Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest

Large neural networks can now generate jokes, but do they really “understand” humor? We challenge AI models with three tasks derived from the New Yorker Cartoon Caption Contest: matching a joke to a cartoon, identifying a winning caption, and explaining why a winning caption is funny. These tasks encapsulate progressively more sophisticated aspects of “understanding” a cartoon; key elements are the complex, often surprising relationships between images and captions and the frequent inclusion of indirect and playful allusions to human experience and culture. We investigate both multimodal and language-only models: the former are challenged with the cartoon images directly, while the latter are given multifaceted descriptions of the visual scene to simulate human-level visual understanding. We find that both types of models struggle at all three tasks. For example, our best multimodal models fall 30 accuracy points behind human performance on the matching task, and, even when provided ground-truth visual scene descriptors, human-authored explanations are preferred head-to-head over the best machine-authored ones (few-shot GPT-4) in more than 2/3 of cases. We release models, code, leaderboard, and corpus, which includes newly-gathered annotations describing the image’s locations/entities, what’s unusual in the scene, and an explanation of the joke.
PaLI: A Jointly-Scaled Multilingual Language-Image Model

Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI (Pathways Language and Image model), a model that extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages. To train PaLI, we make use of large pre-trained encoder-decoder language models and Vision Transformers (ViTs). This allows us to capitalize on their existing capabilities and leverage the substantial cost of training them. We find that joint scaling of the vision and language components is important. Since existing Transformers for language are much larger than their vision counterparts, we train a large, 4-billion parameter ViT (ViT-e) to quantify the benefits from even larger-capacity vision models. To train PaLI, we create a large multilingual mix of pretraining tasks, based on a new image-text training set containing 10B images and texts in over 100 languages. PaLI achieves state-of-the-art in multiple vision and language tasks (such as captioning, visual question-answering, scene-text understanding), while retaining a simple, modular, and scalable design.
A New Integrative Method for Multigroup Comparisons of Censored Survival Outcomes in Multiple Observational Studies

In observational studies, covariate imbalance generates confounding, resulting in biased comparisons. Although propensity score-based weighting approaches facilitate unconfounded group comparisons for implicit target populations, existing techniques may not directly or efficiently analyze multiple studies with multiple groups and provide results generalizable to larger populations. Moreover, few methods deliver precise inferences for various estimands with censored survival outcomes. We propose a new concordant target population approach, which constructs generalized balancing weights and realistic target populations. Our method can incorporate researcher-specified natural population attributes and synthesize information by appropriately compensating for over- or under-represented groups to achieve covariate balance. The constructed {concordant} weights are agnostic to specific estimators, estimands, and outcomes and maximize the effective sample size (ESS) for more precise inferences. Simulation studies and descriptive comparisons of glioblastoma outcomes of racial groups in multiple TCGA studies demonstrate the strategy's practical advantages. Unlike existing weighting techniques, the proposed concordant target population revealed a drastically different result: Blacks were more vulnerable and endured significantly worse prognoses; Asians had the best outcomes with a median overall survival of 1,024 (SE: 15.2) days, compared to 384 (SE: 1.2) and 329 (SE: 19.7) days for Whites and Blacks, respectively.
Market Design with Deferred Acceptance: A Recipe for Policymaking

We introduce a method to derive from a characterization of institutional choice rules (or priority rules), a characterization of the Gale-Shapley deferred-acceptance (DA) matching rule based on these choice rules. We apply our method to school choice in Chile, where we design choice rules for schools that are uniquely compatible with the School Inclusion Law and derive a set of matching properties, compatible with the law, that characterizes the DA rule based on the designed choice rules. Our method provides a recipe for establishing such results and can help policymakers decide on which allocation rule to use in practice.
Improving Self-Supervised Learning by Characterizing Idealized Representations

Despite the empirical successes of self-supervised learning (SSL) methods, it is unclear what characteristics of their representations lead to high downstream accuracies. In this work, we characterize properties that SSL representations should ideally satisfy. Specifically, we prove necessary and sufficient conditions such that for any task invariant to given data augmentations, desired probes (e.g., linear or MLP) trained on that representation attain perfect accuracy. These requirements lead to a unifying conceptual framework for improving existing SSL methods and deriving new ones. For contrastive learning, our framework prescribes simple but significant improvements to previous methods such as using asymmetric projection heads. For non-contrastive learning, we use our framework to derive a simple and novel objective. Our resulting SSL algorithms outperform baselines on standard benchmarks, including SwAV+multicrops on linear probing of ImageNet.
Structural, point-free, non-Hausdorff topological realization of Borel groupoid actions

We extend the Becker–Kechris topological realization and change-of-topology theorems for Polish group actions in several directions. For Polish group actions, we prove a single result that implies the original Becker–Kechris theorems, as well as Sami’s and Hjorth’s sharpenings adapted levelwise to the Borel hierarchy; automatic continuity of Borel actions via homeomorphisms and the equivalence of ‘potentially open’ versus ‘orbitwise open’ Borel sets. We also characterize ‘potentially open’ n-ary relations, thus yielding a topological realization theorem for invariant Borel first-order structures. We then generalize to groupoid actions and prove a result subsuming Lupini’s Becker–Kechris-type theorems for open Polish groupoids, newly adapted to the Borel hierarchy, as well as topological realizations of actions on fiberwise topological bundles and bundles of first-order structures. Our proof method is new even in the classical case of Polish groups and is based entirely on formal algebraic properties of category quantifiers; in particular, we make no use of either metrizability or the strong Choquet game. Consequently, our proofs work equally well in the non-Hausdorff context, for open quasi-Polish groupoids and more generally in the point-free context, for open localic groupoids.
Tackling the Unique Challenges of Low-frequency Solar Polarimetry with the Square Kilometre Array Low Precursor: Pipeline Implementation

The dynamics and the structure of the solar corona are determined by its magnetic field. Measuring coronal magnetic fields is, however, extremely hard. The polarization of low-frequency radio emissions has long been recognized as one of the few effective observational probes of magnetic fields in the mid and high corona. However, the extreme intrinsic variability of this emission, the limited ability of most of the available existing instrumentation (until recently) to capture it, and the technical challenges involved have all contributed to its use being severely limited. The high dynamic-range spectropolarimetric snapshot imaging capability that is needed for radio coronal magnetography is now within reach. This has been enabled by the confluence of data from the Murchison Widefield Array (MWA), a Square Kilometre Array (SKA) precursor, and our unsupervised and robust polarization calibration and imaging software pipeline dedicated to the Sun—Polarimetry using the Automated Imaging Routine for Compact Arrays of the Radio Sun (P-AIRCARS). Here, we present the architecture and implementation details of P-AIRCARS. Although the present implementation of P-AIRCARS is tuned to the MWA, the algorithm itself can easily be adapted for future arrays, such as SKA1-Low. We hope and expect that P-AIRCARS will enable exciting new science with instruments like the MWA, and that it will encourage the wider use of radio imaging in the larger solar physics community.
vec2text with Round-Trip Translations

We investigate models that can generate arbitrary natural language text (e.g. all English sentences) from a bounded, convex and well-behaved control space. We call them universal vec2text models. Such models would allow making semantic decisions in the vector space (e.g. via reinforcement learning) while the natural language generation is handled by the vec2text model. We propose four desired properties: universality, diversity, fluency, and semantic structure, that such vec2text models should possess and we provide quantitative and qualitative methods to assess them. We implement a vec2text model by adding a bottleneck to a 250M parameters Transformer model and training it with an auto-encoding objective on 400M sentences (10B tokens) extracted from a massive web corpus. We propose a simple data augmentation technique based on round-trip translations and show in extensive experiments that the resulting vec2text model surprisingly leads to vector spaces that fulfill our four desired properties and that this model strongly outperforms both standard and denoising auto-encoders.
Nonlinear Young integrals and differential systems in H\"older media

For Holder continuous functions $W(t,x)$ and $\phi_t$, we define nonlinear integral $\int_a^b W(dt, \phi_t)$ in various senses, including Ito-Skorohod and pathwise. We study their properties and relations. The stochastic flow in a time dependent rough vector field associated with $\dot \phi_t=(\partial _tW)(t, \phi_t)$ is also studied and its applications to the transport equation $\partial _t u(t,x)-\partial _t W(t,x)\nabla u(t,x)=0$ in rough media is given. The Feynman-Kac solution to the stochastic partial differential equation with random coefficients $\partial _t u(t,x)+Lu(t,x) +u(t,x)W(t,x)=0$ are given, where $L$ is a second order elliptic differential operator with random coefficients (dependent on $W$). To establish such formula the main difficulty is the exponential integrability of some nonlinear integrals, which is proved to be true under some mild conditions on the covariance of $W$. Along the way, we also obtain an upper bound for increments of stochastic processes on multidimensional rectangles by majorizing measures.
Unsteadiness in transonic shock-wave/boundary-layer interactions: experimental investigation and global stability analysis

A transonic interaction between a shock wave and a turbulent boundary layer is experimentally and theoretically investigated. The configuration is a transonic channel flow over a bump, where a shock wave causes the separation of the boundary layer in the form of a recirculating bubble downstream of the shock foot. Different experimental techniques allow for the identification of the main unsteadiness features. As recognised in similar shock-wave/boundary-layer interactions, the flow field exhibits two distinct characteristic frequencies, whose origins are still controversial: a low-frequency motion which primarily affects the shock wave; and medium-frequency perturbations localised in the shear layer. A Fourier analysis of a series of Schlieren snapshots is performed to precisely characterise the structure of the perturbations at low- and medium-frequencies. Then, the Reynolds-averaged Navier–Stokes (RANS) equations closed with a Spalart–Allmaras turbulence model are solved to obtain a mean flow, which favourably compares with the experimental results. A global stability analysis based on the linearization of the full RANS equations is then performed. The eigenvalues of the Jacobian operator are all damped, indicating that the interaction dynamic cannot be explained by the existence of unstable global modes. The input/output behaviour of the flow is then analysed by performing a singular-value decomposition of the Resolvent operator; pseudo-resonances of the flow may be identified and optimal forcings/responses determined as a function of frequency. It is found that the flow strongly amplifies both medium-frequency perturbations, generating fluctuations in the mixing layer, and low-frequency perturbations, affecting the shock wave. The structure of the optimal perturbations and the preferred frequencies agree with the experimental observations.
Heat-based circuits using quantum rectification

With increased power consumption of modern computer components, a heat-based computer has become ever more relevant due to a lower power expense to process logic bits of information. In a heat-based computer, computations are performed by driving heat currents through a circuit using a temperature difference. Utilizing harmonic oscillators and three-level quantum rectifiers as base components, we study three different heat-based circuits: a series configuration of diodes, a parallel configuration of diodes, and a diode bridge rectifier. We demonstrate the required functionality of each circuit for use as heat-based analogues of standard electronic components. Furthermore, the diode bridge rectifier is found to give consistent output bias independent of the input bias thus rectifying the input. Our results prove the theoretical feasibility of combining heat current components into heat-based circuits. The three circuits should be realizable using several of the current quantum technology platforms.
Empowering GNNs with Fine-grained Communication-Computation Pipelining on Multi-GPU Platforms

The increasing size of input graphs for graph neural networks (GNNs) highlights the demand for using multi-GPU platforms. However, existing multi-GPU GNN solutions suffer from inferior performance due to imbalanced computation and inefﬁcient communication. To this end, we propose MGG , a novel system design to accelerate GNNs on multi-GPU platforms via a GPU-centric software pipeline. MGG explores the potential of hiding remote memory access latency in GNN workloads through ﬁne-grained computation-communication pipelining. Speciﬁcally, MGG introduces a pipeline-aware workload management strategy and a hybrid data layout de-sign to facilitate the communication-computation overlapping. MGG implements an optimized pipeline-centric kernel. It includes workload interleaving and warp-based mapping for efﬁcient GPU kernel operation pipelining and specialized memory designs and optimizations for better data access performance. Besides, MGG incorporates lightweight analytical modeling and optimization heuristics to dynamically improve the GNN execution performance for different settings at run-time. Comprehensive experiments demonstrate that MGG outperforms state-of-the-art multi-GPU systems across various GNN settings: on average 3 . 65 × faster than multi-GPU systems with a uniﬁed virtual memory design and on average 7 . 38 × faster than DGCL framework.
Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora

Within textual emotion classification, the set of relevant labels depends on the domain and application scenario and might not be known at the time of model development. This conflicts with the classical paradigm of supervised learning in which the labels need to be predefined. A solution to obtain a model with a flexible set of labels is to use the paradigm of zero-shot learning as a natural language inference task, which in addition adds the advantage of not needing any labeled training data. This raises the question how to prompt a natural language inference model for zero-shot learning emotion classification. Options for prompt formulations include the emotion name anger alone or the statement “This text expresses anger”. With this paper, we analyze how sensitive a natural language inference-based zero-shot-learning classifier is to such changes to the prompt under consideration of the corpus: How carefully does the prompt need to be selected? We perform experiments on an established set of emotion datasets presenting different language registers according to different sources (tweets, events, blogs) with three natural language inference models and show that indeed the choice of a particular prompt formulation needs to fit to the corpus. We show that this challenge can be tackled with combinations of multiple prompts. Such ensemble is more robust across corpora than individual prompts and shows nearly the same performance as the individual best prompt for a particular corpus.
Light scalars in the Weinberg 3HDM potential with spontaneous CP violation

The Z 2 × Z 2 -symmetric 3HDM potential, sometimes called Weinberg’s 3HDM potential, can accommodate both explicit and spontaneous CP violation as well as natural ﬂavour conservation and is hence relevant for studies of CP violation coming from a realistic extended Higgs sector. The model has an interesting regime, controlled by a single parameter, where it acquires an approximate global 𝑈 ( 1 ) × 𝑈 ( 1 ) symmetry which causes CP violating eﬀects to be small and two pseudo-Goldstone bosons to be present in the scalar spectrum. From parameter space scans, we ﬁnd that, in a realistic implementation featuring an SM-like Higgs boson, this model predominantely leads to the existence of light additional Higgs bosons with a mass lower than the SM-like Higgs boson.
Electronic structure and bulk properties of MB6 and MB12 borides

Ab initio band structure calculations are carried out for the higher borides MB6 and MB12. High-precision measurements of the elastic constants are performed for the compounds ZrB12, HoB12, ErB12, TmB12, LuB12, YB6 and LaB6 at low temperatures. The bulk properties of the borides are analyzed on the basis of the calculated equations of states and balanced crystal orbital overlap populations. Our calculations indicate that hexaborides with divalent metals, CaB6, SrB6, BaB6, and YbB6, are semiconductors with small energy gaps. The metallic MB6 hexaborides with trivalent M atoms are found to possess larger bulk moduli values. For dodecaborides the bulk moduli are found to be higher for MB12 with increased filling of the conduction band (ZrB12, HfB12, UB12) in comparison with M3+B12 compounds. The total energy calculations for different magnetic configurations in YbB12 point to the possibility of antiferromagnetic coupling between Yb3+ ions.
Measurement-Feedback Control with Optimal Data-Dependent Regret

—Inspired by online learning, data-dependent regret has recently been proposed as a criterion for controller design. In the regret-optimal control paradigm, causal controllers are designed to minimize regret against a hypothetical optimal noncausal controller , which selects the globally cost-minimizing sequence of control actions given noncausal access to the disturbance sequence. We extend regret-optimal control to the more challenging measurement-feedback setting, where the online controller must compete against the optimal noncausal controller without directly observing the state or the driving distur- bance. We show that no measurement-feedback controller can have bounded competitive ratio or regret which is bounded by the pathlength of the measurement disturbance. We do derive, however, a controller whose regret has optimal dependence on the joint energy of the driving and measurement disturbances, and another controller whose regret has optimal dependence on the pathlength of the driving disturbance and the energy of the measurement disturbance. The key technique we introduce is a reduction from regret-optimal measurement-feedback control to H ∞ -optimal measurement-feedback control in a synthetic system. We present numerical simulations which illustrate the efﬁcacy of our proposed control algorithms.
Tracing Interstellar Heating: An ALCHEMI Measurement of the HCN Isomers in NGC 253

We analyze HCN and HNC emission in the nearby starburst galaxy NGC 253 to investigate its effectiveness in tracing heating processes associated with star formation. This study uses multiple HCN and HNC rotational transitions observed using the Atacama Large Millimeter/submillimeter Array via the ALCHEMI Large Program. To understand the conditions and associated heating mechanisms within NGC 253's dense gas, we employ Bayesian nested sampling techniques applied to chemical and radiative transfer models, which are constrained using our HCN and HNC measurements. We find that the volume density nH2 and cosmic-ray ionization rate (CRIR) ζ are enhanced by about an order of magnitude in the galaxy’s central regions as compared to those further from the nucleus. In NGC 253's central giant molecular clouds (GMCs), where observed HCN/HNC abundance ratios are the lowest, n ∼ 105.5 cm−3 and ζ ∼ 10−12 s−1 (greater than 104 times the average Galactic rate). We find a positive correlation in the association of both density and CRIR with the number of star formation-related heating sources (supernova remnants, H ii regions, and super hot cores) located in each GMC, as well as a correlation between CRIRs and supernova rates. Additionally, we see an anticorrelation between the HCN/HNC ratio and CRIR, indicating that this ratio will be lower in regions where ζ is higher. Though previous studies suggested HCN and HNC may reveal strong mechanical heating processes in NGC 253's CMZ, we find cosmic-ray heating dominates the heating budget, and mechanical heating does not play a significant role in the HCN and HNC chemistry.
Deep Speech Synthesis from Articulatory Representations

In the articulatory synthesis task, speech is synthesized from input features containing information about the physical behavior of the human vocal tract. This task provides a promising direction for speech synthesis research, as the articulatory space is compact, smooth, and interpretable. Current works have highlighted the potential for deep learning models to perform articulatory synthesis. However, it remains unclear whether these models can achieve the efficiency and fidelity of the human speech production system. To help bridge this gap, we propose a time-domain articulatory synthesis methodology and demonstrate its efficacy with both electromagnetic articulography (EMA) and synthetic articulatory feature inputs. Our model is computationally efficient and achieves a transcription word error rate (WER) of 18.5% for the EMA-to-speech task, yielding an improvement of 11.6% compared to prior work. Through interpolation experiments, we also highlight the generalizability and interpretability of our approach.