Miracle-dz/UniFAR-FTUs huggingface dataset:
PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report.
HICO-DET-SG and V-COCO-SG: New Data Splits for Evaluating the Systematic Generalization Performance of Human-Object Interaction Detection Models

Human-Object Interaction (HOI) detection is a task to localize humans and objects in an image and predict the interactions in human-object pairs. In real-world scenarios, HOI detection models are required systematic generalization, i.e., generalization to novel combinations of objects and interactions, because the train data are expected to cover a limited portion of all possible combinations. However, to our knowledge, no open benchmarks or previous work exist for evaluating the systematic generalization performance of HOI detection models. To address this issue, we created two new sets of HOI detection data splits named HICO-DET-SG and V-COCO-SG based on the HICO-DET and V-COCO datasets, respectively. When evaluated on the new data splits, the representative HOI detection models performed much more poorly than when evaluated on the original splits. This reveals that systematic generalization is a challenging goal in HOI detection. By analyzing the evaluation results, we also gain insights for improving the systematic generalization performance and identify four possible future research directions. We hope that our new data splits and presented analysis will encourage further research on systematic generalization in HOI detection.
AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression

Knowledge distillation has attracted a great deal of interest recently to compress large language models. However, existing knowledge distillation methods suffer from two limitations. First, the student model simply imitates the teacher’s behavior while ignoring the reasoning behind it. Second, these methods usually focus on the transfer of sophisticated model-specific knowledge but overlook data-specific knowledge. In this paper, we present a novel attribution-driven knowledge distillation approach, which explores the token-level rationale behind the teacher model based on Integrated Gradients (IG) and transfers attribution knowledge to the student model. To enhance the knowledge transfer of model reasoning and generalization, we further explore multi-view attribution distillation on all potential decisions of the teacher. Comprehensive experiments are conducted with BERT on the GLUE benchmark. The experimental results demonstrate the superior performance of our approach to several state-of-the-art methods.
Experimental nuclear charge density and theoretical description of the above-barrier light heavy-ion fusion process

Theoretical modeling of nucleus-nucleus collisions is often based on the nucleus-nucleus potential. One of the advanced methods for constructing this potential is the semi-microscopical double-folding model with M3Y-Paris NN-forces. Proton and neutron densities are significant components of this model. The correct nucleon density (ND) must reproduce the experimental nuclear charge density (NCD). However, those who deal with modeling the fusion process typically disregard this circumstance. We aim to achieve a good description of both the nuclear charge density and above-barrier fusion cross sections of even-even light nuclei with . We consider several versions of NDs available in literature and construct our own approximation for the ND of the even-even spherical nuclei 12C, 16O, and 40Ca, abbreviated as FE-density (Fermi+exponential). We carefully compare the NCDs resulting from different versions of NDs with the experimental NCDs. After finding the nucleus-nucleus potential using the double-folding model with the density dependent M3Y-Paris NN-forces and FE densities, we evaluate the above-barrier fusion cross sections for five reactions, 12C+12C, 12C+16O, 16O+16O, 16O+40Ca, and 40Ca+40Ca, for which experimental data are available. The cross sections are calculated using two approaches: a) the barrier penetration model and b) the trajectory model with surface friction (TM). To find the transmission coefficients for the TM, the Langevin equations are employed. For all considered reactions, our TM typically reproduces the above-barrier experimental cross sections within 10−15%. The only adjustable parameter of the model, the optimal friction strength , is found to be approximately 90 for the light reactions 12C+12C, 12C+16O, and 16O+16O and approximately 15 for the heavy reactions 16O+40Ca and 40Ca+40Ca. The latter findings are in reasonable agreement with the systematics found previously. Thus, the FE-recipe allows highly accurate and simultaneous reproduction of both the nuclear charge density and above-barrier fusion cross sections of five reactions involving 12C, 16O, and 40Ca nuclei.
Affective interdependence and welfare

Purely affective interaction allows the welfare of an individual to depend on her own actions and on the profile of welfare levels of others. Under an assumption on the structure of mutual affection that we interpret as"non-explosive mutual affection,"we show that equilibria of simultaneous-move affective interaction are Pareto optimal independently of whether or not an induced standard game exists. Moreover, if purely affective interaction induces a standard game, then an equilibrium profile of actions is a Nash equilibrium of the game, and this Nash equilibrium and Pareto optimal profile of strategies is locally dominant.
Cold PAWS: Unsupervised class discovery and the cold-start problem

In many machine learning applications, labeling datasets can be an arduous and time-consuming task. Although research has shown that semi-supervised learning techniques can achieve high accuracy with very few labels within the ﬁeld of computer vision, little attention has been given to how images within a dataset should be selected for labeling. In this paper, we propose a novel approach based on well-established self-supervised learning, clustering, and manifold learning techniques that address this challenge of selecting an informative image subset to label in the ﬁrst instance, which is known as the cold-start or unsupervised selective labelling problem. We test our approach using several publicly available datasets, namely CIFAR10, Imagenette, DeepWeeds, and EuroSAT, and observe improved performance with both supervised and semi-supervised learning strategies when our label selection strategy is used, in comparison to random sampling. We also obtain superior performance for the datasets considered with a much simpler approach compared to other methods in the literature.
A new approach to deformation of C*-algebras via coactions

We revisit the procedure of deformation of $C^*$-algebras via coactions of locally compact groups and extend the methods to cover deformations for maximal, reduced, and exotic coactions for a given group $G$ and circle-valued Borel $2$-cocycles on $G$. In the special case of reduced (or normal) coactions our deformation method substantially differs from -- but turns out to be equivalent to -- the ones used by previous authors, specially those given by Bhowmick, Neshveyev, and Sangha in [7]. Our approach yields all expected results, like a good behaviour of deformations under nuclearity, continuity of fields of $C^*$-algebras and $K$-theory invariance under mild conditions.
Rethinking People Analytics With Inverse Transparency by Design

Employees work in increasingly digital environments that enable advanced analytics. Yet, they lack oversight over the systems that process their data. That means that potential analysis errors or hidden biases are hard to uncover. Recent data protection legislation tries to tackle these issues, but it is inadequate. It does not prevent data misusage while at the same time stifling sensible use cases for data. We think the conflict between data protection and increasingly data-driven systems should be solved differently. When access to an employees' data is given, all usages should be made transparent to them, according to the concept of inverse transparency. This allows individuals to benefit from sensible data usage while addressing the potentially harmful consequences of data misusage. To accomplish this, we propose a new design approach for workforce analytics software we refer to as inverse transparency by design. To understand the developer and user perspectives on the proposal, we conduct two exploratory studies with students. First, we let small teams of developers implement analytics tools with inverse transparency by design to uncover how they judge the approach and how it materializes in their developed tools. We find that architectural changes are made without inhibiting core functionality. The developers consider our approach valuable and technically feasible. Second, we conduct a user study over three months to let participants experience the provided inverse transparency and reflect on their experience. The study models a software development workplace where most work processes are already digital. Participants perceive the transparency as beneficial and feel empowered by it. They unanimously agree that it would be an improvement for the workplace. We conclude that inverse transparency by design is a promising approach to realize accepted and responsible people analytics.
FACE: Evaluating Natural Language Generation with Fourier Analysis of Cross-Entropy

Measuring the distance between machine-produced and human language is a critical open problem. Inspired by empirical findings from psycholinguistics on the periodicity of entropy in language, we propose FACE, a set of metrics based on Fourier Analysis of the estimated Cross-Entropy of language, for measuring the similarity between model-generated and human-written languages. Based on an open-ended generation task and the experimental data from previous studies, we find that FACE can effectively identify the human-model gap, scales with model size, reflects the outcomes of different sampling methods for decoding, correlates well with other evaluation metrics and with human judgment scores.
Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation

Recent advances in Large Language Models (LLMs) have spurred interest in designing LLM-based agents for tasks that involve interaction with human and artificial agents. This paper addresses a key aspect in the design of such agents: Predicting human decision in off-policy evaluation (OPE), focusing on language-based persuasion games, where the agent's goal is to influence its partner's decisions through verbal messages. Using a dedicated application, we collected a dataset of 87K decisions from humans playing a repeated decision-making game with artificial agents. Our approach involves training a model on human interactions with one agents subset to predict decisions when interacting with another. To enhance off-policy performance, we propose a simulation technique involving interactions across the entire agent space and simulated decision makers. Our learning strategy yields significant OPE gains, e.g., improving prediction accuracy in the top 15% challenging cases by 7.1%. Our code and the large dataset we collected and generated are submitted as supplementary material and publicly available in our GitHub repository: https://github.com/eilamshapira/HumanChoicePrediction
Medium modifications to jet angularities using SCET with Glauber gluons

