Rob Reiner's son Jake shares horrific new details from night of his parents' murders and says it is'almost impossible to process' that his brother Nick has been charged with the killings Bloodbath on the streets as millions of dogs are'massacred' by firing squad ahead of the World Cup Tucker Carlson's secret heiress sister reveals bitter feud over family fortune: He says'I don't know her'... but trove of photos tells a very different story Lesbian sex secrets of Kristi Noem's ICE leader: Ex lover claims jealous rages over men, screaming through hotel walls... and vile tight bodysuit demand Hidden cameras at NYC's live animal markets expose filthy conditions, disease risks, and brutal treatment of chickens, ducks, rabbits and sheep MAUREEN CALLAHAN: Dark indisputable Michael Jackson truths Hollywood STILL covers up. His own daughter reportedly now thinks he was a pedophile, so why's this so hard to say? Scandal after high-ranking female prison officer gave birth to twins... as shocking rumor spreads about identity of their father My senior government source has told me why these scientists may REALLY be going missing. This is so serious even the President is being kept on a'need-to-know basis': KENNEDY Former NFL quarterback Tim Tebow announces tragic news of dad's death after battle with Parkinson's in heartbreaking post Reclusive Athina Onassis, heiress to $2.7billion fortune who stepped away from public life after humiliating heartbreak, breaks cover at Barcelona Bridal Week in rare public appearance Sam's Club just launched a perk that targets Costco's biggest flaw Disappointed customers reveal the most'overrated' chain restaurants... do YOU have good taste? Woke author who boasted about shoplifting from Whole Foods flies into foul-mouthed RAGE when confronted outside her $2.2m Brooklyn brownstone Sherrone Moore's ex-mistress reveals pregnancy as she details night fired Michigan coach came to her apartment Troubling past of'father of the year' who murdered son, 11, in airport bathroom... as grieving grandpa reveals warning sign that something awful was about to happen US threatens to'review' UK claim to Falklands Islands and ban Spain from NATO as punishment for failure to back Iran War'Chemical-spraying' drones reportedly stolen from New Jersey facility sparks fears of'nightmare scenario' An alarm has erupted after 15 powerful agricultural spray drones were stolen in a suspected coordinated heist in New Jersey last month. A report from The High Side claimed the FBI is investigating the theft amid fears the machines could be used to disperse dangerous materials.

As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework that addresses when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with the many examples that have been presented, leading some to suggest that the notion of harm should be abandoned and "replaced by more well-behaved notions". As harm is generally something that is caused, most of these definitions have involved causality at some level. Yet surprisingly, none of them makes use of causal models and the definitions of actual causality that they can express. In this paper we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality [13]. The key features of our definition are that it is based on contrastive causation and uses a default utility to which the utility of actual outcomes is compared. We show that our definition is able to handle the examples from the literature, and illustrate its importance for reasoning about situations involving autonomous systems.

Licence for the codes We use the code for MS-TCN [13], ASRF [24], LAS [9], all of which are under MITLicense according to https://opensource.org/licenses/MIT. For the Jigsaws [18] dataset, we follow the data use agreeement according to https://cs.jhu. Action classification: Action classification is the task of identifying a single action, as opposed to a sequence of actions. Several methods use 2DCNNs to extract frame-wise features from an input video, which are then combined to predict a coarse action taking place in the video [56, 39, 59]. There also exist several works that perform action classification from kinematic data [2, 12]. Action segmentation: Action segmentation is the problem of segmenting an input stream of data, labeling each frame according to the action that is being carried out. Earlier methods for action segmentation employed hidden Markov models [33, 22]. More recently, convolutional neural networks [58, 26] and recurrent neural networks [50] have been applied to this problem Inspired by the success of temporal convolutional networks (TCNs) in speech synthesis, [37] adapted these models to action segmentation. MS-TCN [13], which uses a multi-stage TCN architecture, has become one of the most widely used architecture for action segmentation. Although these methods achieve high frame-wise accuracy, they still produce a significant number of over-segmentation errors. In order to address this, several boundary-aware methods have been developed which perform temporal smoothing of the frame-wise predictions [57, 24]. These methods use ground-truth boundary information to train a binary classification network to perform boundary detection. The boundary estimates are then used to aggregate the frame-wise predictions either in a soft manner (boundary-aware pooling) or by setting a hard threshold. However, for elemental actions with a short duration, such as the functional primitives in the StrokeRehab dataset, the duration of each action is very short. As a result, the boundaries between actions can be hard to detect or even hard to define (see Figure 4). Sequence-to-sequence models: Our proposed method is based on sequence-to-sequence (seq2seq) models. These models allow us to learn a mapping of a variable-length input sequence to a variablelength output sequence [53].

Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy. Nevertheless, it faces challenges with limited high-quality labels and non-IID client data, particularly in applications like autonomous driving. To address these hurdles, we navigate the uncharted waters of Semi-Supervised Federated Object Detection (SSFOD). We present a pioneering SSFOD framework, designed for scenarios where labeled data reside only at the server while clients possess unlabeled data. Notably, our method represents the inaugural implementation of SSFOD for clients with 0% labeled non-IID data, a stark contrast to previous studies that maintain some subset of labels at each client. We propose FedSTO, a two-stage strategy encompassing Selective Training followed by Orthogonally enhanced full-parameter training, to effectively address data shift (e.g.

For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? As surveillance cameras become prevalent in public spaces, using them has proven effective in proactively deterring and preventing such incidents. However, the data collected by these cameras could potentially lead to breaches in privacy for those being filmed. Thus, we hope to find a way to capture scenes of violence while avoiding infringement on personal privacy. DVS cameras can naturally achieve this goal by capturing events of pixel brightness changes. Existing violence detection datasets are filmed with RGB cameras, which cannot ensure privacy preserving.