Machine learning (ML) systems that guarantee security and privacy often rely on Fully Homomorphic Encryption (FHE) as a cornerstone technique, enabling computations on encrypted data without exposing sensitive information. However, a critical limitation of FHE is its computational inefficiency, making it impractical for large-scale applications. In this work, we propose \textit{Nemesis}, a framework that accelerates FHE-based systems without compromising accuracy or security. The design of Nemesis is inspired by Rache (SIGMOD'23), which introduced a caching mechanism for encrypted integers and scalars. Nemesis extends this idea with more advanced caching techniques and mathematical tools, enabling efficient operations over multi-slot FHE schemes and overcoming Rache's limitations to support general plaintext structures. We formally prove the security of Nemesis under standard cryptographic assumptions and evaluate its performance extensively on widely used datasets, including MNIST, FashionMNIST, and CIFAR-10. Experimental results show that Nemesis significantly reduces the computational overhead of FHE-based ML systems, paving the way for broader adoption of privacy-preserving technologies.

With the prevalence of large-scale pretrained vision-language models (VLMs), such as CLIP, soft-prompt tuning has become a popular method for adapting these models to various downstream tasks. However, few works delve into the inherent properties of learnable soft-prompt vectors, specifically the impact of their norms to the performance of VLMs. This motivates us to pose an unexplored research question: "Do we need to normalize the soft prompts in VLMs?" To fill this research gap, we first uncover a phenomenon, called the Low-Norm Effect by performing extensive corruption experiments, suggesting that reducing the norms of certain learned prompts occasionally enhances the performance of VLMs, while increasing them often degrades it. To harness this effect, we propose a novel method named Normalizing the soft-prompt vectors of vision-language models (Nemesis) to normalize soft-prompt vectors in VLMs. To the best of our knowledge, our work is the first to systematically investigate the role of norms of soft-prompt vector in VLMs, offering valuable insights for future research in soft-prompt tuning. The code is available at https://github.com/ShyFoo/Nemesis. In the age of large-scale pretrained vision-language models (VLMs), such as CLIP (Radford et al., 2021), Flamingo (Alayrac et al., 2022), and BLIP (Li et al., 2022), soft-prompt-based methods, also known as prompt-tuning, have emerged as a dominant approach for adapting these models to a wide range of downstream tasks. For instance, Zhou et al. (2022b) propose a Context Optimization (CoOp) method to learn soft prompts in a continuous space of CLIP for image classification tasks. Additionally, Rao et al. (2022) and Du et al. (2022) also employ prompt-tuning to address dense prediction and open-vocabulary object detection tasks, respectively. Recent research in the field of VLMs has been primarily focused on enhancing model performance through the alignment of visual and textual features. For instance, in (Lu et al., 2022), the weight distribution of output embeddings is estimated, while Zang et al. (2022) propose a joint optimization approach for prompts across multiple modalities.

William Shatner, 'Star Trek' alum and author of'Boldly Go,' spoke to Fox News Digital about his decadeslong friendship with Leonard Nimoy, as well as his iconic on-screen kiss with Nichelle Nichols. "Star Trek" fans can bask in nostalgia, as the cast of the iconic science fiction series has reunited. After more than two decades, "Star Trek: Nemesis" actors, including Gates McFadden, LeVar Burton, Jonathan Frakes and Patrick Stewart, revealed the decision to reprise their famous roles and what it was like working together on the spacecraft again on "Star Trek: Picard." Stewart, who's known for his role as Jean Luc Picard in the "Star Trek" franchise, gave fans a preview of what they can expect in the current series. "Star Trek: Nemesis" actors, including, from left, Jonathan Frakes, Patrick Stewart, Gates McFadden, LeVar Burton and Michael Dorn, reprise their famous roles on "Star Trek: Picard."

The perfect Resident Evil game doesn't exist. The series, among the most consequential in gaming, has shifted its focus so often, a "Resident Evil fan" could be many things. One player's definition of a perfect Resident Evil game is another's mark of where the series went astray. Like the Zelda or Mario series, Resident Evil is due some credit for innovating and becoming an industry leader, even if it eventually began to borrow from its action-adventure peers. Still, there are plenty of ideas that persist through each game.

