Miracle-dz/UniFAR-FTUs huggingface dataset:
Eliciting Human Preferences with Language Models

Language models (LMs) can be directed to perform target tasks by using labeled examples or natural language prompts. But selecting examples or writing prompts for can be challenging--especially in tasks that involve unusual edge cases, demand precise articulation of nebulous preferences, or require an accurate mental model of LM behavior. We propose to use *LMs themselves* to guide the task specification process. In this paper, we introduce **Generative Active Task Elicitation (GATE)**: a learning framework in which models elicit and infer intended behavior through free-form, language-based interaction with users. We study GATE in three domains: email validation, content recommendation, and moral reasoning. In preregistered experiments, we show that LMs prompted to perform GATE (e.g., by generating open-ended questions or synthesizing informative edge cases) elicit responses that are often more informative than user-written prompts or labels. Users report that interactive task elicitation requires less effort than prompting or example labeling and surfaces novel considerations not initially anticipated by users. Our findings suggest that LM-driven elicitation can be a powerful tool for aligning models to complex human preferences and values.
Enriched universal algebra

Following the classical approach of Birkhoff, we introduce enriched universal algebra. Given a suitable base of enrichment $\mathcal V$, we define a language $\mathbb L$ to be a collection of $(X,Y)$-ary function symbols whose arities are taken among the objects of $\mathcal V$. The class of $\mathbb L$-terms is constructed recursively from the symbols of $\mathbb L$, the morphisms in $\mathcal V$, and by incorporating the monoidal structure of $\mathcal V$. Then, $\mathbb L$-structures and interpretations of terms are defined, leading to enriched equational theories. In this framework we prove several fundamental theorems of universal algebra, including the characterization of algebras for finitary monads on $\mathcal V$ as models of an equational theories, and several Birkhoff-type theorems.
Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes

We consider the problem of designing sample efficient learning algorithms for infinite horizon discounted reward Markov Decision Process. Specifically, we propose the Accelerated Natural Policy Gradient (ANPG) algorithm that utilizes an accelerated stochastic gradient descent process to obtain the natural policy gradient. ANPG achieves $\mathcal{O}({\epsilon^{-2}})$ sample complexity and $\mathcal{O}(\epsilon^{-1})$ iteration complexity with general parameterization where $\epsilon$ defines the optimality error. This improves the state-of-the-art sample complexity by a $\log(\frac{1}{\epsilon})$ factor. ANPG is a first-order algorithm and unlike some existing literature, does not require the unverifiable assumption that the variance of importance sampling (IS) weights is upper bounded. In the class of Hessian-free and IS-free algorithms, ANPG beats the best-known sample complexity by a factor of $\mathcal{O}(\epsilon^{-\frac{1}{2}})$ and simultaneously matches their state-of-the-art iteration complexity.
A Kripke-Lewis semantics for belief update and belief revision

We provide a new characterization of both belief update and belief revision in terms of a Kripke-Lewis semantics. We consider frames consisting of a set of states, a Kripke belief relation and a Lewis selection function. Adding a valuation to a frame yields a model. Given a model and a state, we identify the initial belief set K with the set of formulas that are believed at that state and we identify either the updated belief set or the revised belief set, prompted by the input represented by formula A, as the set of formulas that are the consequent of conditionals that (1) are believed at that state and (2) have A as antecedent. We show that this class of models characterizes both the Katsuno-Mendelzon (KM) belief update functions and the AGM belief revision functions, in the following sense: (1) each model gives rise to a partial belief function that can be completed into a full KM/AGM update/revision function, and (2) for every KM/AGM update/revision function there is a model whose associated belief function coincides with it. The difference between update and revision can be reduced to two semantic properties that appear in a stronger form in revision relative to update, thus confirming the finding by Peppas et al. (1996) that,"for a fixed theory K, revising K is much the same as updating K"
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning

Large-scale LP problems from industry usually contain much redundancy that severely hurts the efficiency and reliability of solving LPs, making presolve (i.e., the problem simplification module) one of the most critical components in modern LP solvers. However, how to design high-quality presolve routines -- that is, the program determining (P1) which presolvers to select, (P2) in what order to execute, and (P3) when to stop -- remains a highly challenging task due to the extensive requirements on expert knowledge and the large search space. Due to the sequential decision property of the task and the lack of expert demonstrations, we propose a simple and efficient reinforcement learning (RL) framework -- namely, reinforcement learning for presolve (RL4Presolve) -- to tackle (P1)-(P3) simultaneously. Specifically, we formulate the routine design task as a Markov decision process and propose an RL framework with adaptive action sequences to generate high-quality presolve routines efficiently. Note that adaptive action sequences help learn complex behaviors efficiently and adapt to various benchmarks. Experiments on two solvers (open-source and commercial) and eight benchmarks (real-world and synthetic) demonstrate that RL4Presolve significantly and consistently improves the efficiency of solving large-scale LPs, especially on benchmarks from industry. Furthermore, we optimize the hard-coded presolve routines in LP solvers by extracting rules from learned policies for simple and efficient deployment to Huawei's supply chain. The results show encouraging economic and academic potential for incorporating machine learning to modern solvers.
A Hyperparameter Study for Quantum Kernel Methods

Quantum kernel methods are a promising method in quantum machine learning thanks to the guarantees connected to them. Their accessibility for analytic considerations also opens up the possibility of prescreening datasets based on their potential for a quantum advantage. To do so, earlier works developed the geometric difference, which can be understood as a closeness measure between two kernel-based machine learning approaches, most importantly between a quantum kernel and classical kernel. This metric links the quantum and classical model complexities. Therefore, it raises the question of whether the geometric difference, based on its relation to model complexity, can be a useful tool in evaluations other than for the potential for quantum advantage. In this work, we investigate the effects of hyperparameter choice on the model performance and the generalization gap between classical and quantum kernels. The importance of hyperparameter optimization is well known also for classical machine learning. Especially for the quantum Hamiltonian evolution feature map, the scaling of the input data has been shown to be crucial. However, there are additional parameters left to be optimized, like the best number of qubits to trace out before computing a projected quantum kernel. We investigate the influence of these hyperparameters and compare the classically reliable method of cross validation with the method of choosing based on the geometric difference. Based on the thorough investigation of the hyperparameters across 11 datasets we identified commodities that can be exploited when examining a new dataset. In addition, our findings contribute to better understanding of the applicability of the geometric difference.
CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations

Recent work has aimed to capture nuances of human behavior by using LLMs to simulate responses from particular demographics in settings like social science experiments and public opinion surveys. However, there are currently no established ways to discuss or evaluate the quality of such LLM simulations. Moreover, there is growing concern that these LLM simulations are flattened caricatures of the personas that they aim to simulate, failing to capture the multidimensionality of people and perpetuating stereotypes. To bridge these gaps, we present CoMPosT, a framework to characterize LLM simulations using four dimensions: Context, Model, Persona, and Topic. We use this framework to measure open-ended LLM simulations' susceptibility to caricature, defined via two criteria: individuation and exaggeration. We evaluate the level of caricature in scenarios from existing work on LLM simulations. We find that for GPT-4, simulations of certain demographics (political and marginalized groups) and topics (general, uncontroversial) are highly susceptible to caricature.
Epidemiological Investigation of the Peste des Petits Ruminants Outbreaks in Karnataka, India

| The present study reports the epidemiological and virological investigations of the twelve different outbreaks of Peste des Petits Ruminants (PPR) in goats and sheep flocks with considerable morbidity and mortality recorded from different places of Karnataka state in India during 2014-2015. Clinical samples were collected from the affected flocks or villages for laboratory investigation along with epidemiological parameters. The PPR virus (PPRV) antigen and its genome were detected in the infected tissues or swab materials by sandwich enzyme-linked immunosorbent assay (s-ELISA) and reverse transcription-polymerase chain reaction (RT-PCR). The significant epidemiological parameters recorded include young animals being severely affected than adult animals, which showed symptoms suggestive of PPR and changing pattern of disease in term of severity of gross lesions and clinical signs. The source of infection was traced back to introduction of new animals from other flocks or from other states. Despite regular vaccination of sheep and goats in the state, under National Control Programme on PPR (NCP-PPR), outbreaks need to be carefully monitored due mainly to production losses in small ruminants.
Jointly stationary solutions of periodic Burgers flow

For the one dimensional Burgers equation with a random and periodic forcing, it is well-known that there exits a family of invariant measures, each corresponding to a different average velocity. In this paper, we consider the coupled invariant measures and study how they change as the velocity parameter varies. We show that the derivative of the invariant measure with respect to the velocity parameter exists, and it can be interpreted as the steady state of a diffusion advected by the Burgers flow.
Determining the Origin of Very-high-energy Gamma Rays from Galactic Sources by Future Neutrino Observations

Recently, the Large High Altitude Air Shower Observatory (LHAASO) identified 12 gamma-ray sources emitting gamma rays with energies above 100 TeV, making them potential PeV cosmic-ray accelerators (PeVatrons). Neutrino observations are crucial in determining whether the gamma-ray radiation process is of hadronic or leptonic origin. In this paper, we study three detected sources, LHAASO J1908+0621, LHAASO J2018+3651, and LHAASO J2032+4102, which are also the most promising Galactic high-energy neutrino candidate sources with the lowest pretrial p-value based on the stacking searches testing for excess neutrino emission by the IceCube Neutrino Observatory. We study the lepto-hadronic scenario for the observed multiband spectra of these LHAASO sources considering the possible counterpart source of the LHAASO sources. The very-high-energy gamma rays are entirely attributed to the hadronic contribution; therefore, the most optimistic neutrino flux can be derived. Then, we evaluate the statistical significance (p-value) as a function of the observation time of IceCube and the next-generation IceCube-Gen2 neutrino observatory, respectively. Our results tend to disfavor that all gamma rays above 100 GeV from LHAASO J1908+0621 are of purely hadronic origin based on current IceCube observations, but the purely hadronic origin of gamma rays above 100 TeV is still possible. By IceCube-Gen2, the origin of gamma rays above 100 TeV from LHAASO J1908+0621 can be further determined at a 5σ significance level within a running time of ∼3 yr. For LHAASO J2018+3651 and LHAASO J2032+4102, the required running time of IceCube-Gen2 is ∼10 yr (3σ) and ∼10 yr (5σ), respectively. Future observations by the next-generation neutrino telescope will be crucial to understanding the particle acceleration and radiation processes inside the sources.
Quantifying Privacy Risks of Prompts in Visual Prompt Learning

Large-scale pre-trained models are increasingly adapted to downstream tasks through a new paradigm called prompt learning. In contrast to fine-tuning, prompt learning does not update the pre-trained model's parameters. Instead, it only learns an input perturbation, namely prompt, to be added to the downstream task data for predictions. Given the fast development of prompt learning, a well-generalized prompt inevitably becomes a valuable asset as significant effort and proprietary data are used to create it. This naturally raises the question of whether a prompt may leak the proprietary information of its training data. In this paper, we perform the first comprehensive privacy assessment of prompts learned by visual prompt learning through the lens of property inference and membership inference attacks. Our empirical evaluation shows that the prompts are vulnerable to both attacks. We also demonstrate that the adversary can mount a successful property inference attack with limited cost. Moreover, we show that membership inference attacks against prompts can be successful with relaxed adversarial assumptions. We further make some initial investigations on the defenses and observe that our method can mitigate the membership inference attacks with a decent utility-defense trade-off but fails to defend against property inference attacks. We hope our results can shed light on the privacy risks of the popular prompt learning paradigm. To facilitate the research in this direction, we will share our code and models with the community.
Harnessing Dataset Cartography for Improved Compositional Generalization in Transformers

Neural networks have revolutionized language modeling and excelled in various downstream tasks. However, the extent to which these models achieve compositional generalization comparable to human cognitive abilities remains a topic of debate. While existing approaches in the field have mainly focused on novel architectures and alternative learning paradigms, we introduce a pioneering method harnessing the power of dataset cartography (Swayamdipta et al., 2020). By strategically identifying a subset of compositional generalization data using this approach, we achieve a remarkable improvement in model accuracy, yielding enhancements of up to 10% on CFQ and COGS datasets. Notably, our technique incorporates dataset cartography as a curriculum learning criterion, eliminating the need for hyperparameter tuning while consistently achieving superior performance. Our findings highlight the untapped potential of dataset cartography in unleashing the full capabilities of compositional generalization within Transformer models. Our code is available at https://github.com/cyberiada/cartography-for-compositionality.
Modeling Post-Reionization HI Distributions in Fuzzy Dark Matter Cosmologies Using Conditional Normalizing Flows

Upcoming 21 cm intensity mapping experiments like the Square Kilometer Array (SKA) hold significant potential to constrain the properties of dark matter. In this work, we model neutral hydrogen (HI) distributions using high-resolution hydrodynamical N-body simulations of both cold dark matter (CDM) and fuzzy dark matter (FDM) cosmologies in the post-reionization redshift range of z = 3.42 − 4.94. We show that the HI abundance decreases in FDM-like cosmologies. Extreme FDM models with m ∼ 10−22 eV are at odds with a range of measurements. Due to the increased halo bias, the HI bias increases, paralleled by the damped Lyman-α (DLA) bias which we infer from the cross-section of DLAs. The distribution of the latter in extreme FDM models has a high median at the low-mass end, which can be traced to the high column density of cosmic filaments. FDM models exhibit a very similar abundance of DLAs compared to CDM while sub-DLAs are already less abundant. We study the prospects of detecting the brightest HI peaks with SKA1-Low at z = 4.94, indicating moderate signal-to-noise ratios (SNR) at angular resolution θA = 2′ with a rapidly declining SNR for lower values of θA. After training the conditional normalizing flow network HIGlow on 2D HI maps, we interpolate its latent space of axion masses to predict the peak flux for a new, synthetic FDM cosmology, finding good agreement with expectations. This work thus underscores the potential of normalizing flows in capturing complex, non-linear structures within HI maps, offering a versatile tool for conditional sample generation and prediction tasks.
Circumgalactic Medium at High Halo Masses—Signatures of Cold Gas Depletion in Luminous Red Galaxies

We study ultraviolet H i and metal-line transitions in the circumgalactic medium (CGM) of 15 massive, quenched luminous red galaxies (LRGs) at redshift z ∼ 0.5 and with impact parameters up to 400 kpc. We selected eight LRG–CGM systems to study general properties of the CGM around LRGs, while the other seven are already known to contain cool CGM gas from Mg ii optical studies (Mg ii-LRGs). In the general LRG population, we detect H i in four of eight LRGs, in all cases with N H I < 1016.7cm−2. In contrast, all Mg ii-LRGs show H i; for four LRGs, the H i column density is N H I ≳ 1018cm−2. The CGM of LRGs also shows low and intermediate ionized lines (such as C iii, C ii, Si iii, and Si ii) and highly ionized lines of O vi (we detect O vi around five of seven Mg ii-LRGs and one of eight in the random sample). Next, we combine our sample with literature LRGs and ≲L * galaxies, and we find that while for ≲L * galaxies CGM H i Lyα absorption is stronger as galaxies are more massive, the cool CGM traced by H i Lyα is suppressed above stellar masses of M* ∼ 1011.5 M ☉. While most LRG–CGM systems show weak or nondetectable O vi (equivalent width < 0.2 Å), a few LRG–CGM systems show strong O vi 1031, which in most cases likely originates from groups containing both an LRG and a blue star-forming neighboring galaxy.
Quantum spin chains with bond dissipation

