学习异质治疗效果（HTE）是许多领域的重要问题。大多数现有方法都使用单个治疗组和单个结果指标来考虑设置。但是，在许多现实世界中，实验始终如一 - 例如，在互联网公司中，每天进行A/B测试，以衡量许多感兴趣的不同指标的潜在变化的影响。我们表明，即使一个分析师在一个实验中仅关心HTES来实现一个指标，也可以通过共同分析所有数据来利用交叉实验和交叉结果度量相关性来大大提高精度。我们在张量分解框架中对这个想法进行形式化，并提出了一个简单且可扩展的模型，我们称之为低级或LR-LR-LERNER。合成数据和实际数据的实验表明，LR-LEARNER可以比独立的HTE估计更精确。

For applications that require processing large amounts of text at inference time, Large Language Models (LLMs) are handicapped by their limited context windows, which are typically 2048 tokens. In-context learning, an emergent phenomenon in LLMs in sizes above a certain parameter threshold, constitutes one significant example because it can only leverage training examples that fit into the context window. Existing efforts to address the context window limitation involve training specialized architectures, which tend to be smaller than the sizes in which in-context learning manifests due to the memory footprint of processing long texts. We present Parallel Context Windows (PCW), a method that alleviates the context window restriction for any off-the-shelf LLM without further training. The key to the approach is to carve a long context into chunks (``windows'') that fit within the architecture, restrict the attention mechanism to apply only within each window, and re-use the positional embeddings among the windows. We test the PCW approach on in-context learning with models that range in size between 750 million and 178 billion parameters, and show substantial improvements for tasks with diverse input and output spaces. Our results motivate further investigation of Parallel Context Windows as a method for applying off-the-shelf LLMs in other settings that require long text sequences.

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.

Hebrew is a Morphological rich language, making its modeling harder than simpler language. Recent developments such as Transformers in general and Bert in particular opened a path for Hebrew models that reach SOTA results, not falling short from other non-MRL languages. We explore the cutting edge in this field performing style transfer, text generation and classification over news articles collected from online archives. Furthermore, the news portals that feed our collective consciousness are an interesting corpus to study, as their analysis and tracing might reveal insights about our society and discourse.

A gradual semantics takes a weighted argumentation framework as input and outputs a final acceptability degree for each argument, with different semantics performing the computation in different manners. In this work, we consider the problem of attack inference. That is, given a gradual semantics, a set of arguments with associated initial weights, and the final desirable acceptability degrees associated with each argument, we seek to determine whether there is a set of attacks on those arguments such that we can obtain these acceptability degrees. The main contribution of our work is to demonstrate that the associated decision problem, i.e., whether a set of attacks can exist which allows the final acceptability degrees to occur for given initial weights, is NP-complete for the weighted h-categoriser and cardinality-based semantics, and is polynomial for the weighted max-based semantics, even for the complete version of the problem (where all initial weights and final acceptability degrees are known). We then briefly discuss how this decision problem can be modified to find the attacks themselves and conclude by examining the partial problem where not all initial weights or final acceptability degrees may be known.

本文介绍了信息性多臂强盗（IMAB）模型，在每个回合中，玩家选择手臂，观察符号，并以符号的自我信息形式获得未观察到的奖励。因此，手臂的预期奖励是产生其符号的源质量函数的香农熵。玩家的目标是最大程度地提高与武器的熵值相关的预期奖励。在假设字母大小是已知的假设下，为IMAB模型提出了两种基于UCB的算法，该算法考虑了插件熵估计器的偏差。第一种算法在熵估计中乐观地纠正了偏置项。第二算法依赖于数据依赖性置信区间，该置信区间适应具有较小熵值的源。性能保证是通过上限为每种算法的预期遗憾提供的。此外，在Bernoulli案例中，将这些算法的渐近行为与伪遗憾的Lai-Robbins的下限进行了比较。此外，在假设\ textit {cract}字母大小的假设下是未知的，而播放器仅知道其上方的宽度上限，提出了一种基于UCB的算法，在其中，玩家的目的是减少由该算法造成的遗憾。未知的字母尺寸在有限的时间方面。数字结果说明了论文中介绍的算法的预期遗憾。

这项研究提出了一种检测近距离红外（NIR）眼周眼图像的酒精消耗的方法。该研究的重点是确定外部因素（例如酒精对中枢神经系统（CNS））的影响。目的是分析这如何影响虹膜和学生运动，以及是否可以使用标准的Iris NIR相机捕获这些更改。本文提出了一个新型的融合胶囊网络（F-CAPSNET），以对饮酒受试者拍摄的虹膜NIR图像进行分类。结果表明，使用一半参数作为标准胶囊网络算法，F-CAPSNET算法可以检测IRIS NIR图像中的酒精消耗，精度为92.3％。这项工作是开发自动系统以估计“适合值班”并防止因饮酒而导致事故的一步。

联合学习（FL）是使用Edge设备上可能可用的私人数据训练机器学习模型的新兴范式。 FL的分布式操作引起了集中式机器学习中未遇到的挑战，包括需要保留本地数据集的隐私以及由于重复交换更新模型而导致的通信负载。这些挑战通常通过引起更新模型的某些失真的技术来单独解决，例如当地差异隐私（LDP）机制和有损压缩。在这项工作中，我们提出了一种方法创造的联合隐私增强和量化（JOPEQ），该隐私和量化共同实现了FL环境中的有损压缩和隐私增强。特别是，Jopeq利用基于随机晶格的矢量量化，这是一种通用压缩技术，其副产品失真在统计学上等同于加性噪声。通过使用专用的多元隐私保护噪声来增强模型更新，可以利用这种失真来增强隐私。我们表明，JOPEQ在持有所需的隐私级别的同时，根据所需的比特率同时量化数据，而不会特别影响学习模型的实用性。这是通过分析的LDP保证，失真和收敛范围的推导以及数值研究所示的。最后，我们从经验上断言，乔普克（Jopeq）拆除了已知的普通攻击，以利用隐私泄漏。

变异的计算与差异几何形状结合在一起，作为模型和解决图像处理和计算机视觉问题的工具，在20世纪后期和90年代后期引入了。在这些方向上的广泛作品的开始是由大地测量轮廓（GAC），Beltrami框架，Osher和Sethian的水平设置方法等作品标记的。陈和兽医的作品仅举几例。在许多情况下，这些功能的优化是通过梯度下降方法通过计算Euler-Lagrange方程来完成的。在梯度下降方案中直接使用所得的EL方程会导致非几何，在某些情况下，非感觉方程式。为了获得几何和/或感觉方程式，修改这些EL方程甚至功能本身是成本的。本注释的目的是指出得出EL和梯度下降方程的正确方法，以使所得的梯度下降方程是几何的，并且是有道理的。

在这项工作中，我们提出了一个端到端双耳语音合成系统，该系统将低抑制音频编解码器与强大的双耳解码器结合在一起，该解码器能够准确地进行语音双耳化，同时忠实地重建环境因素，例如环境噪声或混响。该网络是经过修改的矢量定量变异自动编码器，经过训练，采用了几个精心设计的目标，包括对抗性损失。我们在具有客观指标和感知研究的内部双耳数据集上评估了所提出的系统。结果表明，所提出的方法比以前的方法更接近地面真相数据。特别是，我们证明了对抗性损失在捕获创建真实听觉场景所需的环境效果中的能力。