Miracle-dz/UniFAR-FTUs huggingface dataset:
LMStyle Benchmark: Evaluating Text Style Transfer for Chatbots

Since the breakthrough of ChatGPT, large language models (LLMs) have garnered significant attention in the research community. With the development of LLMs, the question of text style transfer for conversational models has emerged as a natural extension, where chatbots may possess their own styles or even characters. However, standard evaluation metrics have not yet been established for this new settings. This paper aims to address this issue by proposing the LMStyle Benchmark, a novel evaluation framework applicable to chat-style text style transfer (C-TST), that can measure the quality of style transfer for LLMs in an automated and scalable manner. In addition to conventional style strength metrics, LMStyle Benchmark further considers a novel aspect of metrics called appropriateness, a high-level metrics take account of coherence, fluency and other implicit factors without the aid of reference samples. Our experiments demonstrate that the new evaluation methods introduced by LMStyle Benchmark have a higher correlation with human judgments in terms of appropriateness. Based on LMStyle Benchmark, we present a comprehensive list of evaluation results for popular LLMs, including LLaMA, Alpaca, and Vicuna, reflecting their stylistic properties, such as formality and sentiment strength, along with their appropriateness.
Silico-centric Theory of Mind

Theory of Mind (ToM) refers to the ability to attribute mental states, such as beliefs, desires, intentions, and knowledge, to oneself and others, and to understand that these mental states can differ from one's own and from reality. We investigate ToM in environments with multiple, distinct, independent AI agents, each possessing unique internal states, information, and objectives. Inspired by human false-belief experiments, we present an AI ('focal AI') with a scenario where its clone undergoes a human-centric ToM assessment. We prompt the focal AI to assess whether its clone would benefit from additional instructions. Concurrently, we give its clones the ToM assessment, both with and without the instructions, thereby engaging the focal AI in higher-order counterfactual reasoning akin to human mentalizing--with respect to humans in one test and to other AI in another. We uncover a discrepancy: Contemporary AI demonstrates near-perfect accuracy on human-centric ToM assessments. Since information embedded in one AI is identically embedded in its clone, additional instructions are redundant. Yet, we observe AI crafting elaborate instructions for their clones, erroneously anticipating a need for assistance. An independent referee AI agrees with these unsupported expectations. Neither the focal AI nor the referee demonstrates ToM in our 'silico-centric' test.
StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images

Stain normalization algorithms aim to transform the color and intensity characteristics of a source multi-gigapixel histology image to match those of a target image, mitigating inconsistencies in the appearance of stains used to highlight cellular components in the images. We propose a new approach, StainFuser, which treats this problem as a style transfer task using a novel Conditional Latent Diffusion architecture, eliminating the need for handcrafted color components. With this method, we curate SPI-2M the largest stain normalization dataset to date of over 2 million histology images with neural style transfer for high-quality transformations. Trained on this data, StainFuser outperforms current state-of-the-art GAN and handcrafted methods in terms of the quality of normalized images. Additionally, compared to existing approaches, it improves the performance of nuclei instance segmentation and classification models when used as a test time augmentation method on the challenging CoNIC dataset. Finally, we apply StainFuser on multi-gigapixel Whole Slide Images (WSIs) and demonstrate improved performance in terms of computational efficiency, image quality and consistency across tiles over current methods.
High-dimensional expansion and soficity of groups

For $d \geq 4$ and $p$ a sufficiently large prime, we construct a lattice $\Gamma \leq {\rm PSp}_{2d}(\mathbb Q_p),$ such that its universal central extension cannot be sofic if $\Gamma$ satisfies some weak form of stability in permutations. In the proof, we make use of high-dimensional expansion phenomena and, extending results of Lubotzky, we construct new examples of cosystolic expanders over arbitrary finite abelian groups.
PeV-Scale SUSY and Cosmic Strings from F-term Hybrid Inflation

We consider F-term hybrid inflation (FHI) and SUSY breaking in the context of a B-L extension of MSSM which largely respects a global U(1) R symmetry. The hidden sector Kaehler manifold enjoys an enhanced SU(1,1)/U(1) symmetry with the scalar curvature determined by the achievement of a SUSY-breaking de Sitter vacuum without ugly tuning. FHI turns out to be consistent with data, provided that the magnitude of the emergent soft tadpole term is confined in the range (1.2-100) TeV, and it is accompanied with the production of B-L cosmic strings. If these are metastable they interpret the present observations from PTA experiments on the stochastic background of gravitational waves with dimensionless tension Gmu~(1-9.2)x10^-8. The mu parameter of MSSM arises by appropriately adapting the Giudice-Masiero mechanism and facilitates the out-of-equilibrium decay of the R saxion at a reheat temperature lower than about 71 GeV. Due to the prolonged matter dominated era the gravitational wave signal is suppressed at high frequencies. The SUSY mass scale turns out to lie in the PeV region.
Are Vision Language Models Texture or Shape Biased and Can We Steer Them?

Vision language models (VLMs) have drastically changed the computer vision model landscape in only a few years, opening an exciting array of new applications from zero-shot image classification, over to image captioning, and visual question answering. Unlike pure vision models, they offer an intuitive way to access visual content through language prompting. The wide applicability of such models encourages us to ask whether they also align with human vision - specifically, how far they adopt human-induced visual biases through multimodal fusion, or whether they simply inherit biases from pure vision models. One important visual bias is the texture vs. shape bias, or the dominance of local over global information. In this paper, we study this bias in a wide range of popular VLMs. Interestingly, we find that VLMs are often more shape-biased than their vision encoders, indicating that visual biases are modulated to some extent through text in multimodal models. If text does indeed influence visual biases, this suggests that we may be able to steer visual biases not just through visual input but also through language: a hypothesis that we confirm through extensive experiments. For instance, we are able to steer shape bias from as low as 49% to as high as 72% through prompting alone. For now, the strong human bias towards shape (96%) remains out of reach for all tested VLMs.
Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference

Transformers have emerged as the backbone of large language models (LLMs). However, generation remains inefficient due to the need to store in memory a cache of key-value representations for past tokens, whose size scales linearly with the input sequence length and batch size. As a solution, we propose Dynamic Memory Compression (DMC), a method for on-line key-value cache compression at inference time. Most importantly, the model learns to apply different compression rates in different heads and layers. We retrofit pre-trained LLMs such as Llama 2 (7B, 13B and 70B) into DMC Transformers, achieving up to ~3.7x throughput increase in auto-regressive inference on a NVIDIA H100 GPU. DMC is applied via continued pre-training on a negligible percentage of the original data without adding any extra parameters. We find that DMC preserves the original downstream performance with up to 4x cache compression, outperforming up-trained grouped-query attention (GQA). GQA and DMC can be even combined to obtain compounded gains. As a result DMC fits longer contexts and larger batches within any given memory budget.
Observation of quantum thermalization restricted to Hilbert space fragments

Quantum thermalization occurs in a broad class of systems from elementary particles to complex materials. Out-of-equilibrium quantum systems have long been understood to either thermalize or retain memory of their initial states, but not both. Here we achieve the first simultaneous demonstration of thermalization and memory in a quantum system. Using a Rydberg atom array, we observe quantum thermalization restricted to Hilbert space fragments, where the thermalized system retains characteristics of the initial configuration. Intriguingly, states from different subspaces do not thermalize with each other even when they have the same energy. Our work challenges established ideas of quantum thermalization while experimentally resolving the longstanding tension between thermalization and memory. These results may be applied to control entanglement dynamics in quantum processors and quantum sensors.
Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation

Multimodal large language models (MLLMs) have shown impressive reasoning abilities, which, however, are also more vulnerable to jailbreak attacks than their LLM predecessors. Although still capable of detecting unsafe responses, we observe that safety mechanisms of the pre-aligned LLMs in MLLMs can be easily bypassed due to the introduction of image features. To construct robust MLLMs, we propose ECSO(Eyes Closed, Safety On), a novel training-free protecting approach that exploits the inherent safety awareness of MLLMs, and generates safer responses via adaptively transforming unsafe images into texts to activate intrinsic safety mechanism of pre-aligned LLMs in MLLMs. Experiments on five state-of-the-art (SoTA) MLLMs demonstrate that our ECSO enhances model safety significantly (e.g., a 37.6% improvement on the MM-SafetyBench (SD+OCR), and 71.3% on VLSafe for the LLaVA-1.5-7B), while consistently maintaining utility results on common MLLM benchmarks. Furthermore, we show that ECSO can be used as a data engine to generate supervised-finetuning (SFT) data for MLLM alignment without extra human intervention.
Computational data on ${\mathfrak S}_n$-extensions of $\mathbb Q$

We discuss computational results on field extensions $K/{\mathbb Q}$ of degree $n\le11$ with Galois group of the Galois closure isomorphic to the full symmetric group ${\mathfrak S}_n$. More precisely, we present statistics on the number of such extensions as a function of the field discriminant and compare them to the known predictions by Bhargava and the author. We also investigate the numbers of fields with equal discriminant and tabulate class numbers and class groups to compare them against Cohen--Lenstra--Martinet type of heuristics and their proposed improvements.
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics

We explore a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia's Neutral Point of View (NPOV) principle: acknowledge the absence of a single true answer and surface multiple perspectives. We frame this as retrieval augmented generation, where perspectives are retrieved from a knowledge base and the LLM is tasked with generating a fluent and faithful response from the given perspectives. As a starting point, we use a deterministic retrieval system and then focus on common LLM failure modes that arise during this approach to text generation, namely hallucination and coverage errors. We propose and evaluate three methods to detect such errors based on (1) word-overlap, (2) salience, and (3) LLM-based classifiers. Our results demonstrate that LLM-based classifiers, even when trained only on synthetic errors, achieve high error detection performance, with ROC AUC scores of 95.3% for hallucination and 90.5% for coverage error detection on unambiguous error cases. We show that when no training data is available, our other methods still yield good results on hallucination (84.0%) and coverage error (85.2%) detection.
Climate Immobility Traps: A Household-Level Test

The complex relationship between climate shocks, migration, and adaptation hampers a rigorous understanding of the heterogeneous mobility outcomes of farm households exposed to climate risk. To unpack this heterogeneity, the analysis combines longitudinal multi-topic household survey data from Nigeria with a causal machine learning approach, tailored to a conceptual framework bridging economic migration theory and the poverty traps literature. The results show that pre-shock asset levels, in situ adaptive capacity, and cumulative shock exposure drive not just the magnitude but also the sign of the impact of agriculture-relevant weather anomalies on the mobility outcomes of farming households. While local adaptation acts as a substitute for migration, the roles played by wealth constraints and repeated shock exposure suggest the presence of climate-induced immobility traps.
Faceptor: A Generalist Model for Face Perception

With the comprehensive research conducted on various face analysis tasks, there is a growing interest among researchers to develop a unified approach to face perception. Existing methods mainly discuss unified representation and training, which lack task extensibility and application efficiency. To tackle this issue, we focus on the unified model structure, exploring a face generalist model. As an intuitive design, Naive Faceptor enables tasks with the same output shape and granularity to share the structural design of the standardized output head, achieving improved task extensibility. Furthermore, Faceptor is proposed to adopt a well-designed single-encoder dual-decoder architecture, allowing task-specific queries to represent new-coming semantics. This design enhances the unification of model structure while improving application efficiency in terms of storage overhead. Additionally, we introduce Layer-Attention into Faceptor, enabling the model to adaptively select features from optimal layers to perform the desired tasks. Through joint training on 13 face perception datasets, Faceptor achieves exceptional performance in facial landmark localization, face parsing, age estimation, expression recognition, binary attribute classification, and face recognition, achieving or surpassing specialized methods in most tasks. Our training framework can also be applied to auxiliary supervised learning, significantly improving performance in data-sparse tasks such as age estimation and expression recognition. The code and models will be made publicly available at https://github.com/lxq1000/Faceptor.
Majority-of-Three: The Simplest Optimal Learner?

Developing an optimal PAC learning algorithm in the realizable setting, where empirical risk minimization (ERM) is suboptimal, was a major open problem in learning theory for decades. The problem was finally resolved by Hanneke a few years ago. Unfortunately, Hanneke's algorithm is quite complex as it returns the majority vote of many ERM classifiers that are trained on carefully selected subsets of the data. It is thus a natural goal to determine the simplest algorithm that is optimal. In this work we study the arguably simplest algorithm that could be optimal: returning the majority vote of three ERM classifiers. We show that this algorithm achieves the optimal in-expectation bound on its error which is provably unattainable by a single ERM classifier. Furthermore, we prove a near-optimal high-probability bound on this algorithm's error. We conjecture that a better analysis will prove that this algorithm is in fact optimal in the high-probability regime.
Twenty ways to estimate the Log Gaussian Cox Process model with point and aggregated case data: the rts2 package for R

The R package rts2 provides data manipulation and model fitting tools for Log Gaussian Cox Process (LGCP) models. LGCP models are a key method for disease and other types of surveillance, and provide a means of predicting risk across an area of interest based on spatially-referenced and time-stamped case data. However, these models can be difficult to specify and computationally demanding to estimate. For many surveillance scenarios we require results in near real-time using routinely available data to guide and direct policy responses, or due to limited availability of computational resources. There are limited software implementations available for this real-time context with reliable predictions and quantification of uncertainty. The rts2 package provides a range of modern Gaussian process approximations and model fitting methods to fit the LGCP, including estimation of covariance parameters, using both Bayesian and stochastic Maximum Likelihood methods. The package provides a suite of data manipulation tools. We also provide a novel implementation to estimate the LGCP when case data are aggregated to an irregular grid such as census tract areas.
Generalized Relevance Learning Grassmann Quantization

Due to advancements in digital cameras, it is easy to gather multiple images (or videos) from an object under different conditions. Therefore, image-set classification has attracted more attention, and different solutions were proposed to model them. A popular way to model image sets is subspaces, which form a manifold called the Grassmann manifold. In this contribution, we extend the application of Generalized Relevance Learning Vector Quantization to deal with Grassmann manifold. The proposed model returns a set of prototype subspaces and a relevance vector. While prototypes model typical behaviours within classes, the relevance factors specify the most discriminative principal vectors (or images) for the classification task. They both provide insights into the model's decisions by highlighting influential images and pixels for predictions. Moreover, due to learning prototypes, the model complexity of the new method during inference is independent of dataset size, unlike previous works. We applied it to several recognition tasks including handwritten digit recognition, face recognition, activity recognition, and object recognition. Experiments demonstrate that it outperforms previous works with lower complexity and can successfully model the variation, such as handwritten style or lighting conditions. Moreover, the presence of relevances makes the model robust to the selection of subspaces' dimensionality.
Semiparametric Token-Sequence Co-Supervision

In this work, we introduce a semiparametric token-sequence co-supervision training method. It trains a language model by simultaneously leveraging supervision from the traditional next token prediction loss which is calculated over the parametric token embedding space and the next sequence prediction loss which is calculated over the nonparametric sequence embedding space. The nonparametric sequence embedding space is constructed by a separate language model tasked to condense an input text into a single representative embedding. Our experiments demonstrate that a model trained via both supervisions consistently surpasses models trained via each supervision independently. Analysis suggests that this co-supervision encourages a broader generalization capability across the model. Especially, the robustness of parametric token space which is established during the pretraining step tends to effectively enhance the stability of nonparametric sequence embedding space, a new space established by another language model.
Discovery of a distinct collective mode in kagome superconductors

The collective modes of the superconducting (SC) order parameter fluctuation can provide key insights into the nature of the superconductor, such as the pairing symmetry and orbital nature of the cooper pairs. Their detection has been challenging. Recently, a family of charge density wave (CDW) superconductors has emerged in non-magnetic kagome materials AV3Sb5 (A=K, Rb, Cs), exhibiting evidence for unprecedented time-reversal symmetry breaking CDW, roton-pair density wave (PDW), and higher-charge flux quantization. However, the collective behaviors of the cooper pairs have not been studied. Here, we report a distinct collective mode in the kagome superconductors CsV3-xTaxSb5. Using scanning tunneling microscope/spectroscopy (STM/S), we observe a"peak-dip-hump"feature in the tunneling conductance-a defining signature of collective excitations. The spectral lineshape is well-described by two SC gap functions, one isotropic and one anisotropic, and a bosonic mode due to electron-mode coupling. With increasing x, the two SC gaps, with different pair-breaking strengths and thus different orbital/band characters, move closer in energy, merge into two isotropic gaps of equal amplitude, and then increase synchronously. The collective mode energy, on the other hand, decreases monotonically to well below the quasi-particle excitation gap 2{\Delta} and survives even after the CDW order is suppressed at large x. We identify the collective mode as the Leggett mode of the relative phases between different SC components in the multi-band superconductor or the exotic Bardasis-Schrieffer mode due to a subleading SC component. Our findings provide valuable insights on the nature of the SC ground state and collective excitations, their evolution with Ta-substitutions, and offer a new pathway to study the origin of superconductivity and its interplay with CDW order in kagome superconductors.
An exploration of evolving forms of access to performance and image enhancing drugs (PIED), and body image, on Gold Coast, Australia, with particular emphasis on how users reconcile their drift into criminality

In contemporary culture, the growing importance of body image has created a context where pharmacological and surgical enhancement of the body is accepted in numerous forms. By extension, although controlled or illegal in many countries, Performance and Image Enhancing Drug (PIED) use is also becoming increasingly prevalent among gym attendees in mainstream society. This investigation attempts to understand, and situate growing PIED use and to explore the extent to which theoretical explanations such as drift, normalisation and risk denial help explain individuals’ drift into criminality and how PIED are now permeating the realms of wider acceptance. How PIED are supplied and how this is changing is also considered. A mixed methods approach utilising online surveys (n40), and semi-structured interviews (22 Gold Coast residents) was undertaken, with current and former PIED users. The Gold Coast, in Queensland, Australia was selected as a focus due to its comparatively high numbers of PIED users. Overall the data suggests that for many on the Gold Coast, the body plays a large role in the formation their identity, as it appears to for increasing numbers today. As such, the rise of a gym culture appears to be bringing PIED use on the Gold Coast further into the mainstream, and for some, it is now an accepted practice.