虽然深层学习方法在多个计算机视觉任务中见证了广泛的成功，但代表自然图像的最新方法不一定在其他域中的图像（例如绘画，漫画和草图）上表现良好。这是因为与自然图像相比，数据分布的分布发生了巨大变化。像草图这样的域通常包含稀疏的信息像素。但是，识别此类域中的对象至关重要，给定多个相关的应用程序利用了此类数据，例如草图以图像检索。因此，实现可以在多个领域中表现出色的嵌入学习模型不仅具有挑战性，而且在计算机视觉中起着关键作用。为此，在本文中，我们提出了一种新颖的嵌入学习方法，目的是跨不同领域概括。在训练过程中，鉴于来自域中的查询图像，我们采用封闭式的融合和注意力来产生一个积极的例子，该示例具有广泛的查询对象类别语义（来自多个域）的语义概念。凭借对比度学习，我们将查询和积极的嵌入方式汲取，以学习在跨领域稳健的表示形式。同时，要教导该模型对来自不同语义类别（跨域）的示例进行歧视，我们还维护了负嵌入（来自不同类别）的池。我们在流行的PAC（照片，艺术绘画，卡通和草图）数据集上使用域床框架展示了我们方法的实力。

在本文中，我们解决了时尚电子商务（关于客户经验以及收入）的重要问题：颜色变体识别，即识别完全在其设计（或风格）中匹配的时尚产品，但仅限于不同的颜色。我们提出了一个通用的框架，它利用了深度视觉表现在其心中学习，以解决我们的时尚电子商务平台的问题。我们的框架可以通过手动获得的三胞胎形式的监控信号培训。但是，在时尚电子商务平台（例如我们的时尚电子商务平台）中，可以获得通常存在的整个大量数据的手动注释是不可行的。但是，对于我们的救援，有趣的是，我们观察到时尚电子商务中的这种关键问题也可以通过简单的彩色抖动的图像增强来解决，最近在对比的自我监督学习（SSL）文学中广泛欢迎，这是旨在的在不使用手动标签的情况下学习可视表示。这自然导致了我们思想的一个问题：我们可以利用我们的用例中的SSL，仍然对我们的监督框架获得了可比的表现吗？答案是，是的！因为，颜色变体时尚对象只不过是风格的表现，以不同的颜色，以及培训的模型，培训不变于颜色（有或没有监督），应该能够识别出来！这是本文进一步证明的，既有质量和定量，同时评估几种最先进的SSL技术，也提出了一种新方法。

尽管最先进的对象探测器改进了巨大的改进，但在夜间中的解决物体检测已经稀疏地进行了稀疏地研究，即通过有限的可用纸之间的非均匀评估协议进行了稀疏地进行了稀疏。除了缺乏解决这个问题的方法外，还缺乏足够大的基准数据集来学习夜间对象检测。最近，介绍了大规模的BDD100K，在我们看来，应该被选为基准，在这一领域Keystart Resse。现在，通过方法，现有方法（数量有限），主要是基于生成图像转换的，或者图像增强/照明，这两个都不是自然的，符合人类如何在夜间看到物体（通过专注于对象轮廓）。在本文中，我们弥合了这3个间隙：1。缺乏均匀的评估协议（使用单级探测器，由于其功效和效率），2.数据集选择用于基准测试夜间对象检测，3 。一种解决当前替代品局限性的新方法。我们的方法利用基于对比的学习特征提取器，通过傅里叶变换从频域借用信息，并以持续的基于学习的方式训练。用于对象检测时的学习功能（在微调分类和回归层后），有助于实现新的最先进的经验性能，舒适地优于广泛数量的竞争对手。

Machine Translation (MT) system generally aims at automatic representation of source language into target language retaining the originality of context using various Natural Language Processing (NLP) techniques. Among various NLP methods, Statistical Machine Translation(SMT). SMT uses probabilistic and statistical techniques to analyze information and conversion. This paper canvasses about the development of bilingual SMT models for translating English to fifteen low-resource Indian Languages (ILs) and vice versa. At the outset, all 15 languages are briefed with a short description related to our experimental need. Further, a detailed analysis of Samanantar and OPUS dataset for model building, along with standard benchmark dataset (Flores-200) for fine-tuning and testing, is done as a part of our experiment. Different preprocessing approaches are proposed in this paper to handle the noise of the dataset. To create the system, MOSES open-source SMT toolkit is explored. Distance reordering is utilized with the aim to understand the rules of grammar and context-dependent adjustments through a phrase reordering categorization framework. In our experiment, the quality of the translation is evaluated using standard metrics such as BLEU, METEOR, and RIBES

