You're viewing the US edition You can switch to the UK, AU or IE homepage at any time using this menu. Transgender New York Times executive who was shot dead'by his in-laws' abused BOTH his young children and left them with head injuries, wife claims Female death row inmate set to be executed TOMORROW reveals'kinship' with Lindsay Clancy Madonna's family make intervention: After forgetful and embarrassing VMAs comeback, insiders reveal clash between'control freak' star and her team Nobody wants to say this about Cindy Crawford after her son Presley Gerber's death... but maybe someone should: MAUREEN CALLAHAN Taylor Swift's unreleased suicide note song: Tracks the world was never meant to hear... including one her co-writer said is'the most chilling he's ever heard' Trump's Canadian whiskey ban hits Americans TODAY as trade war escalates - but president faces brutal warning from US experts I see so many male patients whose sleep is ruined by constantly needing to pee at night. What many don't realise is that they don't have a bladder problem... but a curable breathing issue: DR PHILIPPA KAYE'Presley never felt good enough. It bothered him enormously': Modeling agency owner tells ROB SHUTER the nasty industry rumor that haunted Gerber as a teenager'What next, put food on plates?!': Meghan mocked over latest hosting tips advising hosts to'have as much prepped as possible before guests arrive' Married blonde psychiatrist's sessions with teen patient ended in forbidden sex: Naked selfies, explicit messages... and how she showered him with gifts right under lawyer husband's nose Candace Owens in crisis: As family begs her to'disappear' and prays she isn't KILLED, insiders reveal whispers of a foreign reconnaissance mission... and her links to secret military location My son took his own life at 24, months before his wedding. I felt him leave from miles away... then realised the signs were there during our final Mother's Day together Prince Harry'wants to reconcile with William' but 30th anniversary of Diana's death'won't make any difference' in healing their rift, Omid Scobie claims Truth behind Kevin Costner's rotation of blondes: Christine Baumgartner split left him so'burned', there was only one way to revive his spirit... now, insiders spill on his daring next steps Mortifying restaurant humiliation for Mica Miller's pastor husband John-Paul during date night with wife... before a VERY petty TripAdvisor review appeared I was a lifelong atheist... until I saw proof God is real.

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Property-based testing (PBT) is a lightweight formal method, typically implemented as a randomized testing framework. Users specify the input domain for their test using combinators supplied by the PBT framework, and the expected properties or invariants as a unit-test function. The framework then searches for a counterexample, e.g. by generating inputs and calling the test function. In this work, we demonstrate an LLM-based agent which analyzes Python modules, infers function-specific and cross-function properties from code and documentation, synthesizes and executes PBTs, reflects on outputs of these tests to confirm true bugs, and finally outputs actionable bug reports for the developer. We perform an extensive evaluation of our agent across 100 popular Python packages. Of the bug reports generated by the agent, we found after manual review that 56\% were valid bugs and 32\% were valid bugs that we would report to maintainers. We then developed a ranking rubric to surface high-priority valid bugs to developers, and found that of the 21 top-scoring bugs, 86\% were valid and 81\% we would report. The bugs span diverse failure modes from serialization failures to numerical precision errors to flawed cache implementations. We reported 5 bugs, 4 with patches, including to NumPy and cloud computing SDKs, with 3 patches merged successfully. Our results suggest that LLMs with PBT provides a rigorous and scalable method for autonomously testing software. Our code and artifacts are available at: https://github.com/mmaaz-git/agentic-pbt.

'Four dead and 12 injured' in Mississippi shooting after people descend on town for homecoming game Joe Biden, 82, receiving new treatment after'aggressive' cancer spread to his bones REVEALED: The secret George Soros network'behind America's street chaos'... and the dossier that shows how to stop it Tinnitus destroyed Peter's life but doctors dismissed him. Then he tried an extraordinary drug-free University of Cambridge-backed treatment that gives instant relief - no wonder medics say it's so'exciting' KENNEDY: Obama's bitter post about Trump's Gaza peace deal proves what I've long suspected about Barry... and it would make Sigmund Freud blush Gold is soaring... here's what the pros say you should do with your 401(k) before it's too late Model dubbed'the world's most beautiful girl' when she was six is now all grown up and looks VERY different as she poses up a storm at Paris Fashion Week Teacher was'so high on cocaine she thought one of her students was her dog' But now, a royal insider claims they're'just as entitled as their parents' with'shady friends' Heartbreaking moment NFL reporter makes brutal comment about player Xavier Legette's dead father in locker room interview Experts reveal the surprising TRUTH behind RFK Jr's link between circumcision and autism Bombshell records that damn Letitia James and show Trump was RIGHT... and the staggering sum she was swindling Trump starts DOGE 2.0 as mass layoffs take place across federal government amid shutdown Famed'Big Short' investor gives terrifying verdict on Trump hammering China with 100 PERCENT tariff... and issues doomsday warning to Wall Street Jennifer Aniston, you've betrayed every woman with your selfish admission about not having children: CAROLINE BULLOCK Meteorologist's stark warning to Americans to brace for a harsh winter with less snow but more nor'easters Meteorologists are already predicting what the winter months will bring, with some regions of the US expected to see less snow than last year, and nor'easters anticipated to ravage parts of the Northeast. Paul Pastelok, chief meteorologist for AccuWeather's long-range forecasting team, told the Daily Mail that while he didn't expect above normal snowfall for the winter season, he warned that those in the Northeast should brace for nor'easters and it would still be a harsh winter. Pastelok explained that the nor'easter over this weekend is on trend with what is to come, as rapidly developing storms come in off the East Coast. 'People may say, Well, you're forecasting less snow, so it doesn't look like a harsh winter.