We study the effect of bond dissipation on the one-dimensional antiferromagnetic spin-$1/2$ Heisenberg model. In analogy to the spin-Peierls problem, the dissipative bath is described by local harmonic oscillators that modulate the spin exchange coupling, but instead of a single boson frequency we consider a continuous bath spectrum $\propto \omega^s$. Using an exact quantum Monte Carlo method for retarded interactions, we show that for $s<1$ any finite coupling to the bath induces valence-bond-solid order, whereas for $s>1$ the critical phase of the isolated chain remains stable up to a finite critical coupling. We find that, even in the presence of the gapless bosonic spectrum, the spin-triplet gap remains well defined for any system size, from which we extract a dynamical critical exponent of $z=1$. We provide evidence for a Berezinskii-Kosterlitz-Thouless quantum phase transition that is governed by the SU(2)$_1$ Wess-Zumino-Witten model. Our results suggest that the critical properties of the dissipative system are the same as for the spin-Peierls model, irrespective of the different interaction range, i.e., power-law vs. exponential decay, of the retarded dimer-dimer interaction, indicating that the spin-Peierls criticality is robust with respect to the bosonic density of states.
Croissance en champ de clones d'Hevea brasiliensis issus de culture in vitro

Trois essais, plantes en Cote d'Ivoire, analysent la croissance de microboutures de 6 ans issues de semenceaux, d'embryons somatiques de 45 mois de PB 260 et PR 107 et de mitroboutures de 31 mois de IRCA 18. Les resultats montrent que les vitroplants d'hevea, produits par embryogenese somatique ou par microbouturage, ont toujours une croissance egale ou superieure a celle des greffe classiques. Les differences sont analysees est fonction de la juvenilite des plants-meres, des caracteristiques clonales et du niveau de developpement des plants au moment du planting. Ces premiers resultats sont favorables aux vitroplants, d'autant que l'analyse montre qu'il est indispensable d'optimiser la vigueur des plants issus de la culture in vitro ainsi que leur croissance en pepiniere avant de mesurer le gain de croissance et de production reelletnent apporte par ce nouveau materiel vegetal.
Thermodynamics of frozen stars

The frozen star is a recent proposal for a non-singular solution of Einstein's equations that describes an ultracompact object which closely resembles a black hole from an external perspective. The frozen star is also meant to be an alternative, classical description of an earlier proposal, the highly quantum polymer model. Here, we show that the thermodynamic properties of frozen stars closely resemble those of black holes: Frozen stars radiate thermally, with a temperature and an entropy that are perturbatively close to those of black holes of the same mass. Their entropy is calculated using the Euclidean-action method of Gibbons and Hawking. We then discuss their dynamical formation by estimating the probability for a collapsing shell of"normal"matter to transition, quantum mechanically, into a frozen star. This calculation followed from a reinterpretation of a transitional region between the Euclidean frozen star and its Schwarzschild exterior as a Euclidean instanton that mediates a phase transition from the Minkowski interior of an incipient Schwarzschild black hole to a microstate of the frozen star interior. It is shown that, up to negligible corrections, the probability of this transition is $e^{-A/4}$, with $A$ being the star's surface area. Taking into account that the dimension of the phase space is $e^{+A/4}$, we conclude that the total probability for the formation of the frozen star is of order unity. The duration of this transition is estimated, which we then use to argue, relying on an analogy to previous results, about the scaling of the magnitude of the off-diagonal corrections to the number operator for the Hawking-like particles. Such scaling was shown to imply that the corresponding Page curve indeed starts to go down at about the Page time, as required by unitarity.
Learning Co-Speech Gesture for Multimodal Aphasia Type Detection

Aphasia, a language disorder resulting from brain damage, requires accurate identification of specific aphasia types, such as Broca's and Wernicke's aphasia, for effective treatment. However, little attention has been paid to developing methods to detect different types of aphasia. Recognizing the importance of analyzing co-speech gestures for distinguish aphasia types, we propose a multimodal graph neural network for aphasia type detection using speech and corresponding gesture patterns. By learning the correlation between the speech and gesture modalities for each aphasia type, our model can generate textual representations sensitive to gesture information, leading to accurate aphasia type detection. Extensive experiments demonstrate the superiority of our approach over existing methods, achieving state-of-the-art results (F1 84.2\%). We also show that gesture features outperform acoustic features, highlighting the significance of gesture expression in detecting aphasia types. We provide the codes for reproducibility purposes.