Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation

We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the private examples demonstrated in the prompt. We propose a novel algorithm that generates synthetic few-shot demonstrations from the private dataset with formal differential privacy (DP) guarantees, and show empirically that it can achieve effective ICL. We conduct extensive experiments on standard benchmarks and compare our algorithm with non-private ICL and zero-shot solutions. Our results demonstrate that our algorithm can achieve competitive performance with strong privacy levels. These results open up new possibilities for ICL with privacy protection for a broad range of applications.
Distilling Adversarial Prompts from Safety Benchmarks: Report for the Adversarial Nibbler Challenge

Text-conditioned image generation models have recently achieved astonishing image quality and alignment results. Consequently, they are employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the web, they also produce unsafe content. As a contribution to the Adversarial Nibbler challenge, we distill a large set of over 1,000 potential adversarial inputs from existing safety benchmarks. Our analysis of the gathered prompts and corresponding images demonstrates the fragility of input filters and provides further insights into systematic safety issues in current generative image models.
On curvature bounds in Lorentzian length spaces

We introduce several new notions of (sectional) curvature bounds for Lorentzian pre-length spaces: On the one hand, we provide convexity/concavity conditions for the (modified) time separation function, and, on the other hand, we study four-point conditions, which are suitable also for the non-intrinsic setting. Via these concepts we are able to establish (under mild assumptions) the equivalence of all previously known formulations of curvature bounds. In particular, we obtain the equivalence of causal and timelike curvature bounds as introduced in Kunzinger and S\"amann (Ann. Glob. Anal. Geom. 54(3):399-447, 2018).
Direct mass measurements of neutron-rich zinc and gallium isotopes: an investigation of the formation of the first r-process peak

The prediction of isotopic abundances resulting from the rapid neutron capture process (r-process) requires high-precision mass measurements. Mass measurements of $^{79-83}$Zn and $^{85,86}$Ga using TITAN's on-line time-of-flight spectrometer. First time measurements are performed for $^{79m}$Zn, $^{83}$Zn, and $^{86}$Ga. These measurements reduced uncertainties, and are used to calculate isotopic abundances near the first r-process abundance peak using astrophysical conditions present during a binary neutron star (BNS) merger. Good agreement across a range of trajectories is found when comparing to several metal-poor stellar abundances. Particularly, this subset of trajectories produces agreement with the abundance pattern of both the `r/s-star' HD94028 as well as the `r-process star' HD222925. These findings point to a high degree of sensitivity to the electron fraction of a BNS merger on the final elemental abundance pattern near the first r-process peak. In particular, we find that small changes in electron fraction produce distinct abundance patterns that match those of metal-poor stars with different classifications, calls the need for an i-process into question.
Incentivized Communication for Federated Bandits

Most existing works on federated bandits take it for granted that all clients are altruistic about sharing their data with the server for the collective good whenever needed. Despite their compelling theoretical guarantee on performance and communication efficiency, this assumption is overly idealistic and oftentimes violated in practice, especially when the algorithm is operated over self-interested clients, who are reluctant to share data without explicit benefits. Negligence of such self-interested behaviors can significantly affect the learning efficiency and even the practical operability of federated bandit learning. In light of this, we aim to spark new insights into this under-explored research area by formally introducing an incentivized communication problem for federated bandits, where the server shall motivate clients to share data by providing incentives. Without loss of generality, we instantiate this bandit problem with the contextual linear setting and propose the first incentivized communication protocol, namely, Inc-FedUCB, that achieves near-optimal regret with provable communication and incentive cost guarantees. Extensive empirical experiments on both synthetic and real-world datasets further validate the effectiveness of the proposed method across various environments.
Focal Inferential Infusion Coupled with Tractable Density Discrimination for Implicit Hate Speech Detection

Although pre-trained large language models (PLMs) have achieved state-of-the-art on many NLP tasks, they lack understanding of subtle expressions of implicit hate speech. Such nuanced and implicit hate is often misclassified as non-hate. Various attempts have been made to enhance the detection of (implicit) hate content by augmenting external context or enforcing label separation via distance-based metrics. We combine these two approaches and introduce FiADD, a novel Focused Inferential Adaptive Density Discrimination framework. FiADD enhances the PLM finetuning pipeline by bringing the surface form of an implicit hate speech closer to its implied form while increasing the inter-cluster distance among various class labels. We test FiADD on three implicit hate datasets and observe significant improvement in the two-way and three-way hate classification tasks. We further experiment on the generalizability of FiADD on three other tasks, namely detecting sarcasm, irony, and stance, in which surface and implied forms differ, and observe similar performance improvement. We analyze the generated latent space to understand its evolution under FiADD, which corroborates the advantage of employing FiADD for implicit hate speech detection.
ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers

As robotic systems increasingly encounter complex and unconstrained real-world scenarios, there is a demand to recognize diverse objects. The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects. Recent novel object pose estimation methods are solving this issue using task-specific fine-tuned CNNs for deep template matching. This adaptation for pose estimation still requires expensive data rendering and training procedures. MegaPose for example is trained on a dataset consisting of two million images showing 20,000 different objects to reach such generalization capabilities. To overcome this shortcoming we introduce ZS6D, for zero-shot novel object 6D pose estimation. Visual descriptors, extracted using pre-trained Vision Transformers (ViT), are used for matching rendered templates against query images of objects and for establishing local correspondences. These local correspondences enable deriving geometric correspondences and are used for estimating the object's 6D pose with RANSAC-based PnP. This approach showcases that the image descriptors extracted by pre-trained ViTs are well-suited to achieve a notable improvement over two state-of-the-art novel object 6D pose estimation methods, without the need for task-specific fine-tuning. Experiments are performed on LMO, YCBV, and TLESS. In comparison to one of the two methods we improve the Average Recall on all three datasets and compared to the second method we improve on two datasets.
Guided rewiring of social networks reduces polarization and accelerates collective action

Global challenges like climate change may be considered as collective action problems that require sufficient cooperation with pro-mitigation norms, soon enough to be effective. Socio-political polarization is a barrier to collective action. Prior agent-based models of behavioural change on structured networks in a shared socio-political environment have shown that polarization emerges naturally in such systems and that the speed of consensus formation is limited by the rate at which polarized clusters can be dissolved. Here we study how guided social link rewiring affects the speed of network depolarization. We investigate rewiring algorithms representing random meetings, introduction by mutual acquaintances, and bridging between socially distant communities. We find that building lasting links between polarized individuals and communities can accelerate consensus formation when the sociopolitical environment is favourable. This strengthens the evidence that promoting connection between polarized communities could accelerate collective action on urgent global challenges.
Inhomogeneous turbulence for the Wick nonlinear Schr\"odinger equation

We introduce a simplified model for wave turbulence theory -- the Wick NLS, of which the main feature is the absence of all self-interactions in the correlation expansions of its solutions. For this model, we derive several wave kinetic equations that govern the effective statistical behavior of its solutions in various regimes. In the homogeneous setting, where the initial correlation is translation invariant, we obtain a wave kinetic equation similar to the one predicted by the formal theory. In the inhomogeneous setting, we obtain a wave kinetic equation that describes the statistical behavior of the wavepackets of the solutions, accounting for both the transport of wavepackets and collisions among them. Another wave kinetic equation, which seems new in the literature, also appears in a certain scaling regime of this setting and provides a more refined collision picture.
Beyond the Drake Equation: A Time-dependent Inventory of Habitable Planets and Life-bearing Worlds in the Solar Neighborhood

We introduce a mathematical framework for statistical exoplanet population and astrobiology studies that may help direct future observational efforts and experiments. The approach is based on a set of differential equations and provides a time-dependent mapping between star formation, metal enrichment, and the occurrence of exoplanets and potentially life-harboring worlds over the chemo-population history of the solar neighborhood. Our results are summarized as follows: (1) the formation of exoplanets in the solar vicinity was episodic, starting with the emergence of the thick disk about 11 Gyr ago; (2) within 100 pc from the Sun, there are as many as 11,000(η ⊕/0.24) Earth-size planets in the habitable zone (“temperate terrestrial planets” or TTPs) of K-type stars. The solar system is younger than the median TTP, and was created in a star formation surge that peaked 5.5 Gyr ago and was triggered by an external agent; (3) the metallicity modulation of the giant planet occurrence rate results in a later typical formation time, with TTPs outnumbering giant planets at early times; and (4) the closest, life-harboring Earth-like planet would be ≲20 pc away if microbial life arose as soon as it did on Earth in ≳1% of the TTPs around K stars. If simple life is abundant (fast abiogenesis), it is also old, as it would have emerged more than 8 Gyr ago in about one-third of all life-bearing planets today. Older Earth analogs are more likely to have developed sufficiently complex life capable of altering their environment and producing detectable oxygenic biosignatures.
Syntactic Variation Across the Grammar: Modelling a Complex Adaptive System

