IceCube-Gen2: A Vision for the Future of Neutrino Astronomy in Antarctica

The recent observation by the IceCube neutrino observatory of an astrophysical flux of neutrinos represents the "first light" in the nascent field of neutrino astronomy. The observed diffuse neutrino flux seems to suggest a much larger level of hadronic activity in the non-thermal universe than previously thought and suggests a rich discovery potential for a larger neutrino observatory. This document presents a vision for an substantial expansion of the current IceCube detector, IceCube-Gen2, including the aim of instrumenting a $10\,\mathrm{km}^3$ volume of clear glacial ice at the South Pole to deliver substantial increases in the astrophysical neutrino sample for all flavors. A detector of this size would have a rich physics program with the goal to resolve the sources of these astrophysical neutrinos, discover GZK neutrinos, and be a leading observatory in future multi-messenger astronomy programs.Comment: 20 pages, 12 figures. Address correspondence to: E. Blaufuss, F. Halzen, C. Kopper (Changed to add one missing author, no other changes from initial version.

Drawing Learning Charters

{Excerpt} Despite competing demands, modern organizations should not forget that learning is the best way to meet the challenges of the time. Learning charters demonstrate commitment: they area touchstone against which provision and practice can be tested and a waymark with which to guide, monitor, and evaluate progress. It is difficult to argue that what learning charters advocate is not worth striving for. Often, strategic reversals in organizational change are failures of execution. Poor communications explain much. That is because the real power of the vision that underpins change can only be unleashed if institutional commitment is verbalized to frame a desirable future; share core beliefs, common values, and understandings; and help motivate and coordinate the actions that drive transformation. To spark action, credible, focused, jargon-free, on time, liberal, face-to-face, and two-way communication in the right context is necessary. Effective visions cannot be imposed on people: they must be set in motion by way of persuasion. Progressively then, communication for change (i) raises awareness and informs stakeholders of vision, progress, and outcomes; (ii) edifies stakeholders regarding their active involvement in the change process and imparts skills, knowledge, and appreciation; and (iii) generates buy in and a sense of excitement about the transformation. Personnel who communicate well incorporate each day, at every conceivable opportunity, messages that update, educate,and commit. They preach a vision through conversation and storytelling. They continually reaffirm it. The best visions call on the past, relate to the present, and link to the future

Generative Models for Active Vision

The active visual system comprises the visual cortices, cerebral attention networks, and oculomotor system. While fascinating in its own right, it is also an important model for sensorimotor networks in general. A prominent approach to studying this system is active inference—which assumes the brain makes use of an internal (generative) model to predict proprioceptive and visual input. This approach treats action as ensuring sensations conform to predictions (i.e., by moving the eyes) and posits that visual percepts are the consequence of updating predictions to conform to sensations. Under active inference, the challenge is to identify the form of the generative model that makes these predictions—and thus directs behavior. In this paper, we provide an overview of the generative models that the brain must employ to engage in active vision. This means specifying the processes that explain retinal cell activity and proprioceptive information from oculomotor muscle fibers. In addition to the mechanics of the eyes and retina, these processes include our choices about where to move our eyes. These decisions rest upon beliefs about salient locations, or the potential for information gain and belief-updating. A key theme of this paper is the relationship between “looking” and “seeing” under the brain's implicit generative model of the visual world

Visual Function Questionnaire as an outcome measure for homonymous hemianopia: subscales and supplementary questions, analysis from the VISION trial

Background: We conduct supplementary analyses of the NEI VFQ-25 data to evaluate where changes occurred within subscales of the NEI VFQ-25 leading to change in the composite scores between the three treatment arms, and evaluate the NEI VFQ-25 with and without the Neuro 10 supplement. Methods: A prospective, multicentre, parallel, single-blind, three-arm RCT of fourteen UK acute stroke units was conducted. Stroke survivors with homonymous hemianopia were recruited. Interventions included: Fresnel prisms for minimum 2 h, 5 days/week over 6-weeks (Arm a), Visual search training for minimum 30 min, 5 days/week over 6-weeks (Arm b) and standard care-information only (Arm c). Primary and secondary outcomes (including NEI VFQ-25 data) were measured at baseline, 6, 12 and 26 weeks after randomisation. Results: Eighty seven patients were recruited (69% male; mean age (SD) equal to 69 (12) years). At 26 weeks, outcomes for 24, 24 and 22 patients, respectively, were compared to baseline. NEI VFQ-25 (with and without Neuro 10) responses improved from baseline to 26 weeks with visual search training compared to Fresnel prisms and standard care. In subscale analysis, the most impacted across all treatment arms was ‘driving’ whilst the least impacted were ‘colour vision’ and ‘ocular pain’. Conclusions: Composite scores differed systematically for the NEI VFQ-25 (Neuro 10) versus NEI VFQ-25 at all time points. For subscale scores, descriptive statistics suggest clinically relevant improvement in distance activities and vision-specific dependency subscales for NEI VFQ-25 scores in the visual search treatment arm. Trial Registration: Current Controlled Trials ISRCTN05956042

