Group-theoretic algorithms for matrix multiplication

We further develop the group-theoretic approach to fast matrix multiplication introduced by Cohn and Umans, and for the first time use it to derive algorithms asymptotically faster than the standard algorithm. We describe several families of wreath product groups that achieve matrix multiplication exponent less than 3, the asymptotically fastest of which achieves exponent 2.41. We present two conjectures regarding specific improvements, one combinatorial and the other algebraic. Either one would imply that the exponent of matrix multiplication is 2.Comment: 10 page

Efficient Sound Card Based Experimention At Different Levels Of Natural Science Education

Sound cards, which count as standard equipment in today's computers, can be turned into measurement tools, making experimentation very efficient and cheap. The chief difficulties to overcome are the lack of proper hardware interfacing and processing software. Sound-card experimentation becomes really viable only if we demonstrate how to connect different sensors to the sound card and provide suitable open-source software to support the experiments. In our talk, we shall present a few applications of sound cards in measurements: photogates, stopwatches and an example of temperature measurement and registration. We also provide the software for these applications.Comment: MPTL-HSCI 2011 Joint conference, 15-17 September 2011, Ljubljana, Sloveni

Learning to Crawl

Web crawling is the problem of keeping a cache of webpages fresh, i.e., having the most recent copy available when a page is requested. This problem is usually coupled with the natural restriction that the bandwidth available to the web crawler is limited. The corresponding optimization problem was solved optimally by Azar et al. [2018] under the assumption that, for each webpage, both the elapsed time between two changes and the elapsed time between two requests follow a Poisson distribution with known parameters. In this paper, we study the same control problem but under the assumption that the change rates are unknown a priori, and thus we need to estimate them in an online fashion using only partial observations (i.e., single-bit signals indicating whether the page has changed since the last refresh). As a point of departure, we characterise the conditions under which one can solve the problem with such partial observability. Next, we propose a practical estimator and compute confidence intervals for it in terms of the elapsed time between the observations. Finally, we show that the explore-and-commit algorithm achieves an $\mathcal{O}(\sqrt{T})$ regret with a carefully chosen exploration horizon. Our simulation study shows that our online policy scales well and achieves close to optimal performance for a wide range of the parameters.Comment: Published at AAAI 202

Horizontal cooling towers: riverine ecosystem services and the fate of thermoelectric heat in the contemporary Northeast US

The electricity sector is dependent on rivers to provide ecosystem services that help regulate excess heat, either through provision of water for evaporative cooling or by conveying, diluting and attenuating waste heat inputs. Reliance on these ecosystem services alters flow and temperature regimes, which impact fish habitat and other aquatic ecosystem services. We demonstrate the contemporary (2000–2010) dependence of the electricity sector on riverine ecosystem services and associated aquatic impacts in the Northeast US, a region with a high density of thermoelectric power plants. We quantify these dynamics using a spatially distributed hydrology and water temperature model (the framework for aquatic modeling in the Earth system), coupled with the thermoelectric power and thermal pollution model. We find that 28.4% of thermoelectric heat production is transferred to rivers, whereas 25.9% is directed to vertical cooling towers. Regionally, only 11.3% of heat transferred to rivers is dissipated to the atmosphere and the rest is delivered to coasts, in part due to the distribution of power plants within the river system. Impacts to the flow regime are minimal, while impacts to the thermal regime include increased river lengths of unsuitable habitats for fish with maximum thermal tolerances of 24.0, 29.0, and 34.0 ° C in segments downstream of plants by 0.6%, 9.8%, and 53.9%, respectively. Our analysis highlights the interactions among electricity production, cooling technologies, aquatic impacts, and ecosystem services, and can be used to assess the full costs and tradeoffs of electricity production at regional scales

