such Dual Study Programs and about her personal experiences within such programs as well.
After a kind welcome by Britta Kaehler, the Director of the Office for Industrial Links at GJU, Eng. Fadel Labadi (Manager of Industrial Development Department/ACI) and Dr. Wolfgang Frohberg (MOVE-HET/GIZ), Ms. Simone Strasburger shared the status quo of the development and implementation process of Dual Studies at GJU with the audience. `Where are we now?´ and `Next steps!´ have been the headlines of her short presentation.
This was a direct link to the explanations provided by Prof. Dr. Edda Pulst: `It's always 2! ´, she emphasized the importance of a professional guidance of students while they are in the companies. Showing best practice examples from companies like Henkel, SMS Group, Ferrostaal and DHL, she emphasised on the fact that there must be responsible and trained contact persons within the companies who care about the needs of the students and who monitor and support the students' integration into the company and the work processes as well. And `It's always 2!´ also means professional in-company trainers who are able to teach the students the skills required at specific workplaces. And `It's always 2!´ as well describes the huge impact when company experts and university professors run courses and lectures together (team teaching).
An in-depth conversation between Prof. Dr. Edda Pulst and the company representatives on basis of business-driven issues on the one side and academic quality standards on the other side finalized the session. All participants mentioned their satisfaction with the inspiring exchange of ideas.
On that basis the Dual Studies Office will start to provide modularized in-company instructor trainings for partner companies starting spring 2019.


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Courtesy of LMU
LMU health profession students learn to identify and address control drug misuse during a symposium held recently.
LMU symposium addresses opioid epidemic
Published 6:28 am Wednesday, October 25, 2017
HARROGATE, Tenn. — Students across health profession programs gathered at Lincoln Memorial University-DeBusk College of Osteopathic Medicine (LMU-DCOM) on Sept. 29, for an all-day symposium titled, "Addressing the Opioid Epidemic: Control Drug Misuse."
The symposium was designed to prepare LMU health profession students who will soon enter clinical training and future residencies where they will encounter patients exhibiting drug seeking behavior and patients addicted to these medications.
The event featured John Temple, author of American Pain. In his book, Temple tells the story of how the current opioid epidemic began with two felons who were twin brothers that made millions from a gigantic south Florida pain clinic that sold opioids to addicts.
"Whenever I get a chance to talk to medical students I feel like I am really making an impact because if you develop your own ideas and ways of handling this, it's really going to be up to you to figure out where to go next," Temple said.
The symposium was required for LMU's second-year osteopathic medical students, first-year physician assistant students and nurse practitioner students. It was also attended by several social work students, and was open to additional classes via live stream. The objectives of the symposium were to introduce students to the widespread control drug misuse problem in the local and national regions, help students recognize misuse in patients and self, help students recognize substance use disorders as a chronic disease and identify best practices for preventing control drug misuse and non-prescription drug treatment options.
The afternoon session focused on addressing the problem from an inter-professional perspective. Sharing the osteopathic physician perspective was Walter Ehrenfeuchter, DO, professor and director of neuromusculoskeletal medicine and osteopathic manipulative medicine (NMM/OMM) at the Philadelphia College of Osteopathic Medicine.
"Opiates are a double edged sword. We need them for acute care, emergency and post-op; but they are a curse because of the potential for addiction, but we have no other alternatives," Ehrenfeuchter said. "Pills are easy. Osteopathic manipulation, acupuncture, exercise therapy, physical therapy, are all more time-consuming, more expensive, and a bigger commitment for the physician and the patient."
Certified NMM/OMM physicians like Ehrenfeuchter, are well versed in many forms of Osteopathic Manipulative Treatment (OMT) where the hands are used to diagnose, treat, and prevent illness or injury. The treatment can be used to ease pain, promote healing, and increase overall mobility. When appropriate, OMT can complement, and even replace, drugs or surgery.
Ehrenfeuchter said, "We are seeking alternatives that cause the least harm and have the most potential for good, and I personally think osteopathic manipulative medicine fits into that category."
CDC data shows opioid abuse is more prevalent in southern states. Tennessee is ranked among the top three states in the nation with the highest painkiller prescription rate. In Tennessee, from 2014-2015, there was a 12.8 percent increase in drug overdose deaths from opioids. In 2014, 1,263 people in Tennessee died from overdose (the majority of them from opioids) and Tennessee recorded 1,031 cases of infants born with neonatal substance abuse caused by in utero exposure to opioids; twice the national average.
Stacey Maltman, FNP-BC, talked about treatment from the nurse practitioner's perspective. Maltmann, who earned two nursing degrees at LMU, an Associate of Science in Nursing and Master of Science in Nursing at LMU, is an active member of the National Association for Alcoholism and Drug Abuse Counselors (NAADAC).
Other speakers included Dr. Kenneth Trzil, assistant professor of internal medicine at LMU-DCOM and Richard Gillespie, MD, a member of the LMU Board of Trustees. Sharing the physician assistant perspective was Natalie Wilbanks, PA-C, a family practice provider at Dayspring Family Health Centers in Jellico, Tennessee.
For many years, the opioid crisis has received too little attention from the medical community. Because of the rapid rise in the use of these medications in rural Appalachia, the medical community is now paying significant attention to the problem.
Joseph Bardinelli, DO, an LMU-DCOM alumnus from the class of 2016 shared his perspective from working as a resident. He said, "If you can go into your rotations knowing this is the epidemic of your career it will put you way ahead of your colleagues."
Kentucky officer fatally shoots woman after checking on man SHELBYVILLE, Ky. (AP) — A Kentucky police officer has fatally shot... read more

