Optimal Algorithms for k -Search with Application inOption Pricing

In the k-search problem, a player is searching for the k highest (respectively, lowest) prices in a sequence, which is revealed to her sequentially. At each quotation, the player has to decide immediately whether to accept the price or not. Using the competitive ratio as a performance measure, we give optimal deterministic and randomized algorithms for both the maximization and minimization problems, and discover that the problems behave substantially different in the worst-case. As an application of our results, we use these algorithms to price "lookback options”, a particular class of financial derivatives. We derive bounds for the price of these securities under a no-arbitrage assumption, and compare this to classical option pricin

Von Neumann and Newman poker with a flip of hand values

AbstractThe von Neumann and Newman poker models are simplified two-person poker models in which hands are modeled by real values drawn uniformly at random from the unit interval. We analyze a simple extension of both models that introduces an element of uncertainty about the final strength of each player’s own hand, as is present in real poker games. Whenever a showdown occurs, an unfair coin with fixed bias q is tossed, 0≤q≤1/2. With probability 1−q, the higher hand value wins as usual, but, with the remaining probability q, the lower hand wins. Both models favor the first player for q=0 and are fair for q=1/2. Our somewhat surprising result is that the first player’s expected payoff increases with q as long as q is not too large. That is, the first player can exploit the additional uncertainty introduced by the coin toss and extract even more value from his opponent

All keypoints you need: detecting arbitrary keypoints on the body of triple, high, and long jump athletes

Performance analyses based on videos are commonly used by coaches of athletes in various sports disciplines. In individual sports, these analyses mainly comprise the body posture. This paper focuses on the disciplines of triple, high, and long jump, which require fine-grained locations of the athlete’s body. Typical human pose estimation datasets provide only a very limited set of keypoints, which is not sufficient in this case. Therefore, we propose a method to detect arbitrary keypoints on the whole body of the athlete by leveraging the limited set of annotated keypoints and auto- generated segmentation masks of body parts. Evaluations show that our model is capable of detecting keypoints on the head, torso, hands, feet, arms, and legs, including also bent elbows and knees. We analyze and compare different techniques to encode desired keypoints as the model’s input and their embedding for the Transformer backbone

Requirements for Sensor Integrating Machine Elements : A Review of Wear and Vibration Characteristics of Gears

For condition monitoring of machines sensor integrating standard machine elements provide advantage in acquiring high-quality, robust data from individual machine elements and reducing effort in signal processing. However, research covering small and inexpensive consumer-grade MEMS sensors with respect to integration and measurement requirements for wear detection is limited. In order to define such requirements, the state of the art of vibration-based condition monitoring of gears is reviewed and summarised. The focus is on the characteristics of progressive wear and how it might show in the vibration signal. The review finds that correlation between wear and vibration characteristics of gears exist, but the interpretation of the vibration signals is challenging and requires purpose-built signal processing methods. The review also concludes that integrated MEMS acceleration sensors are theoretically able to measure the vibration characteristics of gears to detect wear. Important characteristics are the gear mesh acceleration with its frequencies and harmonic multiples (GMFi). Frequency range requirements for the sensors depend on the operating conditions of gears, the upper frequency limit needs to be greater or equal to 1.3 GMF$_{i,max}$. For the measuring range requirements, upper limits of 20 g RMS can be extracted within certain conditions. Data analysis requires a minimum frequency resolution which affects the size of memory needed for an integrated sensor system. However, there is a lack of research whether the sensitivity and internal noise behaviour of available MEMS sensors is good enough to measure relative changes in the vibration signals caused by wear

