Miracle-dz/UniFAR-FTUs huggingface dataset:
Blockage-Aware Robust Beamforming in RIS-Aided Mobile Millimeter Wave MIMO Systems

Millimeter wave (mmWave) communications are sensitive to blockage over radio propagation paths. The emerging paradigm of reconfigurable intelligent surface (RIS) has the potential to overcome this issue by its ability to arbitrarily reflect the incident signals toward desired directions. This paper proposes a Neyman-Pearson (NP) criterion-based blockage-aware algorithm to improve communication resilience against blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for downlink mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. To minimize the outage probability, a robust RIS beamformer with variant beamwidth is designed to combat uncertain channel state information (CSI). For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we leverage a subspace constraint to reduce the scope of the projection operation and formulate a new Nesterov momentum acceleration scheme to speed up the convergence process of PGD. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach, and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate.
MediSwift: Efficient Sparse Pre-trained Biomedical Language Models

Large language models (LLMs) are typically trained on general source data for various domains, but a recent surge in domain-specific LLMs has shown their potential to outperform general-purpose models in domain-specific tasks (e.g., biomedicine). Although domain-specific pre-training enhances efficiency and leads to smaller models, the computational costs of training these LLMs remain high, posing budgeting challenges. We introduce MediSwift, a suite of biomedical LMs that leverage sparse pre-training on domain-specific biomedical text data. By inducing up to 75% weight sparsity during the pre-training phase, MediSwift achieves a 2-2.5x reduction in training FLOPs. Notably, all sparse pre-training was performed on the Cerebras CS-2 system, which is specifically designed to realize the acceleration benefits from unstructured weight sparsity, thereby significantly enhancing the efficiency of the MediSwift models. Through subsequent dense fine-tuning and strategic soft prompting, MediSwift models outperform existing LLMs up to 7B parameters on biomedical tasks, setting new benchmarks w.r. t efficiency-accuracy on tasks such as PubMedQA. Our results show that sparse pre-training, along with dense fine-tuning and soft prompting, offers an effective method for creating high-performing, computationally efficient models in specialized domains.
Petrological and Mineralogical Aspects of Epithermal Low-Sulfidation Au- and Porphyry Cu-Style Mineralization, Navilawa Caldera, Fiji

The Navilawa caldera is the remnant of a shoshonitic volcano on Viti Levu, Fiji, and sits adjacent to the low-sulfidation Tuvatu epithermal Au–Te deposit. The caldera occurs along the Viti Levu lineament, approximately 50 km SW of the Tavua caldera, which hosts the giant low-sulfidation Emperor epithermal Au–Te deposit. Both calderas host alkaline rocks of nearly identical age (~5.4–4.6 Ma) and mineralization that occurred in multiple stages. The gold mineralization in these locations is spatially and genetically related to monzonite intrusions and low-grade porphyry Cu-style mineralization. Potassic, propylitic, phyllic, and argillic alteration extends from the Tuvatu Au–Te deposit towards the central, northern, and eastern parts of the Navilawa caldera where it is spatially associated with low-grade porphyry Cu–Au mineralization at the Kingston prospect and various epithermal Au–(Te) vein systems, including the Banana Creek and Tuvatu North prospects. Chalcopyrite, and minor bornite, occurs in quartz–calcite–(adularia) veins in the Kingston deposit associated with weak propylitic and phyllic alteration, whereas NE-trending epithermal gold veins at the Banana Creek and Tuvatu North prospects are associated with weak potassic alteration that is overprinted by propylitic and phyllic alteration. Gold is accompanied by chalcopyrite, galena, and sphalerite in quartz–pyrite veins that also have a Ag–As–Hg–Te signature. The temperature range for phyllosilicates in the phyllic alteration (chlorite ± smectite ± corrensite ± illite) is in good agreement with temperatures recorded from previous fluid inclusion studies of quartz at the Banana Creek Au prospect (~260 °C) and the nearby Tuvatu Au–Te deposit (205 to 382 °C). Sulfur isotope compositions of pyrite (−6.2 to +0.4‰) from the Banana Creek prospect indicate a likely magmatic source of sulfur. Oxidation of the ore fluids or a direct addition of volatiles to the hydrothermal fluids may account for the lighter isotopic values. The similarities of the igneous rock types and compositions, transition from porphyry- to epithermal-style mineralization, alteration assemblages, paragenetic relationships, and stable isotope data suggest a common origin for the porphyry- and epithermal-style mineralization within the Navilawa and between the Navilawa and Tavua calderas.
Jupiter's Polar Vortex Crystals Explored using the Shallow Water Equations

At the poles of Jupiter, cyclonic vortices are clustered together in patterns made up of equilateral triangles called vortex crystals. Such patterns are seen in laboratory flows but never before in a planetary atmosphere, where the planet's rotation and gravity add new physics. Here we use the shallow water (SