Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic Bandits

Existing approaches to fairness in stochastic multi-armed bandits (MAB) primarily focus on exposure guarantee to individual arms. When arms are naturally grouped by certain attribute(s), we propose Bi-Level Fairness, which considers two levels of fairness. At the first level, Bi-Level Fairness guarantees a certain minimum exposure to each group. To address the unbalanced allocation of pulls to individual arms within a group, we consider meritocratic fairness at the second level, which ensures that each arm is pulled according to its merit within the group. Our work shows that we can adapt a UCB-based algorithm to achieve a Bi-Level Fairness by providing (i) anytime Group Exposure Fairness guarantees and (ii) ensuring individual-level Meritocratic Fairness within each group. We first show that one can decompose regret bounds into two components: (a) regret due to anytime group exposure fairness and (b) regret due to meritocratic fairness within each group. Our proposed algorithm BF-UCB balances these two regrets optimally to achieve the upper bound of $O(\sqrt{T})$ on regret; $T$ being the stopping time. With the help of simulated experiments, we further show that BF-UCB achieves sub-linear regret; provides better group and individual exposure guarantees compared to existing algorithms; and does not result in a significant drop in reward with respect to UCB algorithm, which does not impose any fairness constraint.
Dual-modal Tactile E-skin: Enabling Bidirectional Human-Robot Interaction via Integrated Tactile Perception and Feedback

To foster an immersive and natural human-robot interaction, the implementation of tactile perception and feedback becomes imperative, effectively bridging the conventional sensory gap. In this paper, we propose a dual-modal electronic skin (e-skin) that integrates magnetic tactile sensing and vibration feedback for enhanced human-robot interaction. The dual-modal tactile e-skin offers multi-functional tactile sensing and programmable haptic feedback, underpinned by a layered structure comprised of flexible magnetic films, soft silicone, a Hall sensor and actuator array, and a microcontroller unit. The e-skin captures the magnetic field changes caused by subtle deformations through Hall sensors, employing deep learning for accurate tactile perception. Simultaneously, the actuator array generates mechanical vibrations to facilitate haptic feedback, delivering diverse mechanical stimuli. Notably, the dual-modal e-skin is capable of transmitting tactile information bidirectionally, enabling object recognition and fine-weighing operations. This bidirectional tactile interaction framework will enhance the immersion and efficiency of interactions between humans and robots.
WebLINX: Real-World Website Navigation with Multi-Turn Dialogue

We propose the problem of conversational web navigation, where a digital agent controls a web browser and follows user instructions to solve real-world tasks in a multi-turn dialogue fashion. To support this problem, we introduce WEBLINX - a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. Our benchmark covers a broad range of patterns on over 150 real-world websites and can be used to train and evaluate agents in diverse scenarios. Due to the magnitude of information present, Large Language Models (LLMs) cannot process entire web pages in real-time. To solve this bottleneck, we design a retrieval-inspired model that efficiently prunes HTML pages by ranking relevant elements. We use the selected elements, along with screenshots and action history, to assess a variety of models for their ability to replicate human behavior when navigating the web. Our experiments span from small text-only to proprietary multimodal LLMs. We find that smaller finetuned decoders surpass the best zero-shot LLMs (including GPT-4V), but also larger finetuned multimodal models which were explicitly pretrained on screenshots. However, all finetuned models struggle to generalize to unseen websites. Our findings highlight the need for large multimodal models that can generalize to novel settings. Our code, data and models are available for research: https://mcgill-nlp.github. io/weblinx
Analysing the Sample Complexity of Opponent Shaping

