Miracle-dz/UniFAR-FTUs huggingface dataset:
BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction

Language models pre-trained on scientific literature corpora have substantially advanced scientific discovery by offering high-quality feature representations for downstream applications. However, these features are often not interpretable, and thus can reveal limited insights to domain experts. Instead of obtaining features from language models, we propose BLIAM, a literature-based data synthesis approach to directly generate training data points that are interpretable and model-agnostic to downstream applications. The key idea of BLIAM is to create prompts using existing training data and then use these prompts to synthesize new data points. BLIAM performs these two steps iteratively as new data points will define more informative prompts and new prompts will in turn synthesize more accurate data points. Notably, literature-based data augmentation might introduce data leakage since labels of test data points in downstream applications might have already been mentioned in the language model corpus. To prevent such leakage, we introduce GDSC-combo, a large-scale drug combination discovery dataset that was published after the biomedical language model was trained. We found that BLIAM substantially outperforms a non-augmented approach and manual prompting in this rigorous data split setting. BLIAM can be further used to synthesize data points for novel drugs and cell lines that were not even measured in biomedical experiments. In addition to the promising prediction performance, the data points synthesized by BLIAM are interpretable and model-agnostic, enabling in silico augmentation for in vitro experiments.
Quark Clusters, QCD Vacuum and the Cosmological 7Li, Dark Matter and Dark Energy Problems

We propose a non-exotic electromagnetic solution (within the standard model of particle physics) to the cosmological 7Li problem based upon a narrow 2 MeV photo-emission line from the decay of light glueballs (LGBs). These LGBs form within color superconducting quark clusters (SQCs), which are tens of Fermi in size, in the radiation-dominated post-BBN epoch. The mono-chromatic line from the LGB→γ+γ decay reduces Big Bang nucleosynthesis (BBN) 7Be by 2/3 without affecting other abundances or the cosmic microwave background (CMB) physics, provided the combined mass of the SQCs is greater than the total baryonic mass in the universe. Following the LGB emission, the in-SQC Quantum ChromoDynamics (QCD) vacuum becomes unstable and “leaks” (via quantum tunneling) into the external space-time (trivial) vacuum, inducing a decoupling of SQCs from hadrons. In seeking a solution to the 7Li problem, we uncovered a solution that also addresses the Dark Energy (DE) and dark matter (DM) problem, making these critical problems intertwined in our model. Being colorless, charge-neutral, optically thin, and transparent to hadrons, SQCs interact only gravitationally, making them a viable cold DM (CDM) candidate. The leakage (i.e., quantum tunneling) of the in-SQC QCD vacuum to the trivial vacuum offers an explanation of DE in our model and allows for a cosmology that evolves into a ΛCDM universe at a low redshift with a possible resolution of the Hubble tension. Our model distinguishes itself by proposing that the QCD vacuum within SQCs possesses the ability to tunnel into the exterior trivial vacuum, resulting in the generation of DE. This implies the possibility that DM and hadrons might represent distinct phases of quark matter within the framework of QCD, characterized by different vacuum properties. We discuss SQC formation in heavy-ion collision experiments at moderate temperatures and the possibility of detection of MeV photons from the LGB→γ+γ decay.
Concentration Bounds for Discrete Distribution Estimation in KL Divergence

We study the problem of discrete distribution estimation in KL divergence and provide concentration bounds for the Laplace estimator. We show that the deviation from mean scales as $\sqrt k /n$ when n≥k, improving upon the best prior result of k/n. We also establish a matching lower bound that shows that our bounds are tight up to polylogarithmic factors.
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs

The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional LSTM with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstarte our findings.
Raman spectroscopy of bimagnon and trimagnon excitations and rotonlike points in a distorted triangular lattice antiferromagnet

