Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Implicit Neural Representation Image Codec in Resource-limited Devices

Displaying high-quality images on edge devices, such as augmented reality devices, is essential for enhancing the user experience. However, these devices often face power consumption and computing resource limitations, making it challenging to apply many deep learning-based image compression algorithms in this field. Implicit Neural Representation (INR) for image compression is an emerging technology that offers two key benefits compared to cutting-edge autoencoder models: low computational complexity and parameter-free decoding. It also outperforms many traditional and early neural compression methods in terms of quality. In this study, we introduce a new Mixed Autoregressive Model (MARM) to significantly reduce the decoding time for the current INR codec, along with a new synthesis network to enhance reconstruction quality. MARM includes our proposed Autoregressive Upsampler (ARU) blocks, which are highly computationally efficient, and ARM from previous work to balance decoding time and reconstruction quality. We also propose enhancing ARU's performance using a checkerboard two-stage decoding strategy. Moreover, the ratio of different modules can be adjusted to maintain a balance between quality and speed. Comprehensive experiments demonstrate that our method significantly improves computational efficiency while preserving image quality. With different parameter settings, our method can outperform popular AE-based codecs in constrained environments in terms of both quality and decoding time, or achieve state-of-the-art reconstruction quality compared to other INR codecs.
How do ionic superdiscs self-assemble in nanopores?

Discotic ionic liquid crystals (DILCs) consist of self-assembled superdiscs of cations and anions that spontaneously stack in linear columns with high one-dimensional ionic and electronic charge mobility, making them prominent model systems for functional soft matter. Unfortunately, a homogeneous alignment of DILCs on the macroscale is often not achievable, which significantly limits their applicability. Infiltration into nanoporous solid scaffolds can in principle overcome this drawback. However, due to the extreme experimental challenges to scrutinise liquid crystalline order in extreme spatial confinement, little is known about the structures of DILCs in nanopores. Here, we present temperature-dependent high-resolution optical birefringence measurement and 3D reciprocal space mapping based on synchrotron-based X-ray scattering to investigate the thermotropic phase behaviour of dopamine-based ionic liquid crystals confined in cylindrical channels of 180~nm diameter in macroscopic anodic aluminum oxide (AAO) membranes. As a function of the membranes' hydrophilicity and thus the molecular anchoring to the pore walls (edge-on or face-on) and the variation of the hydrophilic-hydrophobic balance between the aromatic cores and the alkyl side chain motifs of the superdiscs by tailored chemical synthesis, we find a particularly rich phase behaviour, which is not present in the bulk state. It is governed by a complex interplay of liquid crystalline elastic energies (bending and splay deformations), polar interactions and pure geometric confinement, and includes textural transitions between radial and axial alignment of the columns with respect to the long nanochannel axis.
ToDA: Target-oriented Diffusion Attacker against Recommendation System

Recommendation systems (RS) have become indispensable tools for web services to address information overload, thus enhancing user experiences and bolstering platforms' revenues. However, with their increasing ubiquity, security concerns have also emerged. As the public accessibility of RS, they are susceptible to specific malicious attacks where adversaries can manipulate user profiles, leading to biased recommendations. Recent research often integrates additional modules using generative models to craft these deceptive user profiles, ensuring them are imperceptible while causing the intended harm. Albeit their efficacy, these models face challenges of unstable training and the exploration-exploitation dilemma, which can lead to suboptimal results. In this paper, we pioneer to investigate the potential of diffusion models (DMs), for shilling attacks. Specifically, we propose a novel Target-oriented Diffusion Attack model (ToDA). It incorporates a pre-trained autoencoder that transforms user profiles into a high dimensional space, paired with a Latent Diffusion Attacker (LDA)-the core component of ToDA. LDA introduces noise into the profiles within this latent space, adeptly steering the approximation towards targeted items through cross-attention mechanisms. The global horizon, implemented by a bipartite graph, is involved in LDA and derived from the encoded user profile feature. This makes LDA possible to extend the generation outwards the on-processing user feature itself, and bridges the gap between diffused user features and target item features. Extensive experiments compared to several SOTA baselines demonstrate ToDA's effectiveness. Specific studies exploit the elaborative design of ToDA and underscore the potency of advanced generative models in such contexts.
DsDm: Model-Aware Dataset Selection with Datamodels

When selecting data for training large-scale models, standard practice is to filter for examples that match human notions of data quality. Such filtering yields qualitatively clean datapoints that intuitively should improve model behavior. However, in practice the opposite can often happen: we find that selecting according to similarity with"high quality"data sources may not increase (and can even hurt) performance compared to randomly selecting data. To develop better methods for selecting data, we start by framing dataset selection as an optimization problem that we can directly solve for: given target tasks, a learning algorithm, and candidate data, select the subset that maximizes model performance. This framework thus avoids handpicked notions of data quality, and instead models explicitly how the learning process uses train datapoints to predict on the target tasks. Our resulting method greatly improves language model (LM) performance on both pre-specified tasks and previously unseen tasks. Specifically, choosing target tasks representative of standard LM problems and evaluating on diverse held-out benchmarks, our selected datasets provide a 2x compute multiplier over baseline methods.
Nearly critical superfluid: effective field theory and holography

We study a nearly critical superfluid system from two complementary approaches. Within the first approach, we formulate a Schwinger-Keldysh effective field theory (EFT) for the system when it is located slightly above the critical temperature. The dynamical variables in the EFT construction are two scalars: a neutral scalar associated with the conserved U(1) charge, and a complex scalar describing the order parameter. The set of symmetries, particularly the dynamical Kubo-Martin-Schwinger (KMS) symmetry and chemical shift symmetry, strictly constrains the form of EFT action. Within the second approach, using the holographic Schwinger-Keldysh technique, we derive the effective action for a ``microscopic'' holographic superfluid, confirming the EFT construction. A systematic inclusion of non-Gaussianity is one highlight of present study.
Efficient near-optimal decoding of the surface code through ensembling

We introduce harmonization, an ensembling method that combines several"noisy"decoders to generate highly accurate decoding predictions. Harmonized ensembles of MWPM-based decoders achieve lower logical error rates than their individual counterparts on repetition and surface code benchmarks, approaching maximum-likelihood accuracy at large ensemble sizes. We can use the degree of consensus among the ensemble as a confidence measure for a layered decoding scheme, in which a small ensemble flags high-risk cases to be checked by a larger, more accurate ensemble. This layered scheme can realize the accuracy improvements of large ensembles with a relatively small constant factor of computational overhead. We conclude that harmonization provides a viable path towards highly accurate real-time decoding.
Diagnosing $SO(5)$ Symmetry and First-Order Transition in the $J-Q_3$ Model via Entanglement Entropy

We study the scaling behavior of the R\'enyi entanglement entropy with smooth boundaries at the phase transition point of the two-dimensional $J-Q_3$ model. Using the recently developed scaling formula [Deng {\it et al.}, Phys. Rev. B {\textbf{108}, 125144 (2023)}], we find a subleading logarithmic term with a coefficient showing that the number of Goldstone modes is four, indicating the existence of the spontaneous symmetry breaking from an emergent $SO(5)$ to $O(4)$ in the thermodynamic limit, but restored in a finite size. This result shows that the believed deconfined quantum critical point of the $J-Q_{3}$ model is a weak first-order transition point. Our work provides a new way to distinguish a state with spontaneously broken continuous symmetry from a critical state. The method is particularly useful in identifying weak first-order phase transitions, which are hard to determine using conventional methods.
Pretraining and the Lasso

Pretraining is a popular and powerful paradigm in machine learning. As an example, suppose one has a modest-sized dataset of images of cats and dogs, and plans to fit a deep neural network to classify them from the pixel features. With pretraining, we start with a neural network trained on a large corpus of images, consisting of not just cats and dogs but hundreds of other image types. Then we fix all of the network weights except for the top layer (which makes the final classification) and train (or"fine tune") those weights on our dataset. This often results in dramatically better performance than the network trained solely on our smaller dataset. In this paper, we ask the question"Can pretraining help the lasso? ". We develop a framework for the lasso in which an overall model is fit to a large set of data, and then fine-tuned to a specific task on a smaller dataset. This latter dataset can be a subset of the original dataset, but does not need to be. We find that this framework has a wide variety of applications, including stratified models, multinomial targets, multi-response models, conditional average treatment estimation and even gradient boosting. In the stratified model setting, the pretrained lasso pipeline estimates the coefficients common to all groups at the first stage, and then group specific coefficients at the second"fine-tuning"stage. We show that under appropriate assumptions, the support recovery rate of the common coefficients is superior to that of the usual lasso trained only on individual groups. This separate identification of common and individual coefficients can also be useful for scientific understanding.
TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks

Language models (LMs) can solve tasks such as answering questions about tables or images by writing programs. However, using primitive functions often leads to verbose and error-prone programs, and higher-level functions require expert design. To enable better solutions without human labor, we ask code LMs to curate reusable high-level functions, and use them to write solutions. We present TROVE, a training-free method of inducing a verifiable and efficient toolbox of functions, by generating via using, growing, and periodically trimming the toolbox. On 11 datasets from math, table question answering, and image reasoning tasks, TROVE consistently yields simpler solutions with higher accuracy than baselines using CODELLAMA and previous methods using GPT, while using 79-98% smaller toolboxes. TROVE further enables 31% faster and 13% more accurate human verification than baselines. With the same pipeline, it creates diverse functions for varied tasks and datasets, providing insights into their individual characteristics.
Understanding Emojis : ) in Useful Code Review Comments

Emojis and emoticons serve as non-verbal cues and are increasingly prevalent across various platforms, including Modern Code Review. These cues often carry emotive or instructive weight for developers. Our study dives into the utility of Code Review comments (CR comments) by scrutinizing the sentiments and semantics conveyed by emojis within these comments. To assess the usefulness of CR comments, we augment traditional 'textual' features and pre-trained embeddings with 'emoji-specific' features and pre-trained embeddings. To fortify our inquiry, we expand an existing dataset with emoji annotations, guided by existing research on GitHub emoji usage, and re-evaluate the CR comments accordingly. Our models, which incorporate textual and emoji-based sentiment features and semantic understandings of emojis, substantially outperform baseline metrics. The often-overlooked emoji elements in CR comments emerge as key indicators of usefulness, suggesting that these symbols carry significant weight.
TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data

The robust generalization of deep learning models in the presence of inherent noise remains a significant challenge, especially when labels are subjective and noise is indiscernible in natural settings. This problem is particularly pronounced in many practical applications. In this paper, we address a special and important scenario of monitoring suicidal ideation, where time-series data, such as photoplethysmography (PPG), is susceptible to such noise. Current methods predominantly focus on image and text data or address artificially introduced noise, neglecting the complexities of natural noise in time-series analysis. To tackle this, we introduce a novel neural network model tailored for analyzing noisy physiological time-series data, named TNANet, which merges advanced encoding techniques with confidence learning, enhancing prediction accuracy. Another contribution of our work is the collection of a specialized dataset of PPG signals derived from real-world environments for suicidal ideation prediction. Employing this dataset, our TNANet achieves the prediction accuracy of 63.33% in a binary classification task, outperforming state-of-the-art models. Furthermore, comprehensive evaluations were conducted on three other well-known public datasets with artificially introduced noise to rigorously test the TNANet's capabilities. These tests consistently demonstrated TNANet's superior performance by achieving an accuracy improvement of more than 10% compared to baseline methods.
Viability and control of a delayed SIR epidemic with an ICU state constraint

This paper studies viability and control synthesis for a delayed SIR epidemic. The model integrates a constant delay representing an incubation/latency time. The control inputs model non-pharmaceutical interventions, while an intensive care unit (ICU) state-constraint is introduced to reflect the healthcare system's capacity. The arising delayed control system is analyzed via functional viability tools, providing insights into fulfilling the ICU constraint through feedback control maps. In particular, we consider two scenarios: first, we consider the case of general continuous initial conditions. Then, as a further refinement of our analysis, we assume that the initial conditions satisfy a Lipschitz continuity property, consistent with the considered model. The study compares the (in general, sub-optimal) obtained control policies with the optimal ones for the delay-free case, emphasizing the impact of the delay parameter. The obtained results are supported and illustrated, in a concluding section, by numerical examples.
Computing Diameter+2 in Truly Subquadratic Time for Unit-Disk Graphs

Finding the diameter of a graph in general cannot be done in truly subquadratic assuming the Strong Exponential Time Hypothesis (SETH), even when the underlying graph is unweighted and sparse. When restricting to concrete classes of graphs and assuming SETH, planar graphs and minor-free graphs admit truly subquadratic algorithms, while geometric intersection graphs of unit balls, congruent equilateral triangles, and unit segments do not. Unit-disk graphs are one of the major open cases where the complexity of diameter computation remains unknown. More generally, it is conjectured that a truly subquadratic time algorithm exists for pseudo-disk graphs. In this paper, we show a truly subquadratic algorithm of running time $\tilde{O}(n^{2-1/18})$, for finding the diameter in a unit-disk graph, whose output differs from the optimal solution by at most 2. This is the first algorithm that provides an additive guarantee in distortion, independent of the size or the diameter of the graph. Our algorithm requires two important technical elements. First, we show that for the intersection graph of pseudo-disks, the graph VC-dimension, either of $k$-hop balls or the distance encoding vectors, is 4. This contracts to the VC dimension of the pseudo-disks themselves as geometric ranges (which is known to be 3). Second, we introduce a clique-based $r$-clustering for geometric intersection graphs, which is an analog of the $r$-division construction for planar graphs. We also showcase the new techniques by establishing new results for distance oracles for unit-disk graphs with subquadratic storage and $O(1)$ query time. The results naturally extend to unit $L_1$ or $L_\infty$-disks and fat pseudo-disks of similar size. Last, if the pseudo-disks additionally have bounded ply, we have a truly subquadratic algorithm to find the exact diameter.
New spectral-parameter dependent solutions of the Yang-Baxter equation