redpajama_set_name: RedPajamaCommonCrawl


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Rufin VanRullen
Centre National de la Recherche Scientifique (CNRS), France
Andrea Alamia
UMR5549 Centre de Recherche Cerveau et Cognition (CerCo), France
Bill Lotter
Harvard University, United States
Supplemental Movies
Front. Psychol., 15 March 2018
Sec. Perception Science
Volume 9 - 2018 | https://doi.org/10.3389/fpsyg.2018.00345
Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction
Eiji Watanabe1,2*, Akiyoshi Kitaoka3, Kiwako Sakamoto4,5, Masaki Yasugi1 and Kenta Tanaka6
1Laboratory of Neurophysiology, National Institute for Basic Biology, Okazaki, Japan
2Department of Basic Biology, The Graduate University for Advanced Studies (SOKENDAI), Miura, Japan
3Department of Psychology, Ritsumeikan University, Kyoto, Japan
4Department of Physiological Sciences, The Graduate University for Advanced Studies (SOKENDAI), Miura, Japan
5Division of Integrative Physiology, National Institute for Physiological Sciences (NIPS), Okazaki, Japan
6Sakura Research Office, Wako, Japan
The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.
Deep neural networks (DNNs), which have been developed with reference to the network structures and the operational algorithms of the brain, have achieved notable success in a broad range of fields (LeCun et al., 2015; Schmidhuber, 2015), including computer vision, in which they have produced results comparable to and in some cases superior to human experts (He et al., 2015; Silver et al., 2017). When development began, the most successful DNNs for computer vision relied on supervised learning from large sets of labeled training images. However, the human brain, particularly the cerebral cortex, learns the world at least partially in an unsupervised manner (otherwise known as self-supervised learning, Hinton et al., 1995; Bengio, 2013). These theories based on unsupervised learning suggested that the brain distills the spatiotemporal structure of objects from visual information that is constantly obtained in real time and predicts the future positions and figures of the moving objects. It is assumed that the predictive ability of sensory perception interpolates inevitable neural delay and that human behaviors are adapted to the world that is progressing in real time (Nijhawan, 2008; Heeger, 2017).
DNNs functioning in an unsupervised learning manner similar to the cerebral cortex have been gradually developed. Using auto-encoder networks or generative adversarial networks incorporating recurrent memory cells (long-short-term-memory, LSTM), it is becoming possible to predict the future state of an object in moving images (Mathieu et al., 2015; Srivastava et al., 2015; Lotter et al., 2016; Vondrick et al., 2016; Villegas et al., 2017a,b), although only several video frames ahead. A DNN called PredNet (Lotter et al., 2016) has been intrinsically designed according to the predictive coding theory (Rao and Ballard, 1999; Friston and Kiebel, 2009; Shipp, 2016), which is one of the most influential hypotheses that can comprehensively explain the information-processing mechanism of the visual system of the cerebral cortex (see the discussion section). PredNet learns to predict future frames in a video sequence, with each layer in the network making local predictions using backward information from upper layers and forwarding only the difference values from those predictions to subsequent upper network layers (Figure 1). This artificial network is essentially comparable to the theory of brain's visual processing of predictive coding, in which backward connections from higher- to lower-order visual cortical areas carry predictive information, whereas the forward connections carry the difference values between the predictions and actual lower-level activities. It is hypothesized that the brain endeavors is to minimize such difference values (Rao and Ballard, 1999; Friston and Kiebel, 2009; Shipp, 2016).
Figure 1. A schematic diagram of PredNet (a modification of Figure 1 in Lotter et al., 2016). Illustration of information flow within a single layer is presented. Vertical arrows represent connections with other layers. Each layer consists of "Representation" neurons, which output a layer-specific "Prediction" at each time step, which is subtracted from "Target" to produce an error, which is then propagated laterally and vertically in the network. External data or a lower-layer error signal is input to "Target." In each layer, the input information is not processed directly, and the prediction error signal is processed.
While human visual prediction is extremely accurate, "mistakes" are occasionally made, such as in the case of visual illusions. For example, motion illusion is one of the visual illusions in which we perceive motion that is different from that of the physical stimulus. In the most prominent case represented by the rotating snake illusion (Figure 2), the perception of motion arises from a completely static image (Kitaoka and Ashida, 2003; Conway et al., 2005). Despite being a still image, the rotating snake illusion induces strong perception motion in humans, cats (Bååth et al., 2014), and even fish (Gori et al., 2014). Concerning this notable static stimulus, neurological and psychological studies suggest that both ocular motion and the information processing of the cerebral cortex are responsible for the perception of illusory motion (Hisakata and Murakami, 2008; Kuriki et al., 2008; Ashida et al., 2012). Predictive coding theory has been suggested to be a theoretical mechanism to generate illusions (Notredame et al., 2014; Nour and Nour, 2015; Raman and Sarkar, 2016; Shipp, 2016), including several motion illusions (Watanabe et al., 2010; Edwards et al., 2017). If the predictive coding theory explains these illusions, one might except that PredNet would also include "mistakes" similar to those associated with visual illusions for human.
Figure 2. Akiyoshi Kitaoka's rotating snake illusions (the left 2 panels). In the case of the single-ring version, most people perceive a counter-clock wise motion. In the case of the four-ring version, people perceive clockwise or counter-clockwise motion depending on color alignment. Negative controls (non-illusions) for which people perceive no motion are presented in the right 2 panels. The resolution of all images is width 160 and height 120 pixels. For prediction, the same consecutive 20 images were input to the trained networks. To experience stronger illusory motion perception, please refer to "Akiyoshi's illusion pages," http://www.ritsumei.ac.jp/~akitaoka/index-e.html.
It is worth considering whether indicators such as visual illusions can be reproduced in DNNs as models of the brain. The visual illusions that have been used to analyse the mechanism of visual processing in ordinary brains and to study psychiatric disorders (Gori et al., 2016) may contribute to the study of DNNs as models of the brain. As another viewpoint, DNN technologies are now being applied in the real world. To understand the risks of DNNs, it is therefore critical to know whether DNNs would be misled by the same visual illusions as humans. The visual illusion reflects the constraints operating in the visual system to support the valid formation of visual representations of our external environment and may be a legitimate adaptation to our living environment (Eagleman, 2001); however, such misperception could constitute a fatal mistake, depending on the application of DNNs.
For both purposes, videos of self-motion of viewers were input to PredNet to allow it to learn the spatiotemporal structure of the world via unsupervised learning. We then investigated whether the trained networks could predict a simple rotational motion of a propeller. Next, the rotating snake illusion, which PredNet had not experienced, was input to the trained networks, and we examined whether the prediction image contained an illusionary motion element.
Learning Videos and Generating Predicted Images by DNN
PredNet (Lotter et al., 2016), written in Keras (Chollet, 2015), was ported to Chainer (Tokui et al., 2015), and reconstructed for convenience (https://doi.org/10.6084/m9.figshare.5483710). According to previous results (Lotter et al., 2016) of a random hyperparameter search for learning of a natural image sequence, a four-layer model with 3 × 3 filter sizes for all convolutions and stack sizes per layer of 3, 48, 96, and 192 was adopted. Model weights were optimized using an Adam algorithm (Kingma and Ba, 2014) with default parameters. Models were trained with mean-squared error using videos from the First-Person Social Interactions Dataset (Fathi et al., 2012), which contains day-long videos of eight subjects spending their day at Disney World Resort in Orlando, Florida. The cameras were mounted on a cap worn by the subjects. Eight randomly selected videos from five subjects (Alin 1 and 2, Denis 1, Hussein 1, Michael 2 and 3, and Munehiko 2 and 3) were down-sampled and formatted to MP4 (width of 160 × height of 120 pixels, 30 fps, https://doi.org/10.6084/m9.figshare.5483668.v1) and were reformatted to serial still JPEG images (160 × 120 pixels). In total, the training set consisted of ~530 K images. Each image set derived from eight videos was sequentially input to the network. Model was updated every 20 consecutive images (batch) by backpropagation using the last prediction error of batch. In an experiment (Figure 5), mirrored (horizontally inversed) images were used for training. Training lasted ~10 h on a GPU (GTX-1080, NVIDIA). For the PredNet predictions, there were six testing stimuli: cw rotating propeller, ccw rotating propeller, static propeller, mirrored cw rotating propeller (becomes ccw), mirrored cw rotating propeller (becomes ccw), and rotating snake illusion. In each case, the stimuli were made as 20 frame sequences and the first three PredNet predictions were defined as P1, P2, and P3. The trained network predicted the 21st image (P1) with reference to 20 consecutive images (T1 to T20). Next, it predicted the 22nd image (P2) with reference to 21 consecutive images (T1 to T21) using P1 as the image of T21. The same was the case with P3. Test images (JPEG format, 160 × 120 pixels) were down-sampled from videos of rotating propellers (Figure 3, clockwise and counter-clockwise at 15 rpm, 1280 × 720 pixels, 30 fps) and sampled from the rotating snake illusions (160 × 120 pixels)(https://doi.org/10.6084/m9.figshare.5483680.v1). The program for prediction is incorporated in the above Chainer program for training. At the time step after inputting 20 image sequences of the above-mentioned test stimuli, the activation patterns of the hidden units of PredNet layers were visualized. Visualization was performed using a self-made converter program (https://doi.org/10.6084/m9.figshare.5483710.v1) and TensorBoard in TensorFlow (Version 1.2.1) (Abadi et al., 2016). As a reference for the discussion, we have provided the activation patterns of two particular units out of 181 hidden units (Figures 11, 12).
Figure 3. Rotating propeller. For prediction, a series of 20 consecutive images was input to the trained networks. The first 5 consecutive images are presented here. Resolution of all figures is width 160 and height 120 pixels. The original videos are available at https://doi.org/10.6084/m9.figshare.5483680.v1.
Optical Flow Analysis
Motion vectors observed between two consecutive predicted images (P1/P2 or P2/P3) were measured through optical flow analysis, in which optical flow vectors were calculated by the Lucas-Kanade method (Lucas and Kanade, 1981) using a customized Python program (https://doi.org/10.6084/m9.figshare.5483716.v1, window size 50, quality level 0.3). The rotation center of the propeller was calculated from the motion picture of the propeller by the ImageJ Java program (Schneider et al., 2012). The provisional rotation center of the rotating snake illusion was taken as the center of the ring. The coordinates of the calculated optical flow vectors were transformed into the coordinate system of the original videos or the still images, and the angular velocity was then calculated from the transformed coordinates and the provisional rotation center. The angular velocities calculated from optical flow vectors were averaged for each consecutive pair of the predicted images. A positive sign was assigned for cw rotation. To analyse the differences based on the type of input images, values from 400 to 1000K (number of images) trained networks were averaged (Figure 6), since the illusory motion predicted by the trained networks was observed over 400 K in trained networks (Figure 8). Significant differences between the mean values were analyzed using unpaired t-tests (one-tailed Welch's t-test or Student's t-test).
First, to determine whether the trained networks of PredNet are capable of predicting the real motion of an object, videos of propellers rotating at 15 rpm (Figure 3, clockwise [cw] and counter-clockwise [ccw]) and 0 rpm (Figure 3) were input to the trained networks and used to produce three consecutive predicted images (P1, P2, and P3). Using optical flow analysis, the motion vectors generated between two consecutive predicted images (P1/P2, P2/P3) were quantified, and the angular velocity with the center of propeller rotation was calculated. Figure 4A presents the magnitude of the detected angular velocity of P1/P2. The network models trained with more than 40 K training images predicted rotational motion for both cw and ccw direction, and the direction of the angular velocity corresponding to the direction of propeller rotation was extracted (Supplementary Movies). The predicted rotation motion of P1/P2 was significantly stronger in the ccw direction than in the cw direction (Figure 5B, p < 0.01, t = 4.95, degrees of freedom = 12, comparing average absolute values). Using mirrored images of the rotating propellers, a similar experiment was conducted (Figure 4B). Results similar to those of the experiment using the original image were obtained, but the predicted rotation motion of P1/P2 was significantly stronger in the cw direction than in the ccw direction (Figure 5B, p < 0.01, t = 2.82, degrees of freedom = 12, comparing average absolute values). In experiments wherein training was performed with a mirror image set (Figure 5A), the pattern of asymmetry did not change (Figure 5B, p < 0.01, t = 5.17, degrees of freedom = 12, comparing average absolute values). The propeller had a subtle asymmetrical shape along the direction of rotation. Therefore, the trained DNNs appeared to respond sensitively to the subtle differences. The magnitude of the optical flow vectors close to the rotation center was smaller than that at the periphery, which reflects the relative moving distance observed in each part of the propeller in the video (Figure 7). The rotational motion also existed in P2/P3 but became extremely small (Figure 6). In the cases of P2/P3, a significant difference was detected only between ccw and 0 rpm (p < 0.01, t = −2.951, degree of freedom = 12) and not between cw and 0 rpm (p = 0.156, t = 1.088, degrees of freedom = 12). The smaller effect observed in P2/P3 is simply due to the fact that P2/P3 started predicting from a frame that was temporally latter than P1/P2. In the original description of PredNet (Lotter et al., 2016), the test videos used for prediction were the same type of videos used for learning (e.g., car-cam video vs. car-cam video). In this study, ~5 h of videos of viewer self-motion were input to PredNet for learning without supervision of the rotating propeller. PredNet was still capable of predicting not only the shape and colors of the propellers but also the direction of propeller rotation, indicating that PredNet successfully generalized the spatiotemporal structure of moving objects as predictive information within the network.