We perform a comprehensive analysis of medium modifications on ungroomed jet angularities, $\tau_a$, within the framework of Soft-Collinear Effective Theory with Glauber gluons (SCET$_{\rm G}$). Angularities are a one-parameter family of jet substructure observables with angularity exponent $a<2$ for infrared safety. Variation of the angularity exponent allows to modify the relative weighting of the collinear-to-soft radiations in the jet. In this article, we focus on angularity exponents $a<1$ and provide detailed results for $a=-1,0$, and $0.5$. Within SCET$_{\rm G}$, the interaction between jet and medium constituents is comprehended by off-shell Glauber gluons generated from the color gauge fields in the medium. While the medium modifications are incorporated in the jet function via the use of in-medium splitting functions, the soft function remains unmodified for $a<1$. For all values of $a$, we find that in the medium, the distributions are narrower and have a steeper fall compared to the vacuum ones. This redistribution of the ungroomed angularity spectrum is more apparent for a jet with a larger cone size. We also present results for the medium sensitivity towards $p_T$ of the jet and for a jet initiated in a less central event ($10-30\%$ centrality). Finally, we provide the ratios of nucleus-nucleus and proton-proton differential angularity distributions for different angularity exponents and for two values of the jet radius parameter.
Nonrelativistic limit of first-order relativistic viscous fluids

Out-of-equilibrium effects may play an important role in the dynamics of neutron star mergers and in heavy-ion collisions. Bemfica, Disconzi, Noronha and Kovtun (BDNK) recently derived a causal, locally well-posed, and modally stable relativistic fluid model that incorporates the effects of viscosity and heat diffusion. We study the non-relativistic limit of this fluid model and show that causality for relativistic motion restricts the transport coefficients in the non-relativistic limit. This restriction provides an upper bound on the ratio of the shear viscosity to the entropy density for fluids that can be described as relativistic within the BDNK model and can be exactly modeled using the Navier-Stokes equation in the non-relativistic limit. Furthermore, we show that the Fourier law of heat conduction must be modified by higher gradient corrections for such fluids. We also show that the non-relativistic limit of the BDNK equations of motion are never hyperbolic, in contrast to the non-relativistic limit of extended variable models, whose non-relativistic equations of motion can be hyperbolic or not depending on the scaling of the transport coefficients present in the auxiliary equations for viscous degrees of freedom.
Pragmatic Reasoning in Structured Signaling Games

In this work we introduce a structured signaling game, an extension of the classical signaling game with a similarity structure between meanings in the context, along with a variant of the Rational Speech Act (RSA) framework which we call structured-RSA (sRSA) for pragmatic reasoning in structured domains. We explore the behavior of the sRSA in the domain of color and show that pragmatic agents using sRSA on top of semantic representations, derived from the World Color Survey, attain efficiency very close to the information theoretic limit after only 1 or 2 levels of recursion. We also explore the interaction between pragmatic reasoning and learning in multi-agent reinforcement learning framework. Our results illustrate that artificial agents using sRSA develop communication closer to the information theoretic frontier compared to agents using RSA and just reinforcement learning. We also find that the ambiguity of the semantic representation increases as the pragmatic agents are allowed to perform deeper reasoning about each other during learning.
Potential-tuned magnetic switches and half-metallicity transition in zigzag graphene nanoribbons

Realizing controllable room-temperature ferromagnetism in carbon-based materials is one of recent prospects. The magnetism in graphene nanostructures reported previously is mostly localized by breaking the local sublattice imbalance. Here, we predict a robustly potential-tuned ferromagnetic domain lying between the inter-chain carbon atoms inside the zigzag graphene nanoribbons. We show that the effective zigzag edges provide the strong correlation background through narrowing the band width, while the internal Van Hove filling (VHF) provides the strong ferromagnetic background inherited from the bulk. The induced ferromagnetism exhibit interesting switching effect when the nominal VHF crosses the intra- and inter-chain region by tuning the potential drops. We further observe a robust half-metallicity transition from one spin channel to another within the same magnetic phase. These novel properties provide promising ways to manipulate the spin degree of freedom in graphene nanostructures.
Elementary mechanisms of shear-coupled grain boundary migration for different complexions of a copper tilt grain boundary