Learning in general-sum games often yields collectively sub-optimal results. Addressing this, opponent shaping (OS) methods actively guide the learning processes of other agents, empirically leading to improved individual and group performances in many settings. Early OS methods use higher-order derivatives to shape the learning of co-players, making them unsuitable for shaping multiple learning steps. Follow-up work, Model-free Opponent Shaping (M-FOS), addresses these by reframing the OS problem as a meta-game. In contrast to early OS methods, there is little theoretical understanding of the M-FOS framework. Providing theoretical guarantees for M-FOS is hard because A) there is little literature on theoretical sample complexity bounds for meta-reinforcement learning B) M-FOS operates in continuous state and action spaces, so theoretical analysis is challenging. In this work, we present R-FOS, a tabular version of M-FOS that is more suitable for theoretical analysis. R-FOS discretises the continuous meta-game MDP into a tabular MDP. Within this discretised MDP, we adapt the $R_{max}$ algorithm, most prominently used to derive PAC-bounds for MDPs, as the meta-learner in the R-FOS algorithm. We derive a sample complexity bound that is exponential in the cardinality of the inner state and action space and the number of agents. Our bound guarantees that, with high probability, the final policy learned by an R-FOS agent is close to the optimal policy, apart from a constant factor. Finally, we investigate how R-FOS's sample complexity scales in the size of state-action space. Our theoretical results on scaling are supported empirically in the Matching Pennies environment.
On string theory on deformed BTZ and $T\bar T$

Aspects of superstring theory on deformed BTZ black holes, formed near $k$~NS5 branes by $p$~fundamental strings, and single-trace $T\bar T$ holography, are presented. The excitation energy of a singly wound long string plus its contribution to the energy of the black hole, $1/p$ fraction of the black-hole energy, is the same as that in $T\bar T$ deformed $CFT_2$ with $c=6k$. This supports the claim that the black hole can be thought of as a state in a symmetric product of $p$~$CFT_2$'s of central charge~$6k$, where the black-hole energy is split equally among all $p$ factors. A comment on superstring theory on deformed global $AdS_3$ is also presented.
A Sampling Theory Perspective on Activations for Implicit Neural Representations

Implicit Neural Representations (INRs) have gained popularity for encoding signals as compact, differentiable entities. While commonly using techniques like Fourier positional encodings or non-traditional activation functions (e.g., Gaussian, sinusoid, or wavelets) to capture high-frequency content, their properties lack exploration within a unified theoretical framework. Addressing this gap, we conduct a comprehensive analysis of these activations from a sampling theory perspective. Our investigation reveals that sinc activations, previously unused in conjunction with INRs, are theoretically optimal for signal encoding. Additionally, we establish a connection between dynamical systems and INRs, leveraging sampling theory to bridge these two paradigms.
Splitting probabilities as optimal controllers of rare reactive events

The committor constitutes the primary quantity of interest within chemical kinetics as it is understood to encode the ideal reaction coordinate for a rare reactive event. We show the generative utility of the committor, in that it can be used explicitly to produce a reactive trajectory ensemble that exhibits numerically exact statistics as that of the original transition path ensemble. This is done by relating a time-dependent analogue of the committor that solves a generalized bridge problem, to the splitting probability that solves a boundary value problem under a bistable assumption. By invoking stochastic optimal control and spectral theory, we derive a general form for the optimal controller of a bridge process that connects two metastable states expressed in terms of the splitting probability. This formalism offers an alternative perspective into the role of the committor and its gradients, in that they encode forcefields that guarantee reactivity, generating trajectories that are statistically identical to the way that a system would react autonomously.
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels

In the evolving landscape of federated learning (FL), addressing label noise presents unique challenges due to the decentralized and diverse nature of data collection across clients. Traditional centralized learning approaches to mitigate label noise are constrained in FL by privacy concerns and the heterogeneity of client data. This paper revisits early-learning regularization, introducing an innovative strategy, Federated Label-mixture Regularization (FLR). FLR adeptly adapts to FL's complexities by generating new pseudo labels, blending local and global model predictions. This method not only enhances the accuracy of the global model in both i.i. d. and non-i.i. d. settings but also effectively counters the memorization of noisy labels. Demonstrating compatibility with existing label noise and FL techniques, FLR paves the way for improved generalization in FL environments fraught with label inaccuracies.
Kontextbasierte Aktivitätserkennung - Synergie von Mensch und Technik in der Social Networked Industry