Figure 4. Predicted motion found in the rotating propellers. (A) Optical flow vectors detected between a pair of the first/second consecutive predictive images (P1/P2) of the rotating propellers (15 rpm). (B) Mirrored images of the rotating propellers were used as test stimuli. In other words, the cw of A and the ccw of B are derived from the same video, and the ccw of (A) and the cw of (B) are derived from the same video. Six optical flow vectors were detected in each pair of predicted images. Mean angular velocity was calculated from the optical flow vectors. A positive sign was assigned to clockwise rotation. The results of the clockwise propeller are presented in red, and the results of the counter-clockwise propeller are presented in blue. The rotation rate of 15 rpm is approximately comparable to 5.23 rad (× 0.01). Error bars indicate standard errors, and the slanted zero horizontal lines appear to a kind of café wall illusion.
Figure 5. Models trained with mirrored videos also predicted motion in the rotating propellers. (A) The DNNs were trained using mirrored images of the videos of self-motion of viewers. Predicted angular velocities of propellers were calculated as in Figure 4A. (B) Mean angular velocities (P1/P2) derived from 400 to 1000 K trained networks were averaged (n = 7) and set to absolute values. Error bars indicate standard errors. Asterisks indicate a significant difference (p < 0.01).
Figure 6. Predicted motion detected in the rotating propellers and the illusions. Mean angular velocities derived from 400 to 1000 K trained networks were averaged (n = 7). Error bars indicate standard errors. Asterisks indicate a significant difference (p < 0.01).
Figure 7. Optical flow vectors detected in the rotating propellers. The vectors were obtained between a pair of the first/second consecutive predictive images (P1/P2) of the rotating propellers. Red bars denote the direction and magnitude of vectors, yellow dots denote the start points of the vectors. To aid visualization, the magnitude of the vectors was amplified 30 times. The vectors were written over the P2 images (width 160, height 120 pixels). A network trained with 500 K video frames was used for prediction.
For the next predictive trained networks generated, 20 serial pictures (repeating files of the same still images) of the rotating snake illusion were input into the trained networks with output prediction for three consecutive images. The optical flow analysis revealed that rotational motion was detected on the images generated by the trained networks when ~400 K video frames or more were learned (Figures 8, 9, and Supplementary Movies). To generate rotational motion for pictures of an illusion, longer training times were required than for the rotation prediction of the propeller. The results suggested that the performance of hidden units involved in the reproduction of the illusory motion continued to be refined during training with 40 to 300 K images. It is an interesting question whether hidden units involved in the illusory motion have emerged as refinements of hidden units related to propeller rotation, or whether they emerged independently as different hidden units from propeller rotation. It will be useful to analyse the time series change of the characteristics of the hidden units.
Figure 8. Predicted motion found in the illusion. Optical flow vectors detected between a pair of consecutive predictive images (P1/P2) of the illusion. The mean angular velocity was calculated from the optical flow vectors. A positive sign was assigned to clockwise rotation. One hundred optical flow vectors were detected in each pair of images. Error bars indicate standard errors. Although the reason was unknown, unidirectional optical flows were observed in a wide area of the predicted images from 100 to 300 K. The magnitude of optical flows was biased by location. The apparent positive angular velocities from 100 to 300 K appeared to be caused by the bias.
Figure 9. Optical flow vectors detected in the illusion. Optical flow vectors detected between a pair of consecutive predictive images (P1/P2 or P2/P3) of the illusion. The magnitude of the vectors was amplified 60 times. The left is a single ring of the rotating snake illusion, and the right is a negative control image. The vectors were imposed over the P2 or P3 images or black background (width 160, height 120 pixels). A network trained with 500 K video frames was used for prediction.
The direction of the rotational motion predicted for the illusion was in agreement with the rotation direction of illusory motion perceived by humans (Figures 8, 9). When negative control static images (Figures 8, 9) containing the same shape and color sequence (blue-black-yellow-white or white-yellow-black-blue) as the rotating snake illusion for which humans do not perceive illusory motion were input to the trained networks, rotational motions other than small optical flows were not predicted for the negative controls (Figures 8, 9). When the rotating snake illusion including four illusionary rings in a single image was input to the trained networks, rotating motion was detected in all four rings in the predicted images in the cases of P1/P2 and P2/P3 (Figure 10). The cw and ccw rotating motion were detected simultaneously in the single image, which coincided again with the rotation direction perceived by humans. In the case of four rings experiment (Figure 10), the detected optical flows were concentrated to peripheral region of a ring as compared with the one ring experiment (Figure 9). Regardless of whether it was an image of one ring or an image of four rings, a single image was composed of 160 width × 120 height pixels. In other words, in the case of the four rings, one ring was composed of only 40 width x 30 height pixels. The central region of a ring of the rotating snake illusion was drawn with smaller-sized basic elements than the peripheral region. Therefore, we assumed that the generation of the predicted illusory motion depends on the size of the basic elements. In the case of the four rings in the negative control still images, only a small optical flow that differed from rotation was detected (Figure 10).
Figure 10. Predicted motion in two rotation directions in the illusion. Optical flow vectors detected between consecutive predictive images (P2/P3) of the illusion (Left). Right panels are the non-illusion control. The vectors were imposed over the P3 image or black background. The magnitude of the vectors was amplified 180 times. A significantly larger value (0.081 ± 0.0017) was observed in the illusion (p < 0.01, t = 37.53, degree of freedom = 127) than in the non-illusion control (mean magnitude of optical flow vectors, 0.013 ± 0.00064 pixels). The mean of magnitudes (0.080 ± 0.0025) of the vectors detected by the counter clockwise rings of the illusion was not significantly different from the value (0.081 ± 0.0023) of the clockwise rings (p = 0.390, t = −0.281, degree of freedom = 98).
The DNN that predicted the motion vectors of the unlearned rotating propeller also predicted the rotational motion in the rotating snake illusion, in manner similar to human perception. Previous fMRI studies demonstrated that the rotating snake illusion activated V1 to MT areas of the cerebral cortex (Kuriki et al., 2008; Ashida et al., 2012), which are commonly activated when detecting actual moving objects and global movement in the background. In order to compare the current results to those based on knowledge of the physiology of the human brain, the DNNs were oversimplified, but it is noteworthy that the DNNs based on a theory of the cerebral cortex reproduced illusory motion in a manner similar to human perception. However, although the current experimental results were notable, they did not accurately reproduce the illusory motion perceived by humans. For instance, since illusory rotation of a ring wherein human direct attention is not induced, four simultaneous rotations of the rings presented in Figure 10 are extremely unlikely to occur in a similar manner as human perception (Hisakata and Murakami, 2008). Moreover, the speed of the rotational motion perceived in each ring varies from moment to moment. It may be better to think of PredNet as an abstractive reproduction of a small part of the biological aspects of visual information processing.
In order to verify these phenomena by DNNs, it is at least necessary to reproduce the functional division of the central visual field with a high resolution and the peripheral visual field with a low resolution. Attempts have also been made to incorporate the mechanism that distinguishes between a central visual field and a peripheral visual field into DNNs (Wang and Cottrell, 2017). An application of the DNN will be interesting as a future subject. As mentioned in the introduction section, various methods have been devised for deep learning machines that conduct video prediction (Mathieu et al., 2015; Srivastava et al., 2015; Lotter et al., 2016; Vondrick et al., 2016; Villegas et al., 2017a,b), including CNN-LSTM Encoder-Decoder used as a control in the original paper of PredNet (Lotter et al., 2016). By analyzing the DNNs other than PredNet, we will be able to deepen understanding of the mechanism by which the illusory motion is predicted.
Nevertheless, it is remarkable that the DNN "perceives" the parameters of the physical world as distorted. The information processing of the brain generates illusions not only on visual motions but also on many other parameters, such as the brightness, positions, shapes, colors, sizes, and so on. Determining that the DNN experiences these illusions requires additional investigation, but to use DNN technology in the real world introduces the possibility that these illusions could create errors associated with substantial risks. Users must be aware of this possibility.
The cerebral cortex related to vision is divided into multiple functional areas. Connection between these areas is not a one-way neural network from V1, which is close to the input source of the senses to higher-order areas, but is a reciprocal neural network that allows information to flow in both directions (Friston, 2005; Muckli and Petro, 2013). Based on the anatomical and physiological knowledge derived from primates, a basic theory of predictive coding was proposed from the viewpoint of computation theory (Kawato et al., 1993). The theory postulated that the higher-order regions of the cortex encoding the inverse model of the visual world transmit the prediction signal toward the lower-order regions and that each region detects the prediction error in reference to the sensory signals derived from the input source. Later, this theory was widely applied in the field of neuroscience (Rao and Ballard, 1999; Friston and Kiebel, 2009) and has been also developed as "free energy theory," which is based on a variational Bayesian method (Friston and Kiebel, 2009; Bogacz, 2017). The concept can be applied to more than just the cerebral cortex. For example, it has been hypothesized that the cerebellum performs predictive learning in association with the cerebral cortex (cerebro-cerebellar loops) and seems to contribute not only to sensory motor control but also to attention, language, social cognition, and other functions (Butcher et al., 2017; Sokolov et al., 2017). Although it may not be strictly predictive coding, the dopamine signal originating from the midbrain is thought to code "prediction errors of reward learning," and a similar learning algorithm to that of the cortex seems to be used even if the time from the start of the behavior to the result is relatively long (Schultz, 1998; Schultz and Dickinson, 2000; Keiflin and Janak, 2015; Diederen et al., 2017; Nasser et al., 2017). Therefore, although PredNet was made for engineering purposes, it is expected to become one of the key tools in the future for studying the operational principles of the brain. The predictive coding theory was also expected to be a theoretical mechanism to generate illusions (Notredame et al., 2014; Nour and Nour, 2015; Raman and Sarkar, 2016; Shipp, 2016) and was used as an explanation for certain motion illusions (Watanabe et al., 2010), of bi-stable perception (Weilnhammer et al., 2017) and of an apparent motion illusion (Edwards et al., 2017). The present result reveals the exciting idea that the predictive coding theory accounts for a wide range of visual illusions.