Electromagnetic Momentum in Dispersive Dielectric Media

When the effects of dispersion are included, neither the Abraham nor the Minkowski expression for electromagnetic momentum in a dielectric medium gives the correct recoil momentum for absorbers or emitters of radiation. The total momentum density associated with a field in a dielectric medium has three contributions: (i) the Abraham momentum density of the field, (ii) the momentum density associated with the Abraham force, and (iii) a momentum density arising from the dispersive part of the response of the medium to the field, the latter having a form evidently first derived by D.F. Nelson [Phys. Rev. A 44, 3985 (1991)]. All three contributions are required for momentum conservation in the recoil of an absorber or emitter in a dielectric medium. We consider the momentum exchanged and the force on a polarizable particle (e.g., an atom or a small dielectric sphere) in a host dielectric when a pulse of light is incident upon it, including the dispersion of the dielectric medium as well as a dispersive component in the response of the particle to the field. The force can be greatly increased in slow-light dielectric media.Comment: 9 pages. To be published by Optics Communication

A Cloud-based Machine Learning Pipeline for the Efficient Extraction of Insights from Customer Reviews

The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. In this paper, we present a cloud-based system that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector embedding-based keyword extraction, and clustering. The elements of our model have been integrated and further developed to meet better the requirements of efficient information extraction, topic modeling of the extracted information, and user needs. Furthermore, our system can achieve better results than this task's existing topic modeling and keyword extraction solutions. Our approach is validated and compared with other state-of-the-art methods using publicly available datasets for benchmarking

Task scheduling to constrain peak current consumption in wearable healthcare sensors

Small embedded systems, in our case wearable healthcare devices, have significant engineering challenges to reduce their power consumption for longer battery life while at the same time supporting ever increasing processing requirements for more intelligent applications. Research has primarily focused on achieving lower power operation through hardware designs and intelligent methods of scheduling software tasks, all with the objective to minimize the overall consumed electrical power. However, such an approach inevitably creates points in time where software tasks and peripherals coincide to draw large peaks electrical current creating short-term electrical stress for the battery and power regulators, and adding to electromagnetic interference emissions. This position paper proposes that the power profile of an embedded device using a Real-Time Operating System (RTOS) will significantly benefit if the task scheduler is modified to be informed of the electrical current profile required for each task. This enables the task scheduler to schedule tasks that require large amounts of current to be spread over time, thus constraining the peak current that the system will draw. We propose a solution to inform the task scheduler of a tasks’ power profile and we discuss our application scenario that clearly benefited from the proposal

Refined heart failure detection algorithm for improved clinical reliability of OptiVol alerts in CRT-D recipients

   Background: The reliability of intrathoracic impedance monitoring for prediction of heart failure (HF) by implantable cardiac devices is controversial. Despite using additional device-based parameters described in the PARTNERS HF study, such as new onset of arrhythmias, abnormal autonomics, low biventricular pacing rate or patient activity level, the predictive power of device diagnostic algorithm is still in doubt. The objective of this study was to compare the device diagnostic algorithm described in the PARTNERS HF study to a newly developed algorithm applying refined diagnostic criteria. Methods: Fourty two patients were prospectively enrolled who had been implanted with an intrathoracic impedance and remote monitoring capable implantable cardiac defibrillator with a cardiac resychroniza­tion therapy (CRT-D) device in this observational study. If a remote OptiVolTM alert occurred, patients were checked for presence of HF symptoms. A new algorithm was derived from the original PARTNERS HF criteria, considering more sensitive cut-offs and changes of patterns of the device-based parameters. Results: During an average follow-up of 38 months, 722 remote transmissions were received. From the total of 128 transmissions with OptiVol alerts, 32 (25%) corresponded to true HF events. Upon multivariate discriminant analysis, low patient activity, high nocturnal heart rate, and low CRT pacing (< 90%) proved to be independent predictors of true HF events (all p < 0.01). Incorporating these three refined criteria in a new algorithm, the diagnostic yield of OptiVol was improved by increasing specific­ity from 37.5% to 86.5%, positive predictive value from 34.1% to 69.8% and area under the curve from 0.787 to 0.922 (p < 0.01), without a relevant loss in sensitivity (96.9% vs. 93.8%). Conclusions: A refined device diagnostic algorithm based on the parameters of low activity level, high nocturnal heart rate, and suboptimal biventricular pacing might improve the clinical reliability of OptiVol alerts.