Haystack: a panoptic scene graph dataset to evaluate rare predicate classes

Current scene graph datasets suffer from strong long-tail distributions of their predicate classes. Due to a very low number of some predicate classes in the test sets, no reliable metrics can be retrieved for the rarest classes. We construct a new panoptic scene graph dataset and a set of metrics that are designed as a benchmark for the predictive performance especially on rare predicate classes. To construct the new dataset, we propose a model-assisted annotation pipeline that efficiently finds rare predicate classes that are hidden in a large set of images like needles in a haystack. Contrary to prior scene graph datasets, Haystack contains explicit negative annotations, i.e. annotations that a given relation does not have a certain predicate class. Negative annotations are helpful especially in the field of scene graph generation and open up a whole new set of possibilities to improve current scene graph generation models. Haystack is 100% compatible with existing panoptic scene graph datasets and can easily be integrated with existing evaluation pipelines. Our dataset and code can be found here: https://lorjul.github.io/haystack/. It includes annotation files and simple to use scripts and utilities, to help with integrating our dataset in existing work

Detecting arbitrary keypoints on limbs and skis with sparse partly correct segmentation masks

Analyses based on the body posture are crucial for top- class athletes in many sports disciplines. If at all, coaches label only the most important keypoints, since manual anno- tations are very costly. This paper proposes a method to de- tect arbitrary keypoints on the limbs and skis of professional ski jumpers that requires a few, only partly correct segmen- tation masks during training. Our model is based on the Vision Transformer architecture with a special design for the input tokens to query for the desired keypoints. Since we use segmentation masks only to generate ground truth labels for the freely selectable keypoints, partly correct seg- mentation masks are sufficient for our training procedure. Hence, there is no need for costly hand-annotated segmen- tation masks. We analyze different training techniques for freely selected and standard keypoints, including pseudo la- bels, and show in our experiments that only a few partly cor- rect segmentation masks are sufficient for learning to detect arbitrary keypoints on limbs and skis

Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion

We present a novel method for precise 3D object localization in single images from a single calibrated camera using only 2D labels. No expensive 3D labels are needed. Thus, instead of using 3D labels, our model is trained with easy-to-annotate 2D labels along with the physical knowledge of the object's motion. Given this information, the model can infer the latent third dimension, even though it has never seen this information during training. Our method is evaluated on both synthetic and real-world datasets, and we are able to achieve a mean distance error of just 6 cm in our experiments on real data. The results indicate the method's potential as a step towards learning 3D object location estimation, where collecting 3D data for training is not feasible

Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes

Current scene graph datasets suffer from strong long-tail distributions of their predicate classes. Due to a very low number of some predicate classes in the test sets, no reliable metrics can be retrieved for the rarest classes. We construct a new panoptic scene graph dataset and a set of metrics that are designed as a benchmark for the predictive performance especially on rare predicate classes. To construct the new dataset, we propose a model-assisted annotation pipeline that efficiently finds rare predicate classes that are hidden in a large set of images like needles in a haystack. Contrary to prior scene graph datasets, Haystack contains explicit negative annotations, i.e. annotations that a given relation does not have a certain predicate class. Negative annotations are helpful especially in the field of scene graph generation and open up a whole new set of possibilities to improve current scene graph generation models. Haystack is 100% compatible with existing panoptic scene graph datasets and can easily be integrated with existing evaluation pipelines. Our dataset and code can be found here: https://lorjul.github.io/haystack/. It includes annotation files and simple to use scripts and utilities, to help with integrating our dataset in existing work

TALKING INSTITUTIONS IN THE SHARING ECONOMY: A CONTENT ANALYSIS OF ACTOR QUOTES IN THE PRINT MEDIA AND A TAXONOMY OF DISCURSIVE STRATEGIES

We study how actors engage in institutional work to manage legitimacy by influencing media discourse in the face of discontinuous innovation. We content-analyze actor quotes reproduced in newspaper articles about the ‘sharing economy’ in the taxi and lodging industries to survey this aspect of media discourse and offer a taxonomy of the discursive strategies used in the public debate on institutional change. We find that actor quotes are dominantly from offensive actors striving for institutional change, mostly due to a relatively low share of voice of incumbent firms as defensive actors aiming at institutional maintenance. Whereas offensive actors aimed for legitimacy in their discursive strategies by balancing attacks on existing institutions with assertions of new institutions, defensive actors aimed for legitimacy more by attacking new institutions than by reinforcing existing ones. Our findings suggest that, contrary to prior beliefs, preventing the emergence of new institutions plays a crucial role for defensive institutional work