The migration of grain boundaries leads to grain growth in polycrystals and is one mechanism of grain-boundary-mediated plasticity, especially in metallic nanocrystals. This migration is due to the movement of dislocation-like defects, called disconnections, which couple to externally applied shear stresses. Here, we investigate a $\Sigma$19b symmetric tilt grain boundary without pre-existing defects using atomistic computer simulations with classical potentials. This specific grain boundary exhibits two different atomic structures with different microscopic degrees of freedom (complexions), called ``domino'' and ``pearl'' complexion. We show that the grain boundary migration is affected by both the formation energy of a disconnection dipole and the Peierls-like barrier required to move the disconnections. For the pearl complexion, the latter is much higher, leading to a high stress required for grain boundary migration at low temperatures. However, in absolute values, the Peierls barrier is low and can be overcome by thermal energy even at room temperature. Since the domino complexion has higher disconnection formation energies, it is more resistant to migration at room temperature and above.
Stopping Times of Boundaries: Relaxation and Continuity

We study the properties of the free boundaries and the corresponding hitting times in the context of optimal stopping in discrete time. We first prove the continuity of the map from the boundaries to the expected value of the corresponding stopping policy both in the supremum norm and also in a weaker, novel topology induced by the relaxed $L^\infty$ distance that we introduce. The latter is particularly useful when the optimal stopping boundary is only proved to be semicontinuous. Secondly, we study the connection between the hitting times, and their relaxations as widely employed in recent numerical methods. All these results together with the universal approximation capability of neural networks and the notion of inf/sup convolution are then used to provide a convergence analysis for the algorithm introduced in [Reppen, Soner, and Tissot-Daguette, Neural Optimal Stopping Boundary, 2022] for the numerical resolution of the exercise regions arising in the analysis of Bermudan type option.
Variational Classification

We present a latent variable generalisation of neural network softmax classification trained with cross-entropy loss, referred to as variational classification (VC). Our approach offers a novel probabilistic perspective on the highly familiar softmax classification model, to which it relates similarly to how variational and traditional autoencoders relate. We derive a training objective based on the evidence lower bound (ELBO) that is non-trivial to optimize, and therefore propose an adversarial approach to maximise it. We show that VC addresses an inherent inconsistency within softmax classification, whilst also allowing more flexible choices of prior distributions in the latent space in place of implicit assumptions revealed within off-the-shelf softmax classifiers. Empirical evaluation on image and text classification datasets demonstrates that variational classification maintains prediction accuracy while improving other desirable properties such as calibration and adversarial robustness, particularly under distribution shift and low data settings.
Physical Layer Authentication and Security Design in the Machine Learning Era

Security at the physical layer (PHY) is a salient research topic in wireless systems, and machine learning (ML) is emerging as a powerful tool for providing new data-driven security solutions. Therefore, the application of ML techniques to the PHY security is of crucial importance in the landscape of more and more data-driven wireless services. In this context, we first summarize the family of bespoke ML algorithms that are eminently suitable for wireless security. Then, we review the recent progress in ML-aided PHY security, where the term"PHY security"is classified into two different types: i) PHY authentication and ii) secure PHY transmission. Moreover, we treat neural networks as special types of ML and present how to deal with PHY security optimization problems using neural networks. Finally, we identify some major challenges and opportunities in tackling PHY security challenges by applying carefully tailored ML tools.
Bloch's conjecture for (anti-)autoequivalences on K3 surfaces

In this paper, we study Bloch's conjecture for zero cycles on K3 surfaces and hyper-K\"ahler varieties. We prove Bloch's conjecture for reflexive autoequivalences on K3 surfaces. This confirms Bloch's conjecture for all (anti)-symplectic autoequivalences of a K3 surface with Picard number $>2$. As a consequence, we prove Bloch's conjecture for (anti)-symplectic birational automorphisms on Bridgeland moduli space of a K3 surface with Picard number $>2$. Furthermore, we generalize Huybrechts' work in \cite{Huy12} to twisted K3 surfaces which asserts that the action of autoequivalences of a twisted K3 surface on the Chow group is determined by its action on the cohomology group. This allows us to prove Bloch's conjecture for symplectic birational automorphisms on arbitrary $K3^{[n]}$-type hyper-K\"ahler varieties preserving a birational Lagrangian fibration. We also establish an anti-symplectic version provided Voisin's filtration is the same as the filtration introduced in \cite{SYZ20}. Finally, we prove the constant cycle property for the fixed loci of anti-symplectic involutions on hyper-K\"ahler variety of $K3^{[n]}$-type if $n\leq 2$ or the N\'eron-Severi invariant sublattice has rank $>1$.