Hierarchical Side-Tuning for Vision Transformers

Fine-tuning pre-trained Vision Transformers (ViT) has consistently demonstrated promising performance in the realm of visual recognition. However, adapting large pre-trained models to various tasks poses a significant challenge. This challenge arises from the need for each model to undergo an independent and comprehensive fine-tuning process, leading to substantial computational and memory demands. While recent advancements in Parameter-efficient Transfer Learning (PETL) have demonstrated their ability to achieve superior performance compared to full fine-tuning with a smaller subset of parameter updates, they tend to overlook dense prediction tasks such as object detection and segmentation. In this paper, we introduce Hierarchical Side-Tuning (HST), a novel PETL approach that enables ViT transfer to various downstream tasks effectively. Diverging from existing methods that exclusively fine-tune parameters within input spaces or certain modules connected to the backbone, we tune a lightweight and hierarchical side network (HSN) that leverages intermediate activations extracted from the backbone and generates multi-scale features to make predictions. To validate HST, we conducted extensive experiments encompassing diverse visual tasks, including classification, object detection, instance segmentation, and semantic segmentation. Notably, our method achieves state-of-the-art average Top-1 accuracy of 76.0% on VTAB-1k, all while fine-tuning a mere 0.78M parameters. When applied to object detection tasks on COCO testdev benchmark, HST even surpasses full fine-tuning and obtains better performance with 49.7 box AP and 43.2 mask AP using Cascade Mask R-CNN

Classification and Retrieval of Digital Pathology Scans: A New Dataset

In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai

Transportation mode recognition fusing wearable motion, sound and vision sensors

We present the first work that investigates the potential of improving the performance of transportation mode recognition through fusing multimodal data from wearable sensors: motion, sound and vision. We first train three independent deep neural network (DNN) classifiers, which work with the three types of sensors, respectively. We then propose two schemes that fuse the classification results from the three mono-modal classifiers. The first scheme makes an ensemble decision with fixed rules including Sum, Product, Majority Voting, and Borda Count. The second scheme is an adaptive fuser built as another classifier (including Naive Bayes, Decision Tree, Random Forest and Neural Network) that learns enhanced predictions by combining the outputs from the three mono-modal classifiers. We verify the advantage of the proposed method with the state-of-the-art Sussex-Huawei Locomotion and Transportation (SHL) dataset recognizing the eight transportation activities: Still, Walk, Run, Bike, Bus, Car, Train and Subway. We achieve F1 scores of 79.4%, 82.1% and 72.8% with the mono-modal motion, sound and vision classifiers, respectively. The F1 score is remarkably improved to 94.5% and 95.5% by the two data fusion schemes, respectively. The recognition performance can be further improved with a post-processing scheme that exploits the temporal continuity of transportation. When assessing generalization of the model to unseen data, we show that while performance is reduced - as expected - for each individual classifier, the benefits of fusion are retained with performance improved by 15 percentage points. Besides the actual performance increase, this work, most importantly, opens up the possibility for dynamically fusing modalities to achieve distinct power-performance trade-off at run time

A Look at Vision: Perspectives Throughout the Organizational Hierarchy of the Christian University Context

Research supports vision as a central tenet to leadership. Just as important as the content of vision is how it is communicated. However, once a vision is cast, far less is known about how it is communicated throughout the organization and how it influences members throughout the organizational hierarchy. For faith-based organizations like Christian colleges and universities, vision is particularly important as it serves to steer the institution toward a greater realization of its faith-based identity. This study contributes to the empirical research on vision, its communication, and its effect, as both the nature and impact of vision communication within Christian higher education are explored. A multicase qualitative research design was employed at 2 Christian universities in the southeastern U.S. Purposeful sampling stratified participants based on their position level within the institution (i.e., senior-level executive, mid-level manager, entry-level employee). Data were collected primarily through 36 interviews. Findings show that vision communication was primarily attributed to the president at each institution, though others felt a responsibility in sharing vision and expressed it in different ways. Additionally, there was a strong, shared alignment with the Christian-focused vision, which was a compelling factor for participants. However, the clarity of what the overall vision was or how to implement it was often obscured by factors unique to each institution, resulting in self-interpretations, assumptions, and frustration. Recommendations are provided for practitioners and for further research, including: leveraging positional power the president has as the chief communicator of vision; establishing an infrastructure for vision communication; and, for faith-based institutions, emphasizing the connection of the institution’s faith-identity to its vision in order to further inspire employees to pursue the vision

Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement Learning

The Vision Transformer architecture has shown to be competitive in the computer vision (CV) space where it has dethroned convolution-based networks in several benchmarks. Nevertheless, Convolutional Neural Networks (CNN) remain the preferential architecture for the representation module in Reinforcement Learning. In this work, we study pretraining a Vision Transformer using several state-of-the-art self-supervised methods and assess data-efficiency gains from this training framework. We propose a new self-supervised learning method called TOV-VICReg that extends VICReg to better capture temporal relations between observations by adding a temporal order verification task. Furthermore, we evaluate the resultant encoders with Atari games in a sample-efficiency regime. Our results show that the vision transformer, when pretrained with TOV-VICReg, outperforms the other self-supervised methods but still struggles to overcome a CNN. Nevertheless, we were able to outperform a CNN in two of the ten games where we perform a 100k steps evaluation. Ultimately, we believe that such approaches in Deep Reinforcement Learning (DRL) might be the key to achieving new levels of performance as seen in natural language processing and computer vision. Source code will be available at: https://github.com/mgoulao/TOV-VICRe