In a social networked industry, the focus is on collaboration between humans and technology. Communication is the basic prerequisite for synergetic collaboration between all players. It includes non-verbal as well as verbal interactions. To enable non-verbal interaction, machines must be able to detect and understand human movements. This article presents the ongoing fundamental research on the analysis of human movements using sensor-based activity recognition and identifies potential for a transfer to industrial applications. The focus is on the practical feasibility of activity recognition by adding further data streams such as the position data of logistical objects and tools, meaning the context in which a certain activity is carried out. -- In der Social Networked Industry steht die Zusammenarbeit von Mensch und Technik im Vordergrund. Grundvoraussetzung f\"ur eine synergetische Zusammenarbeit aller Akteure ist die Kommunikation, welche neben verbalen auch nonverbale Interaktionen umfasst. Um eine nonverbale Interaktion zu erm\"oglichen, m\"ussen Maschinen in der Lage sein, menschliche Bewegungen zu erfassen und zu verstehen. Dieser Beitrag stellt die laufende Grundlagenforschung zur Analyse menschlicher Bewegungen mittels sensorgest\"utzter Aktivit\"atserkennung vor und zeigt Ankn\"upfungspunkte f\"ur einen Transfer in industrielle Anwendungen. Im Fokus steht die Praxistauglichkeit der Aktivit\"atserkennung durch die Hinzunahme weiterer Datenstr\"ome wie beispielsweise den Positionsdaten logistischer Objekte und Hilfsmitteln, d. h. dem Kontext, in dem eine gewisse Aktivit\"at ausgef\"uhrt wird.
Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images

We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric context in existing methods impedes their ability to accurately enforce the consistency between the different geometric properties, thereby leading to a bottleneck of geometric estimation quality. We therefore propose the Adaptive Surface Normal (ASN) constraint, a simple yet efficient method. Our approach extracts geometric context that encodes the geometric variations present in the input image and correlates depth estimation with geometric constraints. By dynamically determining reliable local geometry from randomly sampled candidates, we establish a surface normal constraint, where the validity of these candidates is evaluated using the geometric context. Furthermore, our normal estimation leverages the geometric context to prioritize regions that exhibit significant geometric variations, which makes the predicted normals accurately capture intricate and detailed geometric information. Through the integration of geometric context, our method unifies depth and surface normal estimations within a cohesive framework, which enables the generation of high-quality 3D geometry from images. We validate the superiority of our approach over state-of-the-art methods through extensive evaluations and comparisons on diverse indoor and outdoor datasets, showcasing its efficiency and robustness. Code and data are available at https://github.com/xxlong0/ASNDepth.
Neuronal functional connectivity graph estimation with the R package neurofuncon

Researchers continue exploring neurons' intricate patterns of activity in the cerebral visual cortex in response to visual stimuli. The way neurons communicate and optimize their interactions with each other under different experimental conditions remains a topic of active investigation. Probabilistic Graphical Models are invaluable tools in neuroscience research, as they let us identify the functional connections, or conditional statistical dependencies, between neurons. Graphical models represent these connections as a graph, where nodes represent neurons and edges indicate the presence of functional connections between them. We developed the R package neurofuncon for the computation and visualization of functional connectivity graphs from large-scale data based on the Graphical lasso. We illustrate the use of this package with publicly available two-photon calcium microscopy imaging data from approximately 10000 neurons in a 1mm cubic section of a mouse visual cortex.
The Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding

In this paper, we investigate the loss landscape of one-hidden-layer neural networks with ReLU-like activation functions trained with the empirical squared loss. As the activation function is non-differentiable, it is so far unclear how to completely characterize the stationary points. We propose the conditions for stationarity that apply to both non-differentiable and differentiable cases. Additionally, we show that, if a stationary point does not contain"escape neurons", which are defined with first-order conditions, then it must be a local minimum. Moreover, for the scalar-output case, the presence of an escape neuron guarantees that the stationary point is not a local minimum. Our results refine the description of the saddle-to-saddle training process starting from infinitesimally small (vanishing) initialization for shallow ReLU-like networks, linking saddle escaping directly with the parameter changes of escape neurons. Moreover, we are also able to fully discuss how network embedding, which is to instantiate a narrower network within a wider network, reshapes the stationary points.
NCRF: Neural Contact Radiance Fields for Free-Viewpoint Rendering of Hand-Object Interaction

Modeling hand-object interactions is a fundamentally challenging task in 3D computer vision. Despite remarkable progress that has been achieved in this field, existing methods still fail to synthesize the hand-object interaction photo-realistically, suffering from degraded rendering quality caused by the heavy mutual occlusions between the hand and the object, and inaccurate hand-object pose estimation. To tackle these challenges, we present a novel free-viewpoint rendering framework, Neural Contact Radiance Field (NCRF), to reconstruct hand-object interactions from a sparse set of videos. In particular, the proposed NCRF framework consists of two key components: (a) A contact optimization field that predicts an accurate contact field from 3D query points for achieving desirable contact between the hand and the object. (b) A hand-object neural radiance field to learn an implicit hand-object representation in a static canonical space, in concert with the specifically designed hand-object motion field to produce observation-to-canonical correspondences. We jointly learn these key components where they mutually help and regularize each other with visual and geometric constraints, producing a high-quality hand-object reconstruction that achieves photo-realistic novel view synthesis. Extensive experiments on HO3D and DexYCB datasets show that our approach outperforms the current state-of-the-art in terms of both rendering quality and pose estimation accuracy.
A NEW SPECIES OF DIMETRODON (REPTILIA, PELYCOSAURIA) FROM A NON-DELTAIC FACIES IN THE LOWER PERMIAN OF NORTH-CENTRAL NEW MEXICO

A new species, Dimetrodon occidentalis, is based on a small, partial skeleton from the Lower Permian Abo Formation near Jemez Springs, north-central New Mexico; this is the first new species of Dimetrodon to be recognized outside of the Lower Permian beds of north-central Texas and Oklahoma. Differences in proportions of the centra of the dorsal vertebrae and a marked narrowing of the neural spines at the anterior end of the vertebral column distinguish this species from those of comparable sizes from Texas and Oklahoma. Recently reported vertebral fragments of Dimetrodon from deposits of similar age in Utah, Arizona and New Mexico probably pertain to the species described here. The small size of D. occidentalis, particularly its closeness in overall size to the primitive D. milleri from the Putnam Formation of the Wichita Group of Texas, is in accord with previous estimates of a Wolfcampian age for the lower fossiliferous levels of the Abo Formation of northern New Mexico. The presence of Dimetrodon in north-central New Mexico, far removed from deltaic deposits near the borders of persistent seaways where it is normally found, may indicate that it was distributed over a wider range of environments than has recently been suggested.
Measurement of the Branching Fraction of $B^{0} \rightarrow J/\psi \pi^{0}$ Decays

The ratio of branching fractions between $B^{0} \rightarrow J/\psi \pi^{0}$ and $B^{+} \rightarrow J/\psi K^{*+}$ decays is measured with proton-proton collision data collected by the LHCb experiment, corresponding to an integrated luminosity of 9 fb$^{-1}$. The measured value is $\frac{\mathcal{B}_{B^{0} \rightarrow J/\psi \pi^{0}}}{\mathcal{B}_{B^{+} \rightarrow J/\psi K^{*+}}} = (1.153 \pm 0.053 \pm 0.048 ) \times 10^{-2}$, where the first uncertainty is statistical and the second is systematic. The branching fraction for $B^{0} \rightarrow J/\psi \pi^{0}$ decays is determined using the branching fraction of the normalisation channel, resulting in $\mathcal{B}_{B^{0} \rightarrow J/\psi \pi^{0}} = (1.670 \pm 0.077 \pm 0.069 \pm 0.095) \times 10^{-5}$, where the last uncertainty corresponds to that of the external input. This result is consistent with the current world average value and competitive with the most precise single measurement to date.
Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma

Resistance of BRAF-mutant melanomas to targeted therapy arises from the ability of cells to enter a persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. Using spatial transcriptomics in patient derived xenograft models, we capture clonal lineage evolution during treatment, finding the persister state to show increased oxidative phosphorylation, decreased proliferation, and increased invasive capacity, with central-to-peripheral gradients. Phylogenetic tracing identifies intrinsic- and acquired-resistance mechanisms (e.g. dual specific phosphatases, Reticulon-4, CDK2) and suggests specific temporal windows of potential therapeutic efficacy. Using deep learning to analyze histopathological slides, we find morphological features of specific cell states, demonstrating that juxtaposition of transcriptomics and histology data enables identification of phenotypically-distinct populations using imaging data alone. In summary, we define state change and lineage selection during melanoma treatment with spatiotemporal resolution, elucidating how choice and timing of therapeutic agents will impact the ability to eradicate resistant clones. Statement of Significance Tumor evolution is accelerated by application of anti-cancer therapy, resulting in clonal expansions leading to dormancy and subsequently resistance, but the dynamics of this process are incompletely understood. Tracking clonal progression during treatment, we identify conserved, global transcriptional changes and local clone-clone and spatial patterns underlying the emergence of resistance.
The dimensionality of neural coding for cognitive control is gradually transformed within the lateral prefrontal cortex

Implementing cognitive control relies on neural representations that are inherently high-dimensional and distribute across multiple subregions in the prefrontal cortex (PFC). Traditional approaches tackle prefrontal representations by reducing them into a unidimensional measure (univariate amplitude) or using them to distinguish a limited number of alternatives (pattern classification). By contrast, representational similarity analysis (RSA) enables flexibly formulating various hypotheses about informational contents underlying the neural codes, explicitly comparing hypotheses, and examining the representational alignment between brain regions. Here, we used a multifaceted paradigm wherein the difficulty of cognitive control was manipulated separately for five cognitive tasks. We used RSA to unveil representational contents, measure the representational alignment between regions, and quantify representational generality vs. specificity. We found a graded transition in the lateral PFC: The dorsocaudal PFC was tuned to the information about behavioural effort, preferentially connected with the parietal cortex, and representationally generalisable across domains. The ventrorostral PFC was tuned to the abstract structure of tasks, preferentially connected with the temporal cortex, and representationally specific. The middle PFC (interposed between dorsocaudal and ventrorostral PFC) was tuned to individual task-sets, ranked in the middle in terms of connectivity and generalisability. Furthermore, whether a region was dimensionally rich or thin co-varied with its functional profile: Low dimensionality (only gist) in the dorsocaudal PFC dovetailed with better generality, whereas high dimensionality (gist plus details) in the ventrorostral PFC corresponded with better ability to encode subtleties. Our findings, collectively, demonstrate how cognitive control is decomposed into distinct facets that transition steadily along prefrontal subregions. Significance Cognitive control is known to be a high-dimensional construct, implemented along the dorsocaudal-ventrorostral subregions of PFC. However, it remains unclear how prefrontal representations could be dissected in a multivariate fashion to reveal (1) what information is encoded in each subregion, (2) whether information systematically transforms across contiguous PFC subregions as a gradient, (3) how this transformation is affected by functional connectivity. Here we shed light on these issues by using RSA to decode informational composition in the PFC while using participant-specific localisers to facilitate individually-tailored precision. Our findings elucidate the functional organisation of PFC by revealing how a trade-off between dimensionality and generalisability unfolds in the PFC and highlight the strength of RSA in deciphering the coding of cognitive control.