The Yang-Baxter Equation (YBE) plays a crucial role for studying integrable many-body quantum systems. Many known YBE solutions provide various examples ranging from quantum spin chains to superconducting systems. Models of solvable statistical mechanics and their avatars are also based on YBE. Therefore, new solutions of the YBE could be used to construct new interesting 1D quantum or 2D classical systems with many other far-reaching applications. In this work, we attempt to find (almost) exhaustive set of solutions for the YBE in the lowest dimensions corresponding to a two-qubit case. We develop an algorithm, which can potentially be used for generating new higher-dimensional solutions of the YBE.
LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools

Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users' understanding, as one-off explanations may occasionally fall short in providing sufficient information to the user. Current solutions for dialogue-based explanations, however, require many dependencies and are not easily transferable to tasks they were not designed for. With LLMCheckup, we present an easily accessible tool that allows users to chat with any state-of-the-art large language model (LLM) about its behavior. We enable LLMs to generate all explanations by themselves and take care of intent recognition without fine-tuning, by connecting them with a broad spectrum of Explainable AI (XAI) tools, e.g. feature attributions, embedding-based similarity, and prompting strategies for counterfactual and rationale generation. LLM (self-)explanations are presented as an interactive dialogue that supports follow-up questions and generates suggestions. LLMCheckup provides tutorials for operations available in the system, catering to individuals with varying levels of expertise in XAI and supports multiple input modalities. We introduce a new parsing strategy called multi-prompt parsing substantially enhancing the parsing accuracy of LLMs. Finally, we showcase the tasks of fact checking and commonsense question answering.
Exploring the Gas-phase Metallicity Gradients of Star-forming Galaxies at Cosmic Noon

We explore the relationships between the [O/H] gas-phase metallicity radial gradients and multiple galaxy properties for 238 star-forming galaxies at 0.6 < z < 2.6 selected from the CANDELS Lyα Emission at Reionization survey with stellar mass 8.5 < log M * / M ⊙ < 10.5 . The gradients cover the range from −0.11 to 0.22 dex kpc−1, with the median value close to 0. We reconstruct the nonparametric star formation histories (SFHs) of the galaxies with spectral energy distribution modeling using Prospector with more than 40 photometric bands from the Hubble Space Telescope, Spitzer, and ground-based facilities. In general, we find weak or no correlations between the metallicity gradients and most galaxy properties, including the mass-weighted age, recent star formation rate, dust attenuation, and morphology as quantified by both parametric and nonparametric diagnostics. We find a significant but moderate correlation between the gradients and the “evolutionary time,” a temporal metric that characterizes the evolutionary status of a galaxy, with flatter gradients observed in more evolved galaxies. Also, there is evidence that galaxies with multiple star formation episodes in their SFHs tend to develop more negative gas-phase metallicity gradients (higher [O/H] at the center). We conclude that gas kinematics, e.g., radial inflows and outflows, is likely an important process in setting the gas-phase metallicity gradients, in addition to the evolution of the SFH radial profile. Since the gradients are largely independent of the galaxies’ physical properties and only weakly dependent on their SFH, it would appear that the timescale of the gas kinematics is significantly shorter than the evolution of star formation.
A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments

We respond to the recent paper by Makelov et al. (2023), which reviews subspace interchange intervention methods like distributed alignment search (DAS; Geiger et al. 2023) and claims that these methods potentially cause"interpretability illusions". We first review Makelov et al. (2023)'s technical notion of what an"interpretability illusion"is, and then we show that even intuitive and desirable explanations can qualify as illusions in this sense. As a result, their method of discovering"illusions"can reject explanations they consider"non-illusory". We then argue that the illusions Makelov et al. (2023) see in practice are artifacts of their training and evaluation paradigms. We close by emphasizing that, though we disagree with their core characterization, Makelov et al. (2023)'s examples and discussion have undoubtedly pushed the field of interpretability forward.
Acoustic pressure modulation driven by spatially non-uniform flow.

The recent identification of a modulation of acoustic waves that is driven by spatial velocity gradients, using acoustic black and white hole analogues [see Schenke, Sewerin, van Wachem, and Denner, J. Acoust. Soc. Am. 154, 781-791 (2023)], has shed new light on the complex interplay of acoustic waves and non-uniform flows. According to the virtual acoustic black hole hypothesis, these findings should be applicable to acoustic waves propagating in non-uniform flows of arbitrary velocity. In this study, the propagation of acoustic waves in non-uniform flows is investigated by incorporating a leading-order model of acoustic pressure modulation into a Lagrangian wave tracking algorithm. Using this numerical method, the acoustic pressure modulation is recovered accurately in non-uniform subsonic flows. This suggests that spatial velocity gradients drive acoustic pressure modulations in any non-uniform flow, which can, as shown here, be readily quantified.