While language is a complex adaptive system, most work on syntactic variation observes a few individual constructions in isolation from the rest of the grammar. This means that the grammar, a network which connects thousands of structures at different levels of abstraction, is reduced to a few disconnected variables. This paper quantifies the impact of such reductions by systematically modelling dialectal variation across 49 local populations of English speakers in 16 countries. We perform dialect classification with both an entire grammar as well as with isolated nodes within the grammar in order to characterize the syntactic differences between these dialects. The results show, first, that many individual nodes within the grammar are subject to variation but, in isolation, none perform as well as the grammar as a whole. This indicates that an important part of syntactic variation consists of interactions between different parts of the grammar. Second, the results show that the similarity between dialects depends heavily on the sub-set of the grammar being observed: for example, New Zealand English could be more similar to Australian English in phrasal verbs but at the same time more similar to UK English in dative phrases.
Ad-load Balancing via Off-policy Learning in a Content Marketplace

Ad-load balancing is a critical challenge in online advertising systems, particularly in the context of social media platforms, where the goal is to maximize user engagement and revenue while maintaining a satisfactory user experience. This requires the optimization of conflicting objectives, such as user satisfaction and ads revenue. Traditional approaches to ad-load balancing rely on static allocation policies, which fail to adapt to changing user preferences and contextual factors. In this paper, we present an approach that leverages off-policy learning and evaluation from logged bandit feedback. We start by presenting a motivating analysis of the ad-load balancing problem, highlighting the conflicting objectives between user satisfaction and ads revenue. We emphasize the nuances that arise due to user heterogeneity and the dependence on the user's position within a session. Based on this analysis, we define the problem as determining the optimal ad-load for a particular feed fetch. To tackle this problem, we propose an off-policy learning framework that leverages unbiased estimators such as Inverse Propensity Scoring (IPS) and Doubly Robust (DR) to learn and estimate the policy values using offline collected stochastic data. We present insights from online A/B experiments deployed at scale across over 80 million users generating over 200 million sessions, where we find statistically significant improvements in both user satisfaction metrics and ads revenue for the platform.
BELT: Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision

This paper presents BELT, a novel model and learning framework for the pivotal topic of brain-to-language translation research. The translation from noninvasive brain signals into readable natural language has the potential to promote the application scenario as well as the development of brain-computer interfaces (BCI) as a whole. The critical problem in brain signal decoding or brain-to-language translation is the acquisition of semantically appropriate and discriminative EEG representation from a dataset of limited scale and quality. The proposed BELT method is a generic and efficient framework that bootstraps EEG representation learning using off-the-shelf large-scale pretrained language models (LMs). With a large LM's capacity for understanding semantic information and zero-shot generalization, BELT utilizes large LMs trained on Internet-scale datasets to bring significant improvements to the understanding of EEG signals. In particular, the BELT model is composed of a deep conformer encoder and a vector quantization encoder. Semantical EEG representation is achieved by a contrastive learning step that provides natural language supervision. We achieve state-of-the-art results on two featuring brain decoding tasks including the brain-to-language translation and zero-shot sentiment classification. Specifically, our model surpasses the baseline model on both tasks by 5.45% and over 10% and archives a 42.31% BLEU-1 score and 67.32% precision on the main evaluation metrics for translation and zero-shot sentiment classification respectively.
Genetic Composition of Supercritical Branching Populations under Power Law Mutation Rates

We aim at understanding the evolution of the genetic composition of cancer cell populations. To this aim, we consider a branching individual based model representing a cell population where cells divide, die and mutate along the edges of a finite directed graph $(V,E)$. The process starts with only one cell of trait $0$. Following typical parameter values in cancer cell populations we study the model under \textit{large population and power law mutation rates limit}, in the sense that the mutation probabilities are parameterized by negative powers of $n$ and the typical sizes of the population of interest are positive powers of $n$. Under \textit{non-increasing growth rate condition}, we describe the time evolution of the first-order asymptotics of the size of each subpopulation on the $log(n)$ time scale, as well as in the random time scale at which the initial population, resp. the total population, reaches the size $n^{t}$. In particular, such results allow for the perfect characterization of evolutionary pathways. Without imposing any conditions on the growth rates, we describe the time evolution of the order of magnitude of each subpopulation, whose asymptotic limits are positive non-decreasing piecewise linear continuous functions.
Rethinking the Evaluating Framework for Natural Language Understanding in AI Systems: Language Acquisition as a Core for Future Metrics