Since the learning methods of DNNs are visualized as mathematical formulas and program codes and the resulting trained networks can be observed (Figures 11, 12, for example), DNNs can be expected to be powerful tools for verifying the theories and hypotheses proposed in the research fields of neuroscience and psychology (Kriegeskorte, 2015; Cichy et al., 2016; Marblestone et al., 2016; VanRullen, 2017). Detailed comparisons between the biological brain and DNNs with the supervised learning method have been performed. In the context of object recognition, stimulus representations developed by the DNNs have been shown to account for neural signals in primates inferior temporal cortex and in fMRI recording data from the human ventral stream (Cadieu et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014; Güçlü and van Gerven, 2015). Several DNNs were used as computational models for human shape sensitivity; the output layers of the DNNs successfully developed representations that closely related human shape judgement (Kubilius et al., 2016). These reports suggested that some fundamental processes that are shared across different hardware have been captured by DNNs. Nevertheless, DNNs and the brain are considerably different. The hardware components are completely different, and computational algorithms that are not found in the brain, such as back-propagation, are used for DNNs. DNNs manufactured for engineering purposes tend to advance their own evolution rather than remaining close to the biological brain. Only by focusing on physiological knowledge will it be possible to keep the DNN technology in the research area of the biological brain. The present research result suggests that neuroscientists and psychologists should not underestimate the value of the method (VanRullen, 2017) to call "Reverse Psychology." Various types of sensory illusions represented by visual illusions, at least, could be the lodestar that supports the validity of DNNs as a tool for studies of the brain.
Figure 11. Activation patterns of the hidden units of DNNs can be visualized (a lower layer). Samples of the activation patterns of receptive fields in a lower convolution LSTM layer are presented (R-Layer0/ConvLSTM/Variable_1_3_120_160_float32_29, refer to image data numbers of TensorBoard). A network trained with 500K video frames was used for prediction. Twenty consecutive images were input to the trained networks, and visualization was then performed using TensorBoard. In this layer, there are three channels with a 120 (width) × 160 (height) pixel image. In the cw illusion, horizontal inversion pictures of the single-ring illusion (ccw) presented in Figure 2 were used as input pictures. Note that noisy random patterns are specifically observed in "Non-Illusion".
Figure 12. Activation patterns of the hidden units of DNNs can be visualized (an upper layer). Samples of the activation patterns of receptive fields in an upper convolution LSTM layer are presented (R-Layer1/ConvLSTM/Variable_1_48_60_80_float32_30, refer to image data numbers of TensorBoard). A network trained with 500 K video frames was used for prediction. Twenty consecutive images were input to the trained networks, and visualization was performed by TensorBoard. In this layer, there are 48 channels with a 60 (width) × 80 (height) pixel image. Note that noisy random patterns are specifically observed in "Non-Illusion," and that a different pattern appears between the cw and ccw groups.
EW conceived the research and designed the project. AK provided the illusory illustrations and inspired the project members. KT wrote all Python programs. EW and KS collected videos, ran programs, and measured the optical flow data. EW and MY performed data analysis. EW wrote the manuscript. All authors discussed the results and commented on the manuscript.
This work was supported in part by MEXT/JSPS KAKENHI Grant-in-Aid for Scientific Research (C) (grant numbers 26430028, 26861565, and 15K18574).
KT was employed by company Sakura Research Office.
The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We are grateful to Mr. Toshiyuki Sazi for his technical assistance in preparing the propeller rotating device, to Dr. Nozomi Nishiumi for the helpful comments on this manuscript, and to Ms. Mie Watanabe for her secretarial assistance in preparing the manuscript.
A pair of consecutive predictive images was combined in AVI movie format. A network trained with 500K video frames was used for prediction.
Propeller_L.avi: Predictive images of the ccw-rotating propeller.
Propeller_R.avi: Predictive images of the cw-rotating propeller.
Propeller_Z.avi: Predictive images of the motionless-rotating propeller.
Illusion_single.avi: Predictive images of the four rings non-illusion.
Non_illusion_four.avi: Predictive images of the single ring illusion.
Illusion_four.avi: Predictive images of the four rings illusion.
Non_illusion_single.avi: Predictive images of the single ring non-illusion.
The movies were uploaded at https://doi.org/10.6084/m9.figshare.5483689.v1.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., et al. (2016). TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv 1603.04467.
Ashida, H., Kuriki, I., Murakami, I., Hisakata, R., and Kitaoka, A. (2012). Direction-specific fMRI adaptation reveals the visual cortical network underlying the "Rotating Snakes" illusion. Neuroimage 61, 1143–1152. doi: 10.1016/j.neuroimage.2012.03.033
Bååth, R., Seno, T., and Kitaoka, A. (2014). Cats and illusory motion, Psychology 5, 1131–1134. doi: 10.4236/psych.2014.59125
Bengio, Y. (2013). "Unsupervised and Transfer Learning: Challenges in Machine Learning," in JMLR: Workshop and Conference Proceedings, eds I. Guyon, G. Dror, and V. Lemaire (Brookline, MA: Microtome Publishing).
Bogacz, R. (2017). A tutorial on the free-energy framework for modelling perception and learning. J. Math. Psychol. 76(Pt B), 198–211. doi: 10.1016/j.jmp.2015.11.003
Butcher, P. A., Ivry, R. B., Kuo, S. H., Rydz, D., Krakauer, J. W., and Taylor, J. A. (2017). The cerebellum does more than sensory prediction error-based learning in sensorimotor adaptation tasks. J. Neurophysiol. 118, 1622–1636. doi: 10.1152/jn.00451.2017
Cadieu, C. F., Hong, H., Yamins, D. L., Pinto, N., Ardila, D., Solomon, E. A., et al. (2014). Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS Comput. Biol. 10:e1003963. doi: 10.1371/journal.pcbi.1003963
Chollet, F. (2015). Keras. GitHub. Available online at: https://github.com/fchollet/keras.
Cichy, M. R., Khosla, A., Pantazis, D., Torralba, A., and Oliva, A. (2016). Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Sci. Rep. 6:27755. doi: 10.1038/srep27755
Conway, B. R., Kitaoka, A., Yazdanbakhsh, A., Pack, C. C., and Livingstone, M. S. (2005). Neural basis for a powerful static motion illusion. J. Neurosci. 25, 5651–5656. doi: 10.1523/JNEUROSCI.1084-05.2005
Diederen, K. M., Ziauddeen, H., Vestergaard, M. D., Spencer, T., Schultz, W., and Fletcher, P. C. (2017). Dopamine modulates adaptive prediction error coding in the human midbrain and striatum. J. Neurosci. 37, 1708–1720. doi: 10.1523/JNEUROSCI.1979-16.2016
Eagleman, D. M. (2001). Visual illusions and neurobiology. Nat. Rev. Neurosci. 2, 920–926. doi: 10.1038/35104092
Edwards, G., Vetter, P., McGruer, F., Petro, L. S., and Muckli, L. (2017). Predictive feedback to V1 dynamically updates with sensory input. Sci. Rep. 7, 16538. doi: 10.1038/s41598-017-16093-y
Fathi, A., Hodgins, L. K., and Rehg, J. M. (2012). "Social interactions: A first-person perspective," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Providence).
Friston, K. (2005). A theory of cortical responses. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 815–836. doi: 10.1098/rstb.2005.1622
Friston, K., and Kiebel, S. (2009). Predictive coding under the free-energy principle. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 1211–1221. doi: 10.1098/rstb.2008.0300
Gori, S., Agrillo, C., Dadda, M., and Bisazza, A. (2014). Do fish perceive illusory motion? Sci. Rep. 4:6443. doi: 10.1038/srep06443
Gori, S., Molteni, M., and Facoetti, A. (2016). Visual illusions: an interesting tool to investigate developmental dyslexia and autism spectrum disorder. Front. Hum. Neurosci. 10:175. doi: 10.3389/fnhum.2016.00175
Güçlü, U., and van Gerven, M. A. (2015). Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci. 35, 10005–10014. doi: 10.1523/JNEUROSCI.5023-14
He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving deep into rectifiers: surpassing human-level performance on imagenet classification. arXiv 1502.01852.
Heeger, D. J. (2017). Theory of cortical function. Proc. Natl. Acad. Sci. U.S.A. 114, 1773–1782. doi: 10.1073/pnas.1619788114
Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M. (1995). The "wake-sleep" algorithm for unsupervised neural networks. Science 268, 1158–1161. doi: 10.1126/science.7761831
Hisakata, R., and Murakami, I. (2008). The effects of eccentricity and retinal illuminance on the illusory motion seen in a stationary luminance gradient. Vision Res. 48, 1940–1948. doi: 10.1016/j.visres.2008.06.015
Kawato, M., Hayakawa, H., and Inui, T. (1993). A forward-inverse optics model of reciprocal connections between visual cortical areas. Network 4, 415–422. doi: 10.1088/0954-898X_4_4_001
Keiflin, R., and Janak, P. H. (2015). Dopamine prediction errors in reward learning and addiction: from theory to neural circuitry. Neuron 88, 247–263. doi: 10.1016/j.neuron.2015.08.037
Khaligh-Razavi, S. M., and Kriegeskorte, N. (2014). Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Comput. Biol. 10:e1003915. doi: 10.1371/journal.pcbi.1003915
Kingma, D. P., and Ba, J. (2014). Adam: a method for stochastic optimization. arXiv 1412.6980.
Kitaoka, A., and Ashida, H. (2003). Phenomenal characteristics of the peripheral drift illusion. Vision 15, 261–262.
Kriegeskorte, N. (2015). Deep neural networks: a new framework for modeling biological vision and brain information processing. Annu. Rev. Vis. Sci. 1, 417–446. doi: 10.1146/annurev-vision-082114-035447
Kubilius, J., Bracci, S., and Op de Beeck, H. P. (2016). Deep neural networks as a computational model for human shape sensitivity. PLoS Comput. Biol. 12:e1004896. doi: 10.1371/journal.pcbi.1004896
Kuriki, I., Ashida, H., Murakami, I., and Kitaoka, A. (2008). Functional brain imaging of the Rotating Snakes illusion by fMRI. J. Vision 8, 16, 1–10. doi: 10.1167/8.10.16
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature 521, 436–444. doi: 10.1038/nature14539
Lotter, W., Kreiman, G., and Cox, D. (2016). Deep predictive coding networks for video prediction and unsupervised learning, arXiv 1605.08104.
Lucas, B. D., and Kanade, T. (1981). "An iterative image registration technique with an application to stereo vision," in Proceedings of Imaging Understanding Workshop (Vancouver, BC), 121–130.
Marblestone, A. H., Wayne, G., and Kording, K. P. (2016). Toward an integration of deep learning and neuroscience. Front. Comput. Neurosci. 10:94. doi: 10.3389/fncom.2016.00094
Mathieu, M., Couprie, C., and LeCun, Y. (2015). Deep multi-scale video prediction beyond mean square error. arXiv 1511.05440.
Muckli, L., and Petro, L. S. (2013). Network interactions: non-geniculate input to V1. Curr. Opin. Neurobiol. 23, 195–201. doi: 10.1016/j.conb.2013.01.020
Nasser, H. M., Calu, D. J., Schoenbaum, G., and Sharpe, M. J. (2017). The dopamine prediction error: contributions to associative models of reward learning. Front. Psychol. 8:244. doi: 10.3389/fpsyg.2017.00244
Nijhawan, R. (2008). Visual prediction: psychophysics and neurophysiology of compensation for time delays. Behav. Brain. Sci. 31, 179–198. doi: 10.1017/S0140525X08003804
Notredame, C. E., Pins, D., Deneve, D., and Jardri, R. (2014). What visual illusions teach us about schizophrenia. Front. Integr. Neurosci. 8:63. doi: 10.3389/fnint.2014.00063
Nour, M. M., and Nour, J. M. (2015). Perception, illusions and Bayesian inference. Psychopathology 48, 217–221. doi: 10.1159/000437271
CrossRef Full Text
Raman, R., and Sarkar, S. (2016). Predictive coding: a possible explanation of filling-in at the blind spot. PLoS ONE 11:e0151194. doi: 10.1371/journal.pone.0151194
Rao, R. P., and Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79–87. doi: 10.1038/4580
Schmidhuber, J. (2015). Deep learning in neural networks: an overview. Neural Netw. 61, 85–117. doi: 10.1016/j.neunet.2014.09.003
Schneider, C. A., Rasband, W. S., and Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675. doi: 10.1038/nmeth.2089
Schultz, W. (1998). Predictive reward signal of dopamine neurons. J. Neurophys. 80, 1–27. doi: 10.1152/jn.1998.80.1.1
Schultz, W., and Dickinson, A. (2000). Neuronal coding of prediction errors. Ann. Rev. Neurosci. 23, 473–500. doi: 10.1146/annurev.neuro.23.1.473
Shipp, S. (2016). Neural elements for predictive coding. Front. Psychol. 7:1792. doi: 10.3389/fpsyg.2016.01792
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al. (2017). Mastering the game of Go without human knowledge. Nature 550, 354–359. doi: 10.1038/nature24270
Sokolov, A. A., Miall, R. C., and Ivry, R. B. (2017). The cerebellum: adaptive prediction for movement and cognition. Trends Cogn. Sci. 21, 313–332. doi: 10.1016/j.tics.2017.02.005
Srivastava, N., Mansimov, E., and Salakhutdinov, R. (2015). Unsupervised learning of video representations using LSTMs. arXiv 1502.04681.
Tokui, S., Oono, K., Hido, S., and Clayton, J. (2015). "Chainer: a next-generation open source framework for deep learning," in Proceedings of Workshop on Machine Learning Systems (LearningSys) in the Annual Conference on Neural Information Processing Systems (NIPS) (Montreal, QC).
VanRullen, R. (2017). Perception science in the age of deep neural networks. Front. Psychol. 8:142. doi: 10.3389/fpsyg.2017.00142
Villegas, R., Yang, J., Hong, S., Lin, X., and Lee, H. (2017a). Decomposing motion and content for natural video sequence prediction. arXiv 1706.08033.
Villegas, R., Yang, J., Zou, Y., Sohn, S., Lin, X., and Lee, H. (2017b). Learning to generate long-term future via hierarchical prediction. arXiv 1704.05831.
Vondrick, C., Pirsiavash, H., and Torralba, A. (2016). "Generating videos with scene dynamics," in The Annual Conference on Neural Information Processing Systems (NIPS) (Barcelona).
Wang, P., and Cottrell, G. W. (2017). Central and peripheral vision for scene recognition: a neurocomputational modeling exploration. J. Vis. 17:9 doi: 10.1167/17.4.9
Watanabe, E., Matsunaga, W., and Kitaoka, A. (2010). Motion signals deflect relative positions of moving objects, Vision Res. 50, 2381–2390. doi: 10.1016/j.visres.2010.09.021
Weilnhammer, V., Stuke, H., Hesselmann, G., Sterzer, P., and Schmack, K. (2017). A predictive coding account of bistable perception - a model-based fMRI study. PLoS Comput. Biol. 13:e1005536. doi: 10.1371/journal.pcbi.1005536
Keywords: visual illusions, predictive coding, deep learning, artificial intelligence, cerebral cortex
Citation: Watanabe E, Kitaoka A, Sakamoto K, Yasugi M and Tanaka K (2018) Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction. Front. Psychol. 9:345. doi: 10.3389/fpsyg.2018.00345
Received: 12 December 2017; Accepted: 28 February 2018;
Published: 15 March 2018.
Rufin VanRullen, Université Toulouse III Paul Sabatier, France
Andrea Alamia, UMR5549 Centre de Recherche Cerveau et Cognition (CerCo), France
Bill Lotter, Harvard University, United States
Copyright © 2018 Watanabe, Kitaoka, Sakamoto, Yasugi and Tanaka. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Eiji Watanabe, eijwat@gmail.com; eiji@nibb.ac.jp


text:
"Blockbuster" heart trial could put CSL on track to $250
Lex Hall | 16 Jul 2018Text size |
This ASX-listed biotech is working on a "blockbuster" cardiovascular product that has the potential to take its stock price to $250, says Morningstar equity analyst Chris Kallos.
Blood plasma biotherapies company CSL (ASX: CSL) is in late-stage trials of a less invasive cardiovascular therapy. Known as CSL112, the treatment aims to dissolve the buildup of plaque – a leading cause of heart attacks.
CSL112, which is being trialled among 17,000 people, has prompted Kallos to raise his fair value estimate for the company to $200, with the potential to reach $250, pending the trial's success. At midday, CSL was trading at $202.
"It is a blockbuster, no doubt about it," Kallos says. "This company is increasingly diversified and this is a very attractive product that adds to that.
"A lot of money has been spent by other companies trying to do what CSL is doing. But we think CSL has a unique mechanism that allows the transportation of the plaque very quickly.
"Using a 55 per cent probability of success for CSL112 adds $40 to our revised fair value estimate of $200 a share. And all else equal, we see upside potential of $250 pending launch, with downside of $140 should CSL112 fail to progress."
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The new treatment could be on the market in five years pending the trial's success.
The probability of success is based on data from the Food and Drug Administration in the US, which over the past 10 years has approved 55 per cent of drugs targeting cardiovascular indications that reach phase 3.
There is a high incidence of people suffering a second heart attack within 90 days of their first attack, and a subsequent high mortality rate.
Kallos says the treatment could be on the market in five years, with peak sales of about US$10 billion by 2029, assuming a role in first heart attacks.
"If they're successful they should have a submission to the regulator in 2022. The review process should take a year so they could have a product on the market in 2023."
Kallos concedes the product candidate remains relatively high risk, as its efficacy is yet to be proven, but that it has a place in treating sufferers of a first heart attack.
"Pending successful approval of phase-3 trial, I'm assuming that if you have your first heart attack this treatment will be given to you.
"If this proves successful, the idea would be to lessen the need for invasive procedures like stents and instead provide a softer approach like drug therapy."
Read the report on the CSL trial here.
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Lex Hall is a Morningstar content editor, based in Sydney.
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Lex Hall is senior editor for Morningstar Australia
© 2022 Morningstar, Inc. All rights reserved. Neither Morningstar, its affiliates, nor the content providers guarantee the data or content contained herein to be accurate, complete or timely nor will they have any liability for its use or distribution. This information is to be used for personal, non-commercial purposes only. No reproduction is permitted without the prior written consent of Morningstar. Any general advice or 'regulated financial advice' under New Zealand law has been prepared by Morningstar Australasia Pty Ltd (ABN: 95 090 665 544, AFSL: 240892), or its Authorised Representatives, and/or Morningstar Research Ltd, subsidiaries of Morningstar, Inc, without reference to your objectives, financial situation or needs. For more information, refer to our Financial Services Guide (AU) and Financial Advice Provider Disclosure Statement (NZ). Our publications, ratings and products should be viewed as an additional investment resource, not as your sole source of information. Morningstar's full research reports are the source of any Morningstar Ratings and are available from Morningstar or your adviser. Past performance does not necessarily indicate a financial product's future performance. To obtain advice tailored to your situation, contact a licensed financial adviser. Some material is copyright and published under licence from ASX Operations Pty Ltd ACN 004 523 782. The article is current as at date of publication.


