Parents in north London have been told their children could be excluded from school if they are not fully vaccinated against measles amid an outbreak of the highly-contagious disease. Unvaccinated pupils identified as close contacts of people with measles could be excluded for 21 days in accordance with national guidelines, Enfield Council said in a letter to all parents in the borough in late January. At least 34 children have contracted measles in Enfield so far this year, the UK Health Security Agency (UKHSA) has said, and a number sent to hospital. A local health chief meanwhile told the BBC: We are worried because actually, this is a significantly increased number than what we're used to. Asking unvaccinated, close contacts of measles cases to stay off school is fairly standard practice when there are local outbreaks.

At every step we observe semantic drift. For example, in the 5th generation, the model fails to generate a convincing suitcase, which also hints at cross-inconsistency. These phenomena are magnified under the multi-generation telephone game evaluation, allowing it to capture more subtle performance differences between models. Employing a single, unified model (UM) for both visual understanding (image-to-text: I2T) and visual generation (text-to-image: T2I) has opened a new direction in Visual Language Model (VLM) research. While UMs can also support broader unimodal tasks (e.g., text-to-text, image-to-image), we focus on the core cross-modal pair T2I and I2T. Existing evaluation benchmarks consider these capabilities in isolation: FID and GenEval for T2I, and benchmarks such as MME, MMBench for I2T. These isolated single-pass metrics do not reveal cross-consistency: whether a model that "understands" a concept can also "render" it, nor whether semantic meaning is preserved when cycling between image and text modalities. To address this, we introduce the Semantic Drift Protocol (SDP) for Unified Models, a cyclic evaluation protocol that alternates I2T and T2I over multiple generations to quantify semantic drift. We propose two metrics: (i) Mean Cumulative Drift (MCD), an embedding-based measure of overall semantic drift; and (ii) Multi-Generation GenEval (MGG), an object-level compliance score extending GenEval. To assess generalization beyond COCO dataset, which is widely used in training; we create a new benchmark Nocaps+Docci400, sampled from NoCaps and DOCCI and evaluated on seven recent models. SDP reveals substantial variation in cross-modal stability: some models like BAGEL maintain semantic meaning over many alternations, whereas others like VILA-U drift quickly despite strong single-pass scores. Our results highlight SDP as a necessary complement to standard I2T and T2I evaluations. Multimodal Unified Models (UMs) combine visual understanding and generation within a single framework, enabling a wide range of unimodal tasks (e.g., text-to-text, image-to-image) as well as cross-modal tasks (e.g., image-to-text, text-to-image). Despite rapid model progress, UM evaluation remains fragmented. In other words, current single-pass metrics do not assess the retention of entities, attributes, relations, and counts under alternating I2T T2I conversions. We defer unimodal tasks and center our analysis on I2T and T2I tasks as the potential for semantic divergence and its impact on real use is most pronounced on the cross-modal tasks.

Recent research shows how diffusion models can unconditionally generate tile-based game levels, but use of diffusion models for text-to-level generation is underexplored. There are practical considerations for creating a usable model: caption/level pairs are needed, as is a text embedding model, and a way of generating entire playable levels, rather than individual scenes. We present strategies to automatically assign descriptive captions to an existing dataset, and train diffusion models using both pretrained text encoders and simple transformer models trained from scratch. Captions are automatically assigned to generated scenes so that the degree of overlap between input and output captions can be compared. We also assess the diversity and playability of the resulting level scenes. Results are compared with an unconditional diffusion model and a generative adversarial network, as well as the text-to-level approaches Five-Dollar Model and MarioGPT. Notably, the best diffusion model uses a simple transformer model for text embedding, and takes less time to train than diffusion models employing more complex text encoders, indicating that reliance on larger language models is not necessary. We also present a GUI allowing designers to construct long levels from model-generated scenes.

Fox News White House correspondent Peter Doocy has more on President Biden's latest polling and his stance on immigration on'Special Report.' The federal judge presiding over former President Trump's classified records case has denied his motion to dismiss the charges based on the Presidential Records Act. U.S. District Court Judge Aileen Cannon, last month, also dismissed Trump's motion to dismiss charges of retaining classified documents on the grounds of "unconstitutional vagueness." In a filing Thursday, Cannon denied the former president's motion to dismiss, saying that the charges against Trump "make no reference to the Presidential Records Act, nor do they rely on that statute for purposes of stating an offense." Cannon said the Presidential Records Act "does not provide a pre-trial basis to dismiss" the case, saying "all of which state cognizable offenses."