We investigate the experimental signatures of Raman spectroscopy of bi- and tri-magnon excitations in the distorted triangular lattice antiferromagnets alpha-LCr2O4 (L=Sr, Ca). We utilize spin wave theory to analyze the nearly 120 degree spin-3/2 spiral ordered antiferromagnetic ground state to compute the single-magnon density of states, single-magnon dispersion, and bimagnon and trimagnon Raman spectra (polarized and unpolarized). It is found that Raman scattering is capable of capturing the effect of the rotonlike M and M' points on the bimagnon Raman spectrum. Our calculation confirms the connection between single-magnon rotonlike excitation energy and bimagnon Raman excitation spectrum observed experimentally. The roton energy minimum in momentum space is half of the energy of a bimagnon excitation signal. The experimental magnetic Raman scattering result displays two peaks which have a Raman shift of 15 meV and 40 meV, respectively. Theoretical modeling and analysis of the experimental spectrum of alpha-SrCr2O4 within our distorted Heisenberg Hamiltonian lattice suggests that the low-energy peak at 15 meV is associated with the bimagnon excitation, whereas the high-energy peak around 40 meV is primarily a trimagnon excitation. Based on our fitting procedure we propose a new set of magnetic interaction parameters for alpha-SrCr2O4. These parameters reproduce not only the experimental Raman spectrum, but also the inelastic neutron scattering response (including capturing high energy magnon branches). We also compute the unpolarized bimagnon and trimagnon Raman spectra for alpha-CaCr2O4. Furhtermore, we found that the polarization sensitivity of Raman spectrum can be utilized to distinguish the bi- and tri-magnon excitation channels.
Error Correction, Attitude Persistence, and Executive Rewards and Punishments: A Behavioral Theory of Presidential Approval

Beginning with Mueller's (1970) seminal work, researchers have wrestled with explanations of the movement of presidential approval over time. In his initial argument, Mueller states that in tandem, the concepts underlying the coalition of minorities and rally round the flag variables predict that the president's popularity will continually decline over time and that international crises and similar events will explain short-term bumps and wiggles in this otherwise inexorable descent. (1970, 22) From this basis, Mueller posits “… a general downward trend in each president's popularity” (1970, 19) that is linear and deterministic over the course of a term. Others later moved away from arguments of linearity (e.g., Stimson 1976) and from the coalition of minorities concept (e.g., Kemell 1978), but these early characterizations of approval's time path, perpetuated in the “myth of the inexorable descent,” remain to this day.
An Origin of Narrow Extended Structure in the Interstellar Medium: An Interstellar Contrail Created by a Fast-moving Massive Object

We investigate the thermal condensation caused by a massive object that passes through the interstellar medium with high velocity, and propose a mechanism for creating a filamentary gaseous object, or interstellar contrail. Our main result shows that a long interstellar contrail can form with a certain parameter; a compact object more massive than 104 M ☉ can make a filament whose length is larger than 100 pc. Observation of interstellar contrails may provide information on the number, masses, and velocities of fast-moving massive objects, and can be a new method for probing invisible gravitating sources such as intermediate-mass black holes.
WSD: Wild Selfie Dataset for Face Recognition in Selfie Images

With the rise of handy smart phones in the recent years, the trend of capturing selfie images is observed. Hence efficient approaches are required to be developed for recognising faces in selfie images. Due to the short distance between the camera and face in selfie images, and the different visual effects offered by the selfie apps, face recognition becomes more challenging with existing approaches. A dataset is needed to be developed to encourage the study to recognize faces in selfie images. In order to alleviate this problem and to facilitate the research on selfie face images, we develop a challenging Wild Selfie Dataset (WSD) where the images are captured from the selfie cameras of different smart phones, unlike existing datasets where most of the images are captured in controlled environment. The WSD dataset contains 45,424 images from 42 individuals (i.e., 24 female and 18 male subjects), which are divided into 40,862 training and 4,562 test images. The average number of images per subject is 1,082 with minimum and maximum number of images for any subject are 518 and 2,634, respectively. The proposed dataset consists of several challenges, including but not limited to augmented reality filtering, mirrored images, occlusion, illumination, scale, expressions, view-point, aspect ratio, blur, partial faces, rotation, and alignment. We compare the proposed dataset with existing benchmark datasets in terms of different characteristics. The complexity of WSD dataset is also observed experimentally, where the performance of the existing state-of-the-art face recognition methods is poor on WSD dataset, compared to the existing datasets. Hence, the proposed WSD dataset opens up new challenges in the area of face recognition and can be beneficial to the community to study the specific challenges related to selfie images and develop improved methods for face recognition in selfie images.
Atomic physics on a 50 nm scale: Realization of a bilayer system of dipolar atoms

Atomic physics has greatly advanced quantum science, mainly due to the ability to control the position and internal quantum state of atoms with high precision, often at the quantum limit. The dominant tool for this is laser light, which can structure and localize atoms in space (e.g., in optical tweezers, optical lattices, 1D tubes or 2D planes). Due to the diffraction limit of light, the natural length scale for most experiments with atoms is on the order of 500 nm or larger. Here we implement a new super-resolution technique which localizes and arranges atoms on a sub-50 nm scale, without any fundamental limit in resolution. We demonstrate this technique by creating a bilayer of dysprosium atoms, mapping out the atomic density distribution with sub-10 nm resolution, and observing dipolar interactions between two physically separated layers via interlayer sympathetic cooling and coupled collective excitations. At 50 nm, dipolar interactions are 1,000 times stronger than at 500 nm. For two atoms in optical tweezers, this should enable purely magnetic dipolar gates with kHz speed.
Self-Supervised and Invariant Representations for Wireless Localization