LONDON – The European Union's competition chief is getting a new term -- with expanded powers -- in a move that underlines how the bloc's battle to regulate big tech companies is only just beginning. Margrethe Vestager, who angered the Trump administration by imposing multibillion-dollar penalties on the likes of Google and Apple, was reappointed Tuesday for a second five-year term as the bloc's competition commissioner. The Danish politician's tasks will include strengthening competition enforcement in all sectors, stepping up efforts to detect cases of market abuse by big companies, speeding up investigations and helping strengthen cooperation with her global counterparts. Perhaps ominously for the big tech companies that she has cracked down on, Vestager is also getting extra clout. Ursula von der Leyen, the incoming president of the EU's powerful executive arm, promoted Vestager to a commission executive vice-president overseeing the EU's digital innovation and leadership efforts, including artificial intelligence.

Ursula von der Leyen, the incoming president of the EU's powerful executive arm, promoted Vestager to a commission executive vice-president overseeing the EU's digital innovation and leadership efforts, including artificial intelligence. "Margarethe Vestager will coordinate the whole agenda and be the commissioner for competition," von der Leyen said at a press conference . Von der Leyen has said that by the end of her first 100 days in office, she wants to draw up legislation for a European approach on the "human and ethical implications" of artificial intelligence. The Computer & Communications Industry Association, a lobby group with members including Google, Facebook and Amazon, reacted cautiously to Vestager's reappointment. "We encourage the new Commissioners to assess the impact of all the recent EU tech regulation to ensure that future legislation will be evidence-based, proportionate and beneficial," it said in a statement.

Walmart-backed Flipkart has just issued a challenge to Amazon's Alexa and Google Assistant. The home-grown e-commerce giant today announced that it has acquired Bengaluru-based artificial intelligence (AI) startup Liv.ai, which has developed a platform that converts speech-to-text in nine regional languages apart from English. With this move, the e-tailer hopes to soon offer an end-to-end conversational shopping experience for its users. "Given the complexities in typing on vernacular keyboards, voice will become a preferred interface for new shoppers. One does understand that building a voice interface is complex, and is especially challenging in Indian context given multiple languages and accents," Flipkart CEO Kalyan Krishnamurthy said in a statement.

CDC has even identified food safety as one of seven "winnable battles"; however, progress to date has been limited. We show that adaptive inspection process is 64 percent more effective at identifying problematic venues than the current state of the art. If fully deployed, our approach could prevent over 9,000 cases of foodborne illness and 557 hospitalizations annually in Las Vegas alone. Additionally, adaptive inspections result in unexpected benefits, including the identification of venues lacking permits, contagious kitchen staff, and fewer customer complaints filed with the Las Vegas health department.

This issue features expanded versions of articles selected from the 2016 AAAI Conference on Innovative Applications of Artificial Intelligence held in Phoenix, Arizona. We present a selection of three articles that describe deployed applications, two articles that discuss work on emerging applications, and an article based on the 2016 Robert S. Engelmore Memorial Lecture.

Foodborne illness afflicts 48 million people annually in the U.S. alone. Over 128,000 are hospitalized and 3,000 die from the infection. While preventable with proper food safety practices, the traditional restaurant inspection process has limited impact given the predictability and low frequency of inspections, and the dynamic nature of the kitchen environment. Despite this reality, the inspection process has remained largely unchanged for decades. CDC has even identified food safety as one of seven ”winnable battles”; however, progress to date has been limited. In this work, we demonstrate significant improvements in food safety by marrying AI and the standard inspection process. We apply machine learning to Twitter data, develop a system that automatically detects venues likely to pose a public health hazard, and demonstrate its efficacy in the Las Vegas metropolitan area in a double-blind experiment conducted over three months in collaboration with Nevada’s health department. By contrast, previous research in this domain has been limited to indirect correlative validation using only aggregate statistics. We show that adaptive inspection process is 64 percent more effective at identifying problematic venues than the current state of the art. If fully deployed, our approach could prevent over 9,000 cases of foodborne illness and 557 hospitalizations annually in Las Vegas alone. Additionally, adaptive inspections result in unexpected benefits, including the identification of venues lacking permits, contagious kitchen staff, and fewer customer complaints filed with the Las Vegas health department.