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Research & Training Facilities
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SCHOOL OF SCIENCE & ENGINEERING >> BIOMEDICAL ENGINEERING
Biomedical Engineering Home / About / Positions
Professor of the Practice in Biomedical Engineering, Department of Biomedical Engineering
The Department of Biomedical Engineering at Tulane University invites applications for a Professor of the Practice (PoP). Candidates should demonstrate an interest in enhancing the experiences of our undergraduate and graduate students. Our program covers disciplines such as electronics, signals and systems, anatomy, quantitative physiology, biomechanics, biotransport, biomaterials, tissue engineering, design thinking, and innovation. We seek a team-player with expertise in at least one of these disciplines and the desire and initiative to lead our department's growth through excellence in teaching, pedagogical development, student mentorship, field-related professional activity, and service. PoP faculty are encouraged to engage in, or lead, training grants and supervise student projects.
Applicants should have a PhD in engineering or a related field. The Professor of the Practice is a teaching intensive position with copious opportunities for intellectual engagement and institutional and societal contribution. Teaching and service constitute the criteria for advancement for faculty on this teaching track. Tulane University has a commitment to Professors of the Practice as full colleagues with long-term, renewable contracts,
The Department of Biomedical Engineering, founded in 1977, has a an ABET accredited undergraduate program with approximately 200 undergraduate majors and 50 graduate students. Biomedical Engineering is located in the School of Science and Engineering on Tulane's uptown campus. Our academic structure is beneficial to the Department of Biomedical Engineering because of the strong emphasis on interdisciplinary interactions and an administrative structure that reduces the overhead associated with these interactions. The department also administers an interdisciplinary Bioinnovation PhD program that is supported by an NIH T32 training grant.
The department's research and teaching presence on Tulane's uptown campus benefits from a unique cadaveric anatomy lab, dedicated design spaces, and an interdisciplinary MakerSpace. Furthermore, faculty and undergraduate students conduct mentored research in substantial BME-led laboratory space on the downtown Health Science campus. Our combined environment fosters collaborative interactions with the School of Medicine, School of Public Health and Tropical Medicine, the Southeast Louisiana VA Medical Center, Innovation Ochsner and the New Orleans Bioinnovation Center. More information can be found at https://sse.tulane.edu/node/2767 and http://www.bmen.tulane.edu.
Interested parties should submit a letter of interest, a teaching statement, a list of references and a full CV to the Search Committee at apply.interfolio.com/81749.
Applications will be evaluated beginning August 15, 2021 and will continue until the position is filled. Women and under-represented minorities are strongly encouraged to apply. Information requests should be sent to bmen-info@tulane.edu. Applications will be held in confidence, if requested.
Tulane University is an Affirmative Action - Equal Opportunity employer.