In this work, we present a wireless localization method that operates on self-supervised and unlabeled channel estimates. Our self-supervising method learns general-purpose channel features robust to fading and system impairments. Learned representations are easily transferable to new environments and ready to use for other wireless downstream tasks. To the best of our knowledge, the proposed method is the first joint-embedding self-supervised approach to forsake the dependency on contrastive channel estimates. Our approach outperforms fully-supervised techniques in small data regimes under fine-tuning and, in some cases, linear evaluation. We assess the performance in centralized and distributed massive MIMO systems for multiple datasets. Moreover, our method works indoors and outdoors without additional assumptions or design changes.
Online Detection of Changes in Moment-Based Projections: When to Retrain Deep Learners or Update Portfolios?

Sequential monitoring of high-dimensional nonlinear time series is studied for a projection of the second-moment matrix, a problem interesting in its own right and specifically arising in finance and deep learning. Open-end as well as closed-end monitoring is studied under mild assumptions on the training sample and the observations of the monitoring period. Asymptotics is based on Gaussian approximations of projected partial sums allowing for an estimated projection vector. Estimation is studied both for classical non-$\ell_0$-sparsity as well as under sparsity. For the case that the optimal projection depends on the unknown covariance matrix, hard- and soft-thresholded estimators are studied. Applications in finance and training of deep neural networks are discussed. The proposed detectors typically allow to reduce dramatically the required computational costs as illustrated by monitoring synthetic data.
A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization

This paper introduces a new proximal stochastic gradient method with variance reduction and stabilization for minimizing the sum of a convex stochastic function and a group sparsity-inducing regularization function. Since the method may be viewed as a stabilized version of the recently proposed algorithm PStorm, we call our algorithm S-PStorm. Our analysis shows that S-PStorm has strong convergence results. In particular, we prove an upper bound on the number of iterations required by S-PStorm before its iterates correctly identify (with high probability) an optimal support (i.e., the zero and nonzero structure of an optimal solution). Most algorithms in the literature with such a support identification property use variance reduction techniques that require either periodically evaluating an exact gradient or storing a history of stochastic gradients. Unlike these methods, S-PStorm achieves variance reduction without requiring either of these, which is advantageous. Moreover, our support-identification result for S-PStorm shows that, with high probability, an optimal support will be identified correctly in all iterations with the index above a threshold. We believe that this type of result is new to the literature since the few existing other results prove that the optimal support is identified with high probability at each iteration with a sufficiently large index (meaning that the optimal support might be identified in some iterations, but not in others). Numerical experiments on regularized logistic loss problems show that S-PStorm outperforms existing methods in various metrics that measure how efficiently and robustly iterates of an algorithm identify an optimal support.
Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention

Prompt and accurate detection of system anomalies is essential to ensure the reliability of software systems. Unlike manual efforts that exploit all available run-time information, existing approaches usually leverage only a single type of monitoring data (often logs or metrics) or fail to make effective use of the joint information among different types of data. Consequently, many false predictions occur. To better understand the manifestations of system anomalies, we conduct a systematical study on a large amount of heterogeneous data, i.e., logs and metrics. Our study demonstrates that logs and metrics can manifest system anomalies collaboratively and complementarily, and neither of them only is sufficient. Thus, integrating heterogeneous data can help recover the complete picture of a system's health status. In this context, we propose Hades, the first end-to-end semi-supervised approach to effectively identify system anomalies based on heterogeneous data. Our approach employs a hierarchical architecture to learn a global representation of the system status by fusing log semantics and metric patterns. It captures discriminative features and meaningful interactions from heterogeneous data via a cross-modal attention module, trained in a semi-supervised manner. We evaluate Hades extensively on large-scale simulated data and datasets from Huawei Cloud. The experimental results present the effectiveness of our model in detecting system anomalies. We also release the code and the annotated dataset for replication and future research.
Progress Toward Superconductor Electronics Fabrication Process With Planarized NbN and NbN/Nb Layers

