In less than a year-and-a-half, Frankie Johnson, a man incarcerated at the William E Donaldson prison outside Birmingham, Alabama, says he was stabbed around 20 times. In December of 2019, Johnson says, he was stabbed "at least nine times" in his housing unit. In March of 2020, an officer handcuffed him to a desk following a group therapy meeting, and left the unit, after which another prisoner came in and stabbed him five times. In November of the same year, Johnson says, he was handcuffed by an officer and brought to the prison yard, where another prisoner attacked him with an ice pick, stabbing him "five to six times", as two correctional officers looked on. According to Johnson, one of the officers had actually encouraged his attacker to carry out the assault in retaliation for a previous argument between Johnson and the officer.

In Gaza, the sound of drones can be heard everywhere. An analysis by Al Jazeera's digital investigations team, Sanad, has revealed that Israel is repurposing commercial drones to use as weapons of war in the Strip. And as drones become ever more accessible, the line between their civilian use and their military use is becoming increasingly blurred.

A.1 Multimodal Large Language Models Building on the foundational capabilities of groundbreaking Large Language Models (LLMs) such as GPT [3], PALM [6], Mistral [49], and LLama [108], which excel in language understanding and reasoning, recent innovations have integrated these models with other modalities (especially vision), leading to the development of Multimodal Large Language Models (MLLMs). These advanced MLLMs combine and process visual and textual data, demonstrating enhanced versatility in addressing both traditional vision tasks [21, 40, 42, 133] and complex multimodal challenges [34, 70, 136]. Among all MLLMs, proprietary models consistently perform well. OpenAI's GPT-4-Vision [82] pioneered this space by adeptly handling both text and image content. Anthropic's Claude 3 series [7] integrates advanced vision capabilities and multilingual support, enhancing its application across diverse cognitive and real-time tasks.

We only consider the feasible cases. Then we consider the sigm(...) function in Equation 1. For instance, say x is the parameter to be trained, we want x to satisfy sigm(x) = 1 or 0, we would apply gradient-descent which will cause x + / . Two basic blocks of fully connected + ReLU layer are utilized as backbone for all benchmark datasets, generating instance-level representations with a dimensionality of 512. We set one branch here for the binary-classification problems.

Neural Information Processing Systems http://nips.cc/

Having reviewed dozens of drones of all shapes, sizes, and prices, I'd recently come to the conclusion that smaller, lighter, and cheaper drones were the way to go for 90 percent of consumers. But then DJI launched its new premium-priced, jumbo-size flagship consumer drone, the Mavic 4 Pro, and made me fall in love all over again. Yes, this drone is seriously impressive. But before I deep-dive the phenomenally good camera and ridiculously long range, it's important to note that the Mavic 4 Pro will not be officially available in the US. As well as ongoing issues around flight restrictions and security, a DJI spokesperson told WIRED, "Like many global companies, we have had to adjust our market strategy as local conditions and the industry environment have evolved. While we do not have a timeline for when we can introduce the product to the US market, we are closely monitoring the situation and actively exploring every possible solution."

We thank all reviewers for their valuable comments. We address the concerns raised by them below. Reviewer-2 The novelty is incremental. Reviewer-1 The idea of using imitation learning to make approximate decisions is not new. Reviewer-1 The experiments are superficial.

We present a new algorithm for stochastic variational inference that targets at models with non-differentiable densities. One of the key challenges in stochastic variational inference is to come up with a low-variance estimator of the gradient of a variational objective. We tackle the challenge by generalizing the reparameterization trick, one of the most effective techniques for addressing the variance issue for differentiable models, so that the trick works for non-differentiable models as well. Our algorithm splits the space of latent variables into regions where the density of the variables is differentiable, and their boundaries where the density may fail to be differentiable. For each differentiable region, the algorithm applies the standard reparameterization trick and estimates the gradient restricted to the region. For each potentially non-differentiable boundary, it uses a form of manifold sampling and computes the direction for variational parameters that, if followed, would increase the boundary's contribution to the variational objective. The sum of all the estimates becomes the gradient estimate of our algorithm. Our estimator enjoys the reduced variance of the reparameterization gradient while remaining unbiased even for non-differentiable models. The experiments with our preliminary implementation confirm the benefit of reduced variance and unbiasedness.