Recently, large language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, controllable summarization with LLMs remains underexplored, limiting their ability to generate summaries that align with specific user preferences. In this paper, we first investigate the capability of LLMs to control diverse attributes, revealing that they encounter greater challenges with numerical attributes, such as length and extractiveness, compared to linguistic attributes. To address this challenge, we propose a guide-to-explain framework (GTE) for controllable summarization. Our GTE framework enables the model to identify misaligned attributes in the initial draft and guides it in explaining errors in the previous output. Based on this reflection, the model generates a well-adjusted summary. As a result, by allowing the model to reflect on its misalignment, we generate summaries that satisfy the desired attributes in surprisingly fewer iterations than other iterative methods solely using LLMs.

Changing speaker names consistently throughout a dialogue should not affect its meaning and corresponding outputs for text generation from dialogues. However, pre-trained language models, serving as the backbone for dialogue-processing tasks, have shown to be sensitive to nuances. This may result in unfairness in real-world applications. No comprehensive analysis of this problem has been done in the past. In this work, we propose to quantitatively measure a model's sensitivity on speaker names, and comprehensively evaluate a number of known methods for reducing speaker name sensitivity, including a novel approach of our own. Extensive experiments on multiple datasets provide a benchmark for this problem and show the favorable performance of our approach in sensitivity reduction and quality of generation.

Recent progress in deep learning has led to the development of Optical Character Recognition (OCR) systems which perform remarkably well. Most research has been around recurrent networks as well as complex gated layers which make the overall solution complex and difficult to scale. In this paper, we present an Efficient And Scalable TExt Recognizer (EASTER) to perform optical character recognition on both machine printed and handwritten text. Our model utilises 1-D convolutional layers without any recurrence which enables parallel training with considerably less volume of data. We experimented with multiple variations of our architecture and one of the smallest variant (depth and number of parameter wise) performs comparably to RNN based complex choices.

Recent progress in deep learning has led to the development of Optical Character Recognition (OCR) systems which perform remarkably well. Most research has been around recurrent networks as well as complex gated layers which make the overall solution complex and difficult to scale. In this paper, we present an Efficient And Scalable TExt Recognizer (EASTER) to perform optical character recognition on both machine printed and handwritten text. Our model utilises 1-D convolutional layers without any recurrence which enables parallel training with considerably less volume of data. We experimented with multiple variations of our architecture and one of the smallest variant (depth and number of parameter wise) performs comparably to RNN based complex choices. Our 20-layered deepest variant outperforms RNN architectures with a good margin on benchmarking datasets like IIIT-5k and SVT. We also showcase improvements over the current best results on offline handwritten text recognition task. We also present data generation pipelines with augmentation setup to generate synthetic datasets for both handwritten and machine printed text.

Robots, I guess, are really big on Easter. What has led me to this conclusion is the sheer volume of vids that have cropped up this week. So, let's just see how many videos (not all Easter related, by the way) we can cram into one single post. I think the TurtleBots at Clearpath Robotics need to have their dates reset, 'cause they're a wee bit early on the Easter egg hunt: Unknown to the human employees at Clearpath, our robots decided it was high time they partook in the fun of Easter celebrations. Imagine our surprise when we arrived at work to find all available surfaces covered in eggs and an Easter egg hunt in full swing.

You are right that this is a question of forecastability. There have been a few articles on forecastability in the IIF's practitioner-oriented journal Foresight. The problem is that forecastability is already hard to assess in "simple" cases. Suppose you have a time series like this but don't speak German: How would you model the large peak in April, and how would you include this information in any forecasts? Unless you knew that this time series is the sales of eggs in a Swiss supermarket chain, which peaks right before western calendar Easter, you would not have a chance.