Diddy FUMBLES as he speaks in public for first time in 13 months and begs his mother's forgiveness through tears Robert Griffin III involved in'scary' car crash with wife and kids as shocking photos emerge Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with I'm a woman with autism... here are the signs you might be masking, even from yourself Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split Body count from Houston's bayous rises as serial killer whispers grip city and residents are told: 'Be vigilant' Realtor with expensive ex-wife arrested over shocking $11.6m claims about how he was funding Palm Beach lifestyle Trump dollar coin design released by Treasury... and it's inspired by the most iconic political photo of the century I've loved Taylor Swift for years. Mystery deepens over Hulk Hogan's death as his widow faces fresh anguish Warning as pasta salad is recalled due to risk of'fatal infections' Agents rushed to a West Texas farm Thursday morning after a massive balloon from space crash-landed in a crop field.

Anguished Diddy clutches his head in his hands in first image from disastrous sentencing that's gone from bad to worse Mystery deepens over Hulk Hogan's death as his widow faces fresh anguish I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Map shows where new strain of Covid is exploding in 19 states as sufferers are hit with'razor-blade' symptoms US military poised to seize ports and airfields in Venezuela as Trump strikes a fourth'narco-terrorist' boat Body count from Houston's bayous rises as serial killer whispers grip city and residents are told: 'Be vigilant' Selena Gomez's $1.3B fortune could create risks in Benny Blanco marriage despite his $50M success, experts reveal His daughter was warped into an ultra-woke monster and set fire to his life. Now, GOP state senator Jay Block fights back... and reveals the dark secrets she was desperate to hide Realtor with expensive ex-wife arrested over shocking $11.6m claims about how he was funding Palm Beach lifestyle The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with Scientists discover key part of the brain that degrades in Alzheimer's... paving way for breakthrough therapies Lori Loughlin's estranged husband Mossimo Giannulli seen with mystery brunette amid shock split Manchester synagogue terrorist was on bail for alleged rape at the time of his rampage and was'struggling with debt' after'splitting up with his wife and young son' Scientists behind study linking Tylenol to autism accuse Trump of'spreading misinformation' Teresa Giudice thought she was going to'die' during panic attack on Special Forces... after she was called'stupid' Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection A dental surgeon who had achieved professional success felt a void in his life until a rare exhibition in Turin, Italy, changed everything. Dr John Sottosanti stood before the Shroud of Turin, the ancient linen believed to have wrapped Jesus' body after the crucifixion, when he noticed something few had ever seen: the faint outline of human teeth beneath the cloth's imprint.

This poses the question of whether one can still learn fair classifiers given noisy sensitive features.

Data clustering is a central tenet of unsupervised machine learning.

Data clustering is a central tenet of unsupervised machine learning.

In this paper, we demonstrate that theoretically there is no limitation to using low-dimensional embedding-based methods, and provide experimental evidence that overfitting is the root cause of the poor performance of embedding-based methods.

V ariational mutual information (MI) estimators are widely used in unsupervised representation learning methods such as contrastive predictive coding (CPC).

In criminal justice, the "risk-needs-responsivity" model emphasizes matching the level of

Current bias evaluation methods rarely engage with communities impacted by AI systems. Inspired by bug bounties, bias bounties have been proposed as a reward-based method that involves communities in AI bias detection by asking users of AI systems to report biases they encounter when interacting with such systems. In the absence of a state-of-the-art review, this survey aimed to identify and analyse existing AI bias bounty programmes and to present academic literature on bias bounties. Google, Google Scholar, PhilPapers, and IEEE Xplore were searched, and five bias bounty programmes, as well as five research publications, were identified. All bias bounties were organised by U.S.-based organisations as time-limited contests, with public participation in four programmes and prize pools ranging from 7,000 to 24,000 USD. The five research publications included a report on the application of bug bounties to algorithmic harms, an article addressing Twitter's bias bounty, a proposal for bias bounties as an institutional mechanism to increase AI scrutiny, a workshop discussing bias bounties from queer perspectives, and an algorithmic framework for bias bounties. We argue that reducing the technical requirements to enter bounty programmes is important to include those without coding experience. Given the limited adoption of bias bounties, future efforts should explore the transferability of the best practices from bug bounties and examine how such programmes can be designed to be sensitive to underrepresented groups while lowering adoption barriers for organisations.

The rapid expansion of large language models (LLMs) has heightened concerns about their computational and environmental costs. This study investigates the trade-offs between translation quality and efficiency by comparing full-scale, distilled, and quantized models using machine translation as a case study. We evaluated performance on the Flores+ benchmark and through human judgments of conversational translations in French, Hindi, and Kannada. Our analysis revealed that the full 3.3B FP32 model, while achieving the highest BLEU scores, incurred the largest environmental footprint (~ 0.007-0.008 kg CO2 per run). The distilled 600M FP32 model reduced inference time by 71-78% and carbon emissions by 63-65% compared with the full model, with only minimal reductions in BLEU scores. Human evaluations further showed that even aggressive quantization (INT4) preserved high levels of accuracy and fluency, with differences between models generally minor. These findings demonstrate that model compression strategies can substantially reduce computational demands and environmental impact while maintaining competitive translation quality, though trade-offs are more pronounced in low-resource settings. We argue for evaluation frameworks that integrate efficiency and sustainability alongside accuracy as central dimensions of progress in NLP.