In order to increase circuit density of superconductor digital and neuromorphic circuits by 10× and reach integration scale of 108 Josephson junctions (JJs) per chip, we developed a new fabrication process on 200-mm wafers, using self-shunted Nb/Al-AlOx/Nb JJs and kinetic inductors for cell miniaturization. The process has one layer of JJs, one layer of resistors, and ten fully planarized superconducting layers: 8 niobium layers and two layers of high kinetic inductance materials, Mo2N and NbN, with sheet inductance of 8 pH/sq and 3 pH/sq, respectively. The minimum linewidth of NbN kinetic inductors is 250 nm. NbN films were deposited by two methods: with ${T}_c \approx $15.5 K by reactive sputtering of a Nb target in Ar+N2 mixture; with ${T}_c$ in the range from 9 K to 13 K by plasma-enhanced chemical vapor deposition (PECVD) using Tris(diethylamido)(tert-butylimido)niobium(V) metalorganic precursor. PECVD of NbN was investigated to obtain conformal deposition and filling narrow trenches and vias with high depth-to-width ratios, d/w>1, which was not possible to achieve using sputtering and other physical vapor deposition methods at temperatures below 200 °C required to prevent degradation of Nb/Al-AlOx/Nb junctions. Nb layers with 200 nm thickness are used in the process layer stack as ground planes to maintain a high level of interlayer shielding and low intralayer mutual coupling, for passive transmission lines with wave impedances matching impedances of JJs, typically ≤50 Ω, and for low-value inductors. NbN and NbN/Nb bilayer are used for cell inductors. Using NbN/Nb bilayers and individual pattering of both layers to form inductors allowed us to increase critical currents of the interlayer vias and minimize parasitic kinetic inductance associated with the vias and connections to JJs. Fabrication details and results of electrical characterization of NbN films, wires, and vias, and comparison with Nb properties are given.
Induced Non-Local Cosmology

We investigate the cosmological implications of an effective gravitational action, inspired by Sakharov's idea of induced gravity, containing non-local contributions from the operator $\left(\Box +\beta \right)^{-1} R$. The $\beta$ term is a novel feature in the panorama of non-local models of gravity, and arises naturally within Sakharov theory from the potential of a non-minimally coupled scalar field after a spontaneous symmetry breaking takes place. In this class of models the non-local contribution can acquire an oscillatory behaviour, thereby avoiding the divergence of the Hubble parameter at late times, which is another characteristic feature of the non-local models treated in the literature. Furthermore, the effective gravitational coupling $G_{\rm eff}$ inherits the oscillatory behaviour, resulting in alternating epochs of stronger and weaker gravity. This framework is argued to have potentially interesting implications for the $H_0$ and the $\sigma_8$ tensions.
What is the Seiberg–Witten map exactly?

We give a conceptual treatment of the Seiberg–Witten map as a quasi-isomorphism of differential graded algebras. The corresponding algebras have a very simple form, leading to explicit recurrence formulas for the quasi-isomorphism. Unlike most previous papers, our recurrence relations are nonperturbative in the parameter of non-commutativity. Using the language of quasi-isomorphisms, we give a homotopy classification of ambiguities in Seiberg–Witten maps. Possible generalizations to the Wess–Zumino complexes and some other algebras are briefly discussed.
Symbolic Discovery of Optimization Algorithms

We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program space. To bridge the large generalization gap between proxy and target tasks, we also introduce program selection and simplification strategies. Our method discovers a simple and effective optimization algorithm, $\textbf{Lion}$ ($\textit{Evo$\textbf{L}$ved S$\textbf{i}$gn M$\textbf{o}$me$\textbf{n}$tum}$). It is more memory-efficient than Adam as it only keeps track of the momentum. Different from adaptive optimizers, its update has the same magnitude for each parameter calculated through the sign operation. We compare Lion with widely used optimizers, such as Adam and Adafactor, for training a variety of models on different tasks. On image classification, Lion boosts the accuracy of ViT by up to 2% on ImageNet and saves up to 5x the pre-training compute on JFT. On vision-language contrastive learning, we achieve 88.3% $\textit{zero-shot}$ and 91.1% $\textit{fine-tuning}$ accuracy on ImageNet, surpassing the previous best results by 2% and 0.1%, respectively. On diffusion models, Lion outperforms Adam by achieving a better FID score and reducing the training compute by up to 2.3x. For autoregressive, masked language modeling, and fine-tuning, Lion exhibits a similar or better performance compared to Adam. Our analysis of Lion reveals that its performance gain grows with the training batch size. It also requires a smaller learning rate than Adam due to the larger norm of the update produced by the sign function. Additionally, we examine the limitations of Lion and identify scenarios where its improvements are small or not statistically significant. Lion is also successfully deployed in production systems such as Google search ads CTR model.