FX Palo Alto Laboratory

Abstract Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying ground states of quantum problems. A significant advancement toward improving the practical scalability of NQS has been the incorporation of autoregressive models, most recently transformers, as variational ansätze. Transformers learn sequence information with greater expressiveness than recurrent models, but at the cost of increased time complexity with respect to sequence length. We explore the use of the retentive network (RetNet), a recurrent alternative to transformers, as an ansatz for solving electronic ground state problems in ab initio quantum chemistry. Unlike transformers, RetNets overcome this time complexity bottleneck by processing data in parallel during training, and recurrently during inference. We give a simple computational cost estimate of the RetNet and directly compare it with similar estimates for transformers, establishing a clear threshold ratio of problem-to-model size past which the RetNet’s time complexity outperforms that of the transformer. Though this efficiency comes at the expense of decreased expressiveness relative to the transformer, we overcome this gap through training strategies that leverage the autoregressive structure of the model—namely, variational neural annealing. Our findings support the RetNet as a means of improving the time complexity of NQS without sacrificing accuracy. We provide further evidence that the ablative improvements of neural annealing extend beyond the RetNet architecture, suggesting it would serve as an effective general training strategy for autoregressive NQS.

Sense of belonging refers to the human emotional need to affiliate with and be accepted by members of a group.; They are part of it. This allows us to develop pride, pride about what we are, in our roots; with this feeling, people try to care for and preserve their environment in such a way that he helps to make their own community develop, they participate positively in its changes, helps the governments. However, if we not promote and open doors to learning, teaching and disseminating our traditions and customs, we have a society disinterested in its surroundings and environment that tries to find other groups to integrate. he. This is why it is important to promote our history and traditions in our basic education and to involve citizens respecting their culture to ensure the success in the development of our communities and cities.

A tanulmányban azt vizsgáljuk, hogy egy automatizált szoftvertesztelő rendszer megoldást tud-e nyújtani a szoftver funkcionális tesztelése mellett a hatékony és pontos terheléses tesztelésre. A stressztesztet gyakorlati példán, valós üzleti problémán keresztül mutatjuk be: az FX Software Zrt. által fejlesztett InFoRex rendszert vetjük terheléses teszt alá. A stresszteszt futtatásának köszönhetően egy tisztább képet kaptunk a rendszer határaival kapcsolatban. A teszt eredményei szerint garantálható a rendszer megbízhatósága kritikus helyzetekben is, míg az eredmények tárolására készített szoftver segítségével korán észrevehetővé tehető, ha rossz irányba indul el egy fejlesztés. Rávilágítunk, hogy drasztikusan kisebb erőforrásigény mellett sokkal nagyobb terhelés alá tudunk vetni egy-egy szoftvert automatizáltan, de ehhez azonban továbbra is szükség van emberi kontrollra. Összességében a stresszteszt automatizálása hozzásegít egy céget egy minőségi és stabil szoftver fejlesztéséhez, mindezt alacsony erőforrás felhasználás mellett.

Reconstruction of 3D scenes from 2D images is a technical challenge that impacts domains from Earth and planetary sciences and space exploration to augmented and virtual reality. Typically, reconstruction algorithms first identify common features across images and then minimize reconstruction errors after estimating the shape of the terrain. This bundle adjustment (BA) step optimizes around a single, simplifying scalar value that obfuscates many possible causes of reconstruction errors (e.g., initial estimate of the position and orientation of the camera, lighting conditions, ease of feature detection in the terrain). Reconstruction errors can lead to inaccurate scientific inferences or endanger a spacecraft exploring a remote environment. To address this challenge, we present VECTOR, a visual analysis tool that improves error inspection for stereo reconstruction BA. VECTOR provides analysts with previously unavailable visibility into feature locations, camera pose, and computed 3D points. VECTOR was developed in partnership with the Perseverance Mars Rover and Ingenuity Mars Helicopter terrain reconstruction team at the NASA Jet Propulsion Laboratory. We report on how this tool was used to debug and improve terrain reconstruction for the Mars 2020 mission.

Maqolada “Tarixi anbiyo va hukamo” asari haqida fikr yuritiladi. Asardagi ulul-azm deb ta’riflangan payg‘ambarlar bilan bog‘liq hikoyatlar tahlil qilinadi.

Canada’s public health (PH) systems are vulnerable to constant system and structural changes, influenced by political and economic factors. This rapid review examines how PH system restructuring impacts population health outcomes, with special consideration of health equity. Due to a lack of Canadian evidence, international research was examined to produce recommendations for Canadian nurses, researchers, and decision-makers. Evidence indicates that PH spending and PH system organization have important impacts on population health outcomes and suggests PH reform has a negative impact on health equity. Opportunities for advocacy, activism, lobbying and capacity building to achieve health equity are discussed. Nurses, in a unique position between public policy and the lives of those they care for, are presented with the opportunity to effect social change through political action and to work across disciplines to address inequities. We encourage researchers and decision-makers to prioritize looking more deeply at the impact of PH reform.

Forecasting human actions and motion trajectories address the problem of predicting what a person is going to do next and how they will perform it. This is crucial in a wide range of applications, such as assisted living and future co-robotic settings. We propose to simultaneously learn actions and action-related human motion dynamics while existing works perform them independently. This paper presents a method to jointly forecast categories of human action and skeletal joint pose, allowing the two tasks to reinforce each other. As a result, our system can predict future actions and the motion trajectories that will result. To achieve this, we define a task of joint action classification and pose regression. We employ a sequence to sequence encoder-decoder model combined with multi-task learning to forecast future actions and poses progressively before the action happens. Experimental results on two public datasets, IkeaDB and OAD, demonstrate the effectiveness of the proposed method.

This scholarly inquiry employs currere and collaborative ethnography to examine an emerging response to the question, what is education? Three doctoral co-researchers, given a short time limit to respond, found a method to research, discuss, analyze and synthesize reflecting on various philosophies of education. Collaborative ethnography allowed for the doctoral co-researchers to dialogue and discover based on their own perspective while finding new understanding through the others. Currere offered a framework of ongoing reflection on each doctoral co-researchers’ life experience in relation to the inquiry question. The result is an intertwined collaboration of reflection on educational experiences, looking towards the future, analyzing the present moment in inquiry and creating an intriguing synthesis of new understanding.