Not only have deep networks become standard in machine learning, they are increasingly of interest in neuroscience as models of cortical computation that capture relationships between structural and functional properties. In addition they are a useful target of theoretical research into the properties of network computation. Deep networks typically have a serial or approximately serial organization across layers, and this is often mirrored in models that purport to represent computation in mammalian brains. There are, however, multiple examples of parallel pathways in mammalian brains. In some cases, such as the mouse, the entire visual system appears arranged in a largely parallel, rather than serial fashion. While these pathways may be formed by differing cost functions that drive different computations, here we present a new mathematical analysis of learning dynamics in networks that have parallel computational pathways driven by the same cost function. We use the approximation of deep linear networks with large hidden layer sizes to show that, as the depth of the parallel pathways increases, different features of the training set (defined by the singular values of the input-output correlation) will typically concentrate in one of the pathways. This result is derived analytically and demonstrated with numerical simulation. Thus, rather than sharing stimulus and task features across multiple pathways, parallel network architectures learn to produce sharply diversified representations with specialized and specific pathways, a mechanism which may hold important consequences for codes in both biological and artificial systems.

In this paper, we propose a novel text promptable surgical instrument segmentation approach to overcome challenges associated with diversity and differentiation of surgical instruments in minimally invasive surgeries. We redefine the task as text promptable, thereby enabling a more nuanced comprehension of surgical instruments and adaptability to new instrument types. Inspired by recent advancements in vision-language models, we leverage pretrained image and text encoders as our model backbone and design a text promptable mask decoder consisting of attention-and convolution-based prompting schemes for surgical instrument segmentation prediction. Our model leverages multiple text prompts for each surgical instrument through a new mixture of prompts mechanism, resulting in enhanced segmentation performance. Additionally, we introduce a hard instrument area reinforcement module to improve image feature comprehension and segmentation precision. Extensive experiments on several surgical instrument segmentation datasets demonstrate our model's superior performance and promising generalization capability. To our knowledge, this is the first implementation of a promptable approach to surgical instrument segmentation, offering significant potential for practical application in the field of robotic-assisted surgery.

Deep neural networks (DNNs) typically require massive data to train on, which is a hurdle for numerous practical domains. Facing the data shortfall, one viable option is to acquire domain-specific training data from external uncensored sources, such as open webs or third-party data collectors. However, the quality of such acquired data is often not rigorously scrutinized, and one cannot easily rule out the risk of `poisoned examples being included in such unreliable datasets, resulting in unreliable trained models which pose potential risks to many high-stake applications. While existing options usually suffer from high computational costs or assumptions on clean data access, this paper attempts to detect backdoors for potential victim models with minimal prior knowledge. In particular, provided with a trained model, users are assumed to (1) have no prior knowledge of whether it is already poisoned, or what the target class/percentage of samples is poisoned, and (2) have no access to a clean sample set from the same training distribution, nor any trusted model trained on such clean data. To tackle this challenging scenario, we first observe the contrasting channel-level statistics between the backdoor trigger and clean image features, and consequently, how they can be differentiated by progressive channel shuffling. We then propose the randomized channel shuffling method for backdoor-targeted class detection, which requires only a few feed-forward passes.

Many recent studies have employed task-based modeling with recurrent neural networks (RNNs) to infer the computational function of different brain regions. These models are often assessed by quantitatively comparing the low-dimensional neural dynamics of the model and the brain, for example using canonical correlation analysis (CCA). However, the nature of the detailed neurobiological inferences one can draw from such efforts remains elusive. For example, to what extent does training neural networks to solve simple tasks, prevalent in neuroscientific studies, uniquely determine the low-dimensional dynamics independent of neural architectures? Or alternatively, are the learned dynamics highly sensitive to different neural architectures?

Why is Israel still in southern Lebanon? A war to shape Lebanon's future Two people have been killed in an Israeli drone strike on a minibus in eastern Lebanon as near-daily ceasefire violations continue, Lebanese state media reported. Lebanon's National News Agency (NNA) said on Thursday that the drone hit the vehicle on the Hosh al-Sayyed Ali road in the Hermel district. Israeli military spokesperson Avichay Adraee claimed on X that Thursday's strike targeted a "terrorist operative" in al-Nasiriyah in eastern Lebanon. The attack came hours after a passerby was injured in an Israeli drone strike targeting a car in the town of Jennata in the Tyre district of southern Lebanon late on Wednesday.

In this paper, we present a novel problem, namely video timeline modeling. Our objective is to create a video-associated timeline from a set of videos related to a specific topic, thereby facilitating the content and structure understanding of the story being told. This problem has significant potential in various real-world applications, for instance, news story summarization.

The vulnerability of deep neural networks has been widely found in various models as well as tasks where slight perturbations on the inputs could lead to incorrect predictions. These perturbed inputs are known as adversarial examples and one of the intriguing properties of them is Adversarial Transfersability, i.e. the capability of adversarial examples to fool other models. Traditionally, this transferability is always regarded as a critical threat to the defense against adversarial attacks, however, we argue that the network robustness can be significantly boosted by utilizing adversarial transferability from a new perspective. In this work, we first discuss the influence of different popular normalization layers on the adversarial transferability, and then provide both empirical evidence and theoretical analysis to shed light on the relationship between normalization types and transferability. Based on our theoretical analysis, we propose a simple yet effective module named Random Normalization Aggregation (RNA) which replaces the batch normalization layers in the networks and aggregates different selected normalization types to form a huge random space. Specifically, a random path is sampled during each inference procedure so that the network itself can be treated as an ensemble of a wide range of different models. Since the entire random space is designed with low adversarial transferability, it is difficult to perform effective attacks even when the network parameters are accessible. We conduct extensive experiments on various models and datasets, and demonstrate the strong superiority of proposed algorithm.

A formidable challenge in multi-label learning is to model the interdependencies between labels and features. Unfortunately, the statistical properties of existing multi-label dependency modelings are still not well understood. Copulas are a powerful tool for modeling dependence of multivariate data, and achieve great success in a wide range of applications, such as finance, econometrics and systems neuroscience. This inspires us to develop a novel copula multi-label learning paradigm for modeling label and feature dependencies. The copula based paradigm enables to reveal new statistical insights in multi-label learning. In particular, the paper first leverages the kernel trick to construct continuous distribution in the output space, and then estimates our proposed model semiparametrically where the copula is modeled parametrically, while the marginal distributions are modeled nonparametrically. Theoretically, we show that our estimator is an unbiased and consistent estimator and follows asymptotically a normal distribution. Moreover, we bound the mean squared error of estimator. The experimental results from various domains validate the superiority of our proposed approach.

This year, the right-to-repair movement got a boost from--surprisingly--big tech, tariffs, and economic downturn. It has been a big year for the right to repair, the movement of advocates pushing for people to be able to fix their own electronics and equipment without manufacturer approval. The issue has gathered broad support from technologists, farmers, military leaders, and politicians on both sides of the aisle. It is popular with just about everyone--except the companies who stand to gain if the parts, instructions, and tools necessary to fix their products remain under lock and key. Three US states passed right-to-repair laws this year, including in heavily Republican states like Texas where the measure received a unanimous vote in both the House and Senate.

What Kind of New World Is Being Born? According to the Gospel of Luke, the Virgin Mary first learns that she'll soon give birth to Christ when she gets an unsolicited visit from an angel. Nice messenger service if you can get it. But before trusty Gabriel can dispense the good news upon which Christmas depends he has to calm the girl down. "Fear not," he says, and, in a way, this sombre reassurance is the Yuletide message in drastic miniature.