Deep neural networks have emerged as the workhorse for a large section of robotics and control applications, especially as models for dynamical systems. Such data-driven models are in turn used for designing and verifying autonomous systems. This is particularly useful in modeling medical systems where data can be leveraged to individualize treatment. In safety-critical applications, it is important that the data-driven model is conformant to established knowledge from the natural sciences. Such knowledge is often available or can often be distilled into a (possibly black-box) model $M$. For instance, the unicycle model for an F1 racing car. In this light, we consider the following problem - given a model $M$ and state transition dataset, we wish to best approximate the system model while being bounded distance away from $M$. We propose a method to guarantee this conformance. Our first step is to distill the dataset into few representative samples called memories, using the idea of a growing neural gas. Next, using these memories we partition the state space into disjoint subsets and compute bounds that should be respected by the neural network, when the input is drawn from a particular subset. This serves as a symbolic wrapper for guaranteed conformance. We argue theoretically that this only leads to bounded increase in approximation error; which can be controlled by increasing the number of memories. We experimentally show that on three case studies (Car Model, Drones, and Artificial Pancreas), our constrained neurosymbolic models conform to specified $M$ models (each encoding various constraints) with order-of-magnitude improvements compared to the augmented Lagrangian and vanilla training methods.

This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with cooling Google's data centers more efficiently, we recently conducted live experiments on two real-world facilities in partnership with Trane Technologies, a building management system provider. These live experiments had a variety of challenges in areas such as evaluation, learning from offline data, and constraint satisfaction. Our paper describes these challenges in the hope that awareness of them will benefit future applied RL work. We also describe the way we adapted our RL system to deal with these challenges, resulting in energy savings of approximately 9% and 13% respectively at the two live experiment sites.

Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.

The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized.

Existing regulations prohibit model developers from accessing protected attributes (gender, race, etc.), often resulting in fairness assessments on populations without knowing their protected groups. In such scenarios, institutions often adopt a separation between the model developers (who train models with no access to the protected attributes) and a compliance team (who may have access to the entire dataset for auditing purpose). However, the model developers might be allowed to test their models for bias by querying the compliance team for group fairness metrics. In this paper, we first demonstrate that simply querying for fairness metrics, such as statistical parity and equalized odds can leak the protected attributes of individuals to the model developers. We demonstrate that there always exist strategies by which the model developers can identify the protected attribute of a targeted individual in the test dataset from just a single query. In particular, we show that one can reconstruct the protected attributes of all the individuals from O(Nk log n/Nk) queries when Nk<<n using techniques from compressed sensing (n: size of the test dataset, Nk: size of smallest group). Our results pose an interesting debate in algorithmic fairness: should querying for fairness metrics be viewed as a neutral-valued solution to ensure compliance with regulations? Or, does it constitute a violation of regulations and privacy if the number of queries answered is enough for the model developers to identify the protected attributes of specific individuals? To address this supposed violation, we also propose Attribute-Conceal, a novel technique that achieves differential privacy by calibrating noise to the smooth sensitivity of our bias query, outperforming naive techniques such as Laplace mechanism. We also include experimental results on the Adult dataset and synthetic data (broad range of parameters).

已经开发了增强学习（RL）技术来优化工业冷却系统，与传统的启发式政策相比，提供了可观的节能。工业控制中的一个主要挑战涉及由于机械限制而在现实世界中可行的学习行为。例如，某些操作只能每隔几个小时执行一次，而其他动作可以更频繁地采取。如果没有广泛的奖励工程和实验，RL代理可能无法学习机械的现实操作。为了解决这个问题，我们使用层次结构的增强学习与多种根据操作时间尺度控制动作子集的代理。我们的分层方法可以在现有基线上节省能源，同时在模拟的HVAC控制环境中保持在安全范围内的限制（例如操作冷却器）。