Treat yourself to a test drive in the 2019 Porsche Cayenne. Porsche made sure to keep road-handling and sportiness at the top of it's priority list. Turbocharger technology provides forced air induction, enhancing performance while preserving fuel economy. Porsche prioritized handling and performance with features such as: leather upholstery, heated and ventilated seats, air conditioning, power moon roof, a power liftgate, rain sensing wipers, and seat memory. Audio features include a CD player with MP3 capability, steering wheel mounted audio controls, A 40 gigabyte hard drive, and 14 speakers, providing excellent sound throughout the cabin. Porsche also prioritized safety and security with features such as: dual front impact airbags with occupant sensing airbag, head curtain airbags, traction control, brake assist, a panic alarm, an emergency communication system, and 4 wheel disc brakes with ABS. When road conditions become unpredictable, rely on all wheel drive to maintain outstanding control. We have the vehicle you've been searching for at a price you can afford. Please don't hesitate to give us a call.


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ArtfixDaily Artwire™
WINTER ANTIQUES SHOW CELEBRATES ITS 57TH YEAR WITH THREE NEW EXHIBITORS AND A LOAN EXHIBITION FROM HISTORIC CHARLESTON FOUNDATION
From exhibitors James & Nancy Glazer at the Winter Antiques Show is this Game board. Circa 1890. New England. Intricate geometric designs surround a central landscape painting on this Parcheesi game board.
From the Loan Exhibition presented by Historic Charleston Foundation at the Winter Antiques Show is Thomas Sully's Mrs. Robert Gilmor, Jr. (Sarah Reeve Ladson), 1823, Oil on canvas, 91.6 x 71.8 cm (36 1/8 x 28 1/4 in.). Gibbes Museum of Art/Carolina Art Association.
(ARTFIXdaily.com)
The Winter Antiques Show celebrates its 57th year as America's most prestigious antiques show, providing museums, established collectors, dealers, design professionals and first-time buyers with opportunities to see and purchase exceptional pieces showcased by 74 exhibitors. This year, specialists in 17th to 19th century American furniture and decorative arts, old master drawings and European sculpture, and Southeast Asian art join this fully vetted show, which runs from January 21-30, 2011. From an Egyptian relief depicting Akhenaten through mid–century furniture by Gerald Summers and Gio Ponti, every object exhibited at the Winter Antiques Show is vetted for quality and authenticity. All net proceeds from sponsors, special events, and ticket sales support East Side House Settlement, a non-profit in the South Bronx providing social services to community residents.
Dan and Cynthia Lufkin, passionate patrons of the arts, are the Winter Antiques Show's Honorary Co-Chairs for 2011. Sallie Krawcheck, President of Global Wealth and Investment Management, Bank of America, is Chair of the Opening Night Party on Thursday, January 20th, with U.S. Trust, Bank of America Private Wealth Management, as sponsor of the event.
Young Collectors Night, an exciting evening for new collectors as well as art and design enthusiasts, will be held Thursday, January 27th. This year's event includes an exclusive "Meet the Designers" reception with New York-based interior designers. Architects and designers also host the "Expert Eye" series, featuring lectures and book signings by Thomas Jayne (The Finest Rooms in America), Pauline C. Metcalf (Syrie Maugham), and Peter Pennoyer (Peter Pennoyer Architects). The 2011 Special Guest Lecturer is Vanessa Remington, Assistant Curator of Paintings at the Royal Collection, on the 19th-century portrait miniature collection of Queen Victoria.
The 2011 loan exhibition, Grandeur Preserved: Masterworks Presented by Historic Charleston Foundation highlights more than fifty objects in an unprecedented collaboration among Charleston's leading cultural institutions as well as private collections. The exhibition has been organized by Historic Charleston Foundation, established in 1947 as an educational non-profit dedicated to the preservation of buildings, landscapes and cultural resources in Charleston and its historic surroundings. Important objects from Historic Charleston Foundation's two museum houses will be complemented with loans from The Charleston Museum, Drayton Hall, Gibbes Museum of Art/Carolina Art Association, and Middleton Place Foundation, and include many works that will be on view for the first time. A five-part lecture series related to the loan exhibition will be presented during the Show. Jeff Daly, formerly senior design advisor to the director at The Metropolitan Museum of Art and now head his own design firm, will design the loan exhibition, sponsored by Chubb Personal Insurance for the 15th consecutive year.
New exhibitors at the 2011 Winter Antiques Show:
Richard Philp: Established in 1966 in London, Richard Philp specializes in early portraiture, old master drawings, Medieval and Renaissance sculpture, antiquities, and 20th-century British and European drawings. The gallery is known for its exciting and eclectic mix of periods and cultures. Returning to the Winter Antiques Show after a hiatus, Philp's offerings include a range of Egyptian and medieval sculptures as well as a marble Byzantine capital with two eagles and carved foliage dating to the reign of Justinian (6th century).
Christopher T. Rebollo Antiques: Based in Mechanicsville, Pennsylvania, Rebollo focuses on 17th-19th century American Furniture and Decorative Arts, including paintings, metalwork and ceramics. The gallery specializes in choice examples of inlaid hardwood furniture with an emphasis on original condition and design. Rebollo will feature a Philadelphia Carved Mahogany Side Chair (c. 1768-1780) attributed to James Gillingham. The chair is in a remarkable state of preservation including a very early finish and retaining an historic Federal period seat covering. The carving is crisp with nuances intact.
Carlton Rochell Asian Art: Established in New York in 2002, Carlton Rochell focuses on important sculpture, paintings and decorative arts from India, Tibet, Nepal, Cambodia, Thailand and Indonesia prior to 1850. Rochell founded Sotheby's Indian and Southeast Asian Art Department in 1988, where he handled the sale of many significant private collections. Rochell will exhibit a figure of Shiva Adhikaranandi from India, Tamil Nadu, 12th century, Chola Dynasty, in copper alloy. The four-armed Shiva stands in a lively attitude upon a circular lotus pedestal, with his primary hands pressed together in anjali mudra in front of his chest. Together with Brahma and Vishnu, Shiva, the destroyer, is one of the three most important deities of the Hindu pantheon. While representations of Shiva Nataraja, Lord of the Dance, are quite common, the present manifestation is much less so. Images of Adhikaranandi are typically represented with Shiva having a bovine head, and those in which he has a human head, such as this one, are considerably rarer. This magnificent figure was made during the Chola dynasty, a period considered to be the zenith of bronze sculpture production in India.
Other selected highlights:
Christ blessing in a historiated initial "V" by Lorenzo Monaco. c. 1412-13 or 1420-1423. This splendid cutting comes from Corale H 74 (Florence, Museo Nazionale del Bargello), a choir book by Lorenzo Monaco. The manuscript, a Gradual, is one of three Choir Books made for the Church of the Hospital of Santa Maria Nuova in Florence. Scholars agree that the miniatures from these books convey the expressive force of the art of Lorenzo Monaco along with the elegance of his figures. The present miniature came to light in 1975 and was not systematically studied until its publication in the catalogue of the Lorenzo Monaco exhibition in 2006. -- Les Enluminures
Six-Fold Screen by Stani Buncho. c. 1800. Ink and color on gold leaf. This rare and exceptionally high six-fold screen by the celebrated painter is from the artist's mature period in the fuku yamato-e style and depicts Mount Tsukuba; the mate to this screen depicting Mount Fuji is in collection of Utsunomiya Art University. Screens by this well-known painter are extremely rare. -- Joan B. Mirviss, Ltd.
Portrait of Alice Hooper by John Singleton Copley, 1763. Oil on canvas. Copley painted this portrait of Alice Hooper, a daughter of Robert Hooper, at that time the wealthiest man in Marblehead, Massachusetts. Copley, only twenty-five years old, was at a pivotal point in his career. He had been painting professionally since 1753 when he was an astoundingly talented fifteen years old. In ten years time Copley achieved an impressive and self-taught mastery over the technical aspects of portraiture. -- Hirschl & Adler Galleries, Inc.
Lieutenant John Templar Shubrick by Anson Dickinson. 1814. Watercolor on ivory. Templar Shubrick was born at Belevedere Plantation into one of Charleston's great military families. In the Navy, Shubrick saw action throughout the War of 1812, serving on the "Chesapeake" and the "Constitution," before being taken prisoner by the British during their capture of the "Hornet." After receiving commendations for bravery from Congress and the State of South Carolina, Shubrick left for Algiers to help negotiate the Treaty of Algiers. Upon returning to Washington, D.C. for the ratification of the treaty, Shubrick's ship disappeared. Neither he nor his men were heard from again. -- Elle Shushan
Sleep by Albert Marque, marble relief, c. 1899. This graceful marble sculpture by Albert Marque, at once calm and serene, shows an allegory of Sleep, carved in low relief. Once owned by the photographer Ludovic Baschet, it was exhibited in Paris at the Salon de la Societe Nationale des Beaux-Arts in 1899. It was a few years later in the Salon d'Automne of 1905, when Marque had two sculptures displayed alongside paintings by Matisse, Derain and Vlaminck, that the art critic Louis Vauxcelles famously described his work: "Amid the orgy of pure colour, the candor of the busts is surprising: Donatello among the Fauves." Hence was born the term 'fauve'. -- Daniel Katz Limited
Complex Mask (Donati Studio Mask). Yup'ik; Kuskokwim Region, Alaska. c. 1890-1905. Wood, pigment, sinew, vegetal fiber, cotton thread, replaced feathers. This extraordinary Eskimo masks comes from the estate of the Surrealist artist Enrico Donati (1909-2008).Yup'ik Eskimo masks are possibly the highest form of expression of Native American art and profoundly influenced the Surrealist artists who had escaped Paris in the Second World War and settled in New York. The Studio Mask was originally acquired by Adam Hollis Twitchell who began trading along the Kuskokwim River in Alaska in 1905. Twitchell purchased many of his masks directly from the natives immediately after the ceremonial dances that he had witnessed. -- Donald Ellis Gallery Ltd.
Game Board. c. 1890. This extraordinary playing board is notable for its colorful decoration and the painting of a house at its center. While images or shapes usually appear at the center of a game board, only a handful of examples with a house are known to exist. One side of the features a Parcheesi board, with "home" represented by a painting of a house. The other side depicts a checkerboard in red and black. The maker of this game board went beyond necessity and has created something that is truly celebratory. The object was probably made in Pennsylvania and resided in a Pennsylvania collection for over 40 years. -- James & Nancy Glazer
Constellation Sculpture by Phil Powell and Paul Evans. c. 1953. Powell took up residence at his studio in New Hope, Pennsylvania in the late 1940s and began a decades long career honing his craft as a woodworker and artist. This unique illuminated wall sculpture ranks as one of his early masterpieces. The metalwork is by Paul Evans, who began working in Powell's studio after his graduation from Cranbrook. The metalwork consists of a metal frame overlaid in melted bronze with a volcanic texture, including the surrounds for the lamps. -- Lost City Arts
Themed booths at the Winter Antiques Show:
Among the Show's highlights are the booths created around a theme or inspiration. This year, portrait miniature specialist Elle Shushan's booth will be inspired by the portico of one of Charleston's most famous houses. Hans P. Kraus, Jr. Fine Photographs will re-create a 19th century portrait photography studio, including a camera and backdrop. Gerald Peters Gallery will feature works by members of The Cornish Colony, including Thomas Wilmer Dewing, Augustus St. Gaudens and Dennis Miller Bunker.
A full list of captions and images is available in the image gallery at: http://www.winterantiquesshow.com/press/.
The 57th annual Winter Antiques Show will be held from January 21-30, 2011 at the Park Avenue Armory, 67th Street and Park Avenue, New York City. Show hours are from 12 p.m. to 8 p.m. daily, except Sundays, Tuesday, and Thursday, 12 p.m. to 6 p.m. To purchase tickets for the Opening Night Party on Thursday, January 20th or the Young Collectors Night on Thursday, January 27th, please call (718) 292-7392 or visit the Show's website at www.winterantiquesshow.com. General admission to the Show is $20, which includes the Show's award-winning catalogue.
Tags: african arts American art American furniture antiquities arms & armor art nouveau - art deco arts & crafts asian antiques ceramics ethnographic & oceanic
A Rounded View: Artists of Color Discuss the Works of European Masters
Andrew Jones' first-ever Collections Curated by Designers of Distinction auction will be held Feb. 23rd
Asia Week New York to Present "Opportunities and Perspectives in Collecting Asian Art," a Panel Discussion at The Winter Show, Sunday, January 26th
In 'Designing Deco' Exhibition, Objects Tell Stories of Vanity, Speed, Vice and Jazz
Exhibition of French Still Lifes Serves Up Visual Splendor
Step Inside A Vibrant Slice of Mid-20th Century Country Life With Queena Stovall's Art
Part 2 of the Ken Fee collection of mostly Western bitters bottles will be auctioned online, Feb. 14-23
Affordable art offered in Shannon's online sale, now through January 23rd
Park Avenue Armory Convenes Multidisciplinary Cohort of Cultural Institutions to Commission New Work by 100 Women Artists in Response to the Centennial of the 19th Amendment
See all Press Release
Asia Week New York to Present "Opportuniti...


PANDA stands for Partial Augmented Nitritation Denitritation Alkalinity recover and was mainly developed by Prof. Dr.-Ing. Peter Hartwig and Prof. Dr.-Ing. Karl-Heinz Rosenwinkel from ISAH University of Hannover.


body length - 1cm Family Tortricidae.
Brown moth with overlapping wings. Body length is about 12mm.
A small moth Family Tortricidae.
Moth. Approximately 1" in length.
A small member of the Tortricidae family.
A new born tortricid micro moth.


An inclusive community that promotes a culture of openness, understanding and mutual support to students with Special Education Needs (SEN). At Ngee Ann Polytechnic (NP), we want you to have a holistic education experience and campus life, attain useful life skills to be independent, and gain meaningful employment after graduation.
At NP, we care for our students and we want to journey alongside with you. Our counsellors can provide you with confidential and professional counselling.
In March 2014, a SEN Fund has been set up by the Ministry of Education to allow polytechnic students with physical, hearing or visual impairment (PI, HI or VI) to have equal access to their institutions' programmes and services, as well as learn to be independent learners and workers. You can tap into the SEN Fund to purchase Assistive Technology (AT) devices and support services.
Click here for a list of AT devices and support services.
Individuals who are medically certified to have a permanent disability can enjoy 25% discount off adult fares and no additional cost for distance travelled beyond 7.2km.
Individuals who are medically certified as unable to take public transport and totally dependent on taxis for travelling to school or work can receive up to 50% subsidy for taxi fares.
We are continuously improving our campus to provide barrier-free accessibility to and within most buildings. If you require additional assistance for access to specific facilities, you can approach the Administrative Office in your Academic Schools.


Panels did indeed show up from Cool Wheels in Latvia. Cherry Classic Cars wasted no time in offering them up to the body. As expected with composite reproduction panels it's going to take quite a bit if fettling to get a good fit.
They also set about fixing the rust on the shell and other minor repairs.


With nearly 100 years under our belt and well into the 21st century, Mussehl & Westphal continues to provide the world with the highest quality musical saws.
Mussehl & Westphal is the maker of the original musical saw. Our singing saws are made of the finest steel and possess an outstanding range of over 2 octaves.
Personalized customer care and a superior product has garnered Mussehl & Westphal a first class reputation that continues to set the standard.
"Hello Mary! Just came to say that the saw arrived today!! I just love it! It is a surprise gift to my boyfriend, he'll get insane when he sees it, he was seeking one for a long time! Thanks for the lovely letter!!
I just want to say thank you for shipping my order to me so fast! I was a little nervous that it wouldn't be here in time for the holidays, so you can imagine my surprise when I saw the package on my doorstep this morning. Happy Holidays!
Click on Events for all festival dates & info.


In the last 2 days, a certified fire door inspector and a hardware supplier have both asked me where it is stated that Maine and Massachusetts have adopted the 2007 edition of NFPA 80, which includes the requirement for the annual inspection of fire doors.
In Maine, it's pretty easy: Office of State Fire Marshal, State of Maine – Adopted NFPA Standards. Scroll down to NFPA 80, and you'll see the 2007 edition listed.
This section lists 7 locations within 780 CMR that NFPA 80 is referenced.
– 302.1.1.1 refers to NFPA 80 for maximum clearances for doors in incidental use areas.
– 715.4.6.1 should actually be 715.3.6.1 and it has to do with size limits on glazing in fire doors.
– 715.4.4 relates to non-wire glass.
– 715.4.7.2 should be 715.3.7.2 and is regarding automatic closing fire door assemblies.
– 715.5 should be 715.4, which is related to fire protection rated glazing.
– 715.3.5 is not listed above but contains a reference to NFPA 80 regarding fire door labels.
– 1008.1.3.3 is related to horizontal sliding doors.
So the clearest reference that would point to the requirement for fire door inspection is 715.3 because according to that paragraph, fire door assemblies have to be installed in accordance with NFPA 80.
We are spreading the word about fire door inspection but it's a slow process. If it was easy, everyone would do it!
Mass-Fire Door Inspection. Lori when you hear of any other references that would point to the requirement of Fire Door Inspections for Massachusetts I would love to hear about them!!


unable to earn enough income to support themselves (and any family).
whether the client could change their position to ease their hardship (eg look for work, change the focus of their business).
Residency criteria for an Invalid's Benefit and for New Zealand Superannuation were reduced from 1 July 2007, to match those for the other main benefits. This resulted in an increase in the proportion of those receiving an Emergency Benefit who were working-age people.
This decrease (see table EB.1) reflected, at least in part, the reduced residency criteria from July 2007 outlined above for New Zealand Superannuation and some other benefits. This change reduced demand for Emergency Benefit, particularly among those aged 65 or over.
Many of the clients receiving an Emergency Benefit did so because they were not residentially qualified to receive another pension or main benefit.
The number of clients recorded in SWIFTT as receiving an Emergency Benefit at the end of June.
Between 2007/2008 and 2011/2012, cancellations of Emergency Benefits exceeded grants of Emergency Benefits (see table EB.2 and table EB.3).
An increase between 2007/2008 and 2011/2012 in Emergency Benefits granted (see table EB.2) reflected demand for benefits arising from the economic recession followed by a slow recovery.
The number of successful applications for an Emergency Benefit recorded in SWIFTT in years ended June.
Increases between 2009/2010 and 2011/2012 in cancellations of Emergency Benefits (see table EB.3) were driven by increased cancellations of Emergency Benefits by working-age clients. This largely reflected increases in Emergency Benefits granted to working-age clients, combined with the short-term nature of this assistance for most working-age recipients.
The number of cancellations of an Emergency Benefit recorded in SWIFTT during years ended June.
Between 2008 and 2012, an estimated 0.1% or less of working-age people were receiving an Emergency Benefit.
A large majority of Emergency Benefit recipients were clients aged 65 or over who did not qualify for New Zealand Superannuation (usually because they did not meet residency criteria). Once granted an Emergency Benefit, these clients tended to remain on an Emergency Benefit until they had fulfilled New Zealand Superannuation residency criteria, at which point they transferred to New Zealand Superannuation.
In 2011 and 2012, 58% of working-age Emergency Benefit recipients were aged 18–40 (see table EB.4). This compared with 44% in 2008.
In 2011 and 2012, around 25% of working-age Emergency Benefit recipients were aged 50–64 (see table EB.4). The use of Emergency Benefits among those aged 50–64 declined after the July 2007 change in residency criteria of other assistance reduced demand for Emergency Benefits in this age group.
The number of working-age clients recorded in SWIFTT as receiving an Emergency Benefit at the end of June.
Sixty-three percent of working-age clients granted an Emergency Benefit between 2008/2009 and 2011/2012 were aged under 40 (see table EB.5). This includes around 27% aged 18–24.
Increases between 2008/2009 and 2011/2012 in grants of Emergency Benefits were most apparent among 18–40 year olds.
The number of successful applications for an Emergency Benefit from working-age clients recorded in SWIFTT during years ended June.
From 2007/2008 to 2011/2012, between 48% and 52% of working-age clients who cancelled an Emergency Benefit entered paid work.


That will allow Meade to present himself as an apolitical candidate who is not tainted by the PRI's history of corruption and authoritarianism, which could help him win over independent votes. Some government critics loathe the PRI, but are afraid that a Lopez Obrador victory could turn Mexico into the next Venezuela.
Second, Meade is a technocrat who will be able to present himself as the right candidate for difficult economic times. At a time when a rightist populist U.S. president is threatening to pull out of the North American Free Trade Agreement, which would affect more than 80 percent of Mexico's exports, Meade will be able to say he has the perfect credentials to maintain economic stability and find new export markets.
An economist and attorney, Meade got his Ph.d. in economics from Yale University, and served twice as finance minister, and once as minister of foreign affairs. By comparison, Lopez Obrador is a former Mexico City mayor with virtually no international experience.
Third, Meade has not been tainted by corruption scandals, and has a reputation of being an affable person. I had a long conversation with him over coffee during a visit he made to Miami last year. He came across as a candid politician who does not speak in the convoluted, highly scripted manner of Peña Nieto and other PRI politicians.
But, having said that, Meade will still be the underdog in this race. Lopez Obrador is leading in the polls with about 30 percent of the vote, followed by Meade with 23 percent and three independent candidates who account for much of the remainder.
One thing that all polls show is that Mexicans are eager for change. When asked by Latinobarómetro whether they agree with the claim that, "The government rules for a few powerful groups," 90 percent of Mexicans said Yes, more than in all other Latin American countries except Brazil.
The PRI's strategy to win, judging from what I'm told by party insiders, will be to divide the anti-PRI vote into several factions, so as to take votes away from Lopez Obrador. In Mexico, there is no second-round vote, which means that Meade could possibly win with only 30 percent of the vote, or even less, if the opposition vote is splintered among three or four opposition candidates.
In the past, this strategy has often worked for the PRI, which is known to have financed opposition parties to help divide its opponents. But this time, given Mexico's anti-government sentiment, it will be a big gamble: Lopez Obrador, as well, could win with 30 percent of the vote, or less.
Mexico's tragedy is that, largely because of Peña Nieto's failure to push for a political reform that would have allowed a second-round vote, the country will be condemned to having a weak president. That could lead to a leftist-populist overreaction, or to a continuation of the status quo with little room for making needed reforms.
Either way, Mexico's faces difficult times.