The properties of exoplanet host stars are traditionally characterized through a detailed forward-modeling analysis of high-resolution spectra. However, many exoplanet radial velocity surveys employ iodine-cell-calibrated spectrographs, such that the vast majority of spectra obtained include an imprinted forest of iodine absorption lines. For surveys that use iodine cells, iodine-free "template" spectra must be separately obtained for precise stellar characterization. These template spectra often require extensive additional observing time to obtain, and they are not always feasible to obtain for faint stars. In this paper, we demonstrate that machine learning methods can be applied to infer stellar parameters and chemical abundances from iodine-imprinted spectra with high accuracy and precision. The methods presented in this work are broadly applicable to any iodine-cell-calibrated spectrograph. We make publicly available our spectroscopic pipeline, the Cannon HIRES Iodine Pipeline (CHIP), which derives stellar parameters and 15 chemical abundances from iodine-imprinted spectra of FGK stars and which has been set up for ease of use with Keck/HIRES spectra. Our proof-of-concept offers an efficient new avenue to rapidly estimate a large number of stellar parameters even in the absence of an iodine-free template spectrum.

Generative language models (LMs) have become omnipresent across data science. For a wide variety of tasks, inputs can be phrased as natural language prompts for an LM, from whose output the solution can then be extracted. LM performance has consistently been increasing with model size - but so has the monetary cost of querying the ever larger models. Importantly, however, not all inputs are equally hard: some require larger LMs for obtaining a satisfactory solution, whereas for others smaller LMs suffice. Based on this fact, we design a framework for cost-effective language model choice, called "Fly-swat or cannon" (FORC). Given a set of inputs and a set of candidate LMs, FORC judiciously assigns each input to an LM predicted to do well on the input according to a so-called meta-model, aiming to achieve high overall performance at low cost. The cost-performance tradeoff can be flexibly tuned by the user. Options include, among others, maximizing total expected performance (or the number of processed inputs) while staying within a given cost budget, or minimizing total cost while processing all inputs. We evaluate FORC on 14 datasets covering five natural language tasks, using four candidate LMs of vastly different size and cost. With FORC, we match the performance of the largest available LM while achieving a cost reduction of 63%. Via our publicly available library, researchers as well as practitioners can thus save large amounts of money without sacrificing performance.

In the east Ukrainian city of Bakhmut, 15 kilometres (nine miles) from the positions held by Russian forces, an artillery unit waits for the signal. The four soldiers duck and put their hands over their ears. "According to the coordinates we received, the target is infantry," says Oleksandr, 37, between two radioed orders. Around 30 seconds later, the 50 kilo (110-pound) "fragmentation" shell, pinched from the Russians after their retreat from a nearby town, will explode above the position held by Moscow's troops, showering them with its payload. A Ukrainian drone supports the operation "in real time", monitoring the effectiveness of the strike from the old Soviet D-20 cannon in order to better calibrate the next one.

Optii Solutions, the leading hotel operations software, has announced the appointment of Andy Cannon as Financial Controller. Cannon will help elevate the company's finance function amidst rapid growth. Cannon has more than two decades of finance experience and has joined Optii most recently from RW3 Technologies Inc., the global leader in Commerce Execution SaaS products, where he ran all aspects of the accounting department and was a key contributor to the company's successful acquisition by Wiser Solutions. In his new role, Cannon will be responsible for ensuring that Optii's financial controls are healthy and the correct internal systems are in place to support the global growth. In the first half of 2022 alone, Optii has tripled its footprint in North America and the company continues to add some of the world's most recognizable hotels and brands to its portfolio, including The Don CeSar and Nemacolin.

Reliance on multiple data points, from historic prices and trends to property ownership data, is nothing new. However, the data that we use to make our housing choices have become increasingly sophisticated. Quantarium's valuation models provide property valuations through a self-learning AI engine. Quantarium's advances in Computer Vision (CV) technology is allowing AVM to transcend historical constraints; understanding their journey and the impact of AI is critical to the future of real estate. Quantarium is a leading producer and purveyor of value-added data in residential real estate. Their data and analytics scientists and experts illustrated great innovation in their approach to building the industry's leading RE data lake, so it is a team-wide validation to receive this distinction.

Data parallelism does a good job in speeding up the training. However, when it comes to the case when the memory of a single device can not host a whole model, data parallelism would not have the chance to do anything. Another option is to split the model by operator, or horizontally. Megatron-LM introduced a 1-Dimensional distributed method to use GPUs to speed up the training process. Optimus is a 2D solution for distributed tensor parallelism. However, these methods have a high communication overhead and a low scaling efficiency on large-scale computing clusters. To solve this problem, we investigate the 2.5-Dimensional distributed tensor parallelism.Introduced by Solomonik et al., 2.5-Dimensional Matrix Multiplication developed an effective method to perform multiple Cannon's algorithm at the same time to increase the efficiency. With many restrictions of Cannon's Algorithm and a huge amount of shift operation, we need to invent a new method of 2.5-dimensional matrix multiplication to enhance the performance. Absorbing the essence from both SUMMA and 2.5-Dimensional Matrix Multiplication, we introduced SUMMA2.5-LM for language models to overcome the abundance of unnecessary transmission loss result from the increasing size of language model parallelism. Compared to previous 1D and 2D model parallelization of language models, our SUMMA2.5-LM managed to reduce the transmission cost on each layer, which could get a 1.45X efficiency according to our weak scaling result between 2.5-D [4,4,4] arrangement and 2-D [8,8,1] arrangement.