In the burgeoning field of artificial intelligence (AI), the unprecedented progress of large language models (LLMs) in natural language processing (NLP) offers an opportunity to revisit the entire approach of traditional metrics of machine intelligence, both in form and content. As the realm of machine cognitive evaluation has already reached Imitation, the next step is an efficient Language Acquisition and Understanding. Our paper proposes a paradigm shift from the established Turing Test towards an all-embracing framework that hinges on language acquisition, taking inspiration from the recent advancements in LLMs. The present contribution is deeply tributary of the excellent work from various disciplines, point out the need to keep interdisciplinary bridges open, and delineates a more robust and sustainable approach.
Designing Loving-Kindness Meditation in Virtual Reality for Long-Distance Romantic Relationships

Loving-kindness meditation (LKM) is used in clinical psychology for couples' relationship therapy, but physical isolation can make the relationship more strained and inaccessible to LKM. Virtual reality (VR) can provide immersive LKM activities for long-distance couples. However, no suitable commercial VR applications for couples exist to engage in LKM activities of long-distance. This paper organized a series of workshops with couples to build a prototype of a couple-preferred LKM app. Through analysis of participants' design works and semi-structured interviews, we derived design considerations for such VR apps and created a prototype for couples to experience. We conducted a study with couples to understand their experiences of performing LKM using the VR prototype and a traditional video conferencing tool. Results show that LKM session utilizing both tools has a positive effect on the intimate relationship and the VR prototype is a more preferable tool for long-term use. We believe our experience can inform future researchers.
"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents

The widespread use of Large Language Model (LLM)-based conversational agents (CAs), especially in high-stakes domains, raises many privacy concerns. Building ethical LLM-based CAs that respect user privacy requires an in-depth understanding of the privacy risks that concern users the most. However, existing research, primarily model-centered, does not provide insight into users' perspectives. To bridge this gap, we analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users. We found that users are constantly faced with trade-offs between privacy, utility, and convenience when using LLM-based CAs. However, users' erroneous mental models and the dark patterns in system design limited their awareness and comprehension of the privacy risks. Additionally, the human-like interactions encouraged more sensitive disclosures, which complicated users' ability to navigate the trade-offs. We discuss practical design guidelines and the needs for paradigmatic shifts to protect the privacy of LLM-based CA users.
A Comparison between the Morphologies and Structures of Dwarf Galaxies with and without Active Massive Black Holes

We study the morphologies and structures of 57 dwarf galaxies that are representative of the general population of dwarf galaxies and compare their demographics to a sample of dwarf galaxies hosting optically selected active galactic nuclei (AGNs). The two samples span the same galaxy stellar mass (109 ≲ M ⋆/M ⊙ ≲ 109.5) and color range, and the observations are well-matched in physical resolution. The fractions of irregular galaxies (14%) and early types/ellipticals (∼18%) are nearly identical among the two samples. However, among galaxies with disks (the majority of each sample), the AGN hosts almost always have a detectable (pseudo)bulge, while a large fraction of the non-AGN hosts are pure disk galaxies with no detectable (pseudo)bulge. Central point sources of light consistent with nuclear star clusters are detected in many of the non-AGN hosts. In contrast, central point sources detected in the AGN hosts are, on average, more than 2 orders of magnitude more luminous, suggesting the point sources in these objects are dominated by AGN light. The preference for (pseudo)bulges in dwarf AGN hosts may inform searches for massive black holes in dwarf galaxies and attempts to constrain the black hole occupation fraction, which, in turn, has implications for our understanding of black hole seeding mechanisms.
AEROCRETE MADE WITH LOW SILICON TAILINGS OF CHENGCHAO IRON ORE MINE

The experimental study on the tailings from Chengchao Iron Ore Mine has been introduced briefly, By analyzing the chemical and mineral compositions of Chenchao Iron Ore. the technological process has been constituted. According to the comparison experiments. the optimal proportion of cement and lime. dosage of the additional silicon—materials, calcium-silicon proportion. dosage of aluminum powder and other parameters have been deemed. Aerocrete has been trial-produced successfully with the low-silicon irom are tailings. It has opened up a new way for the exploitation of the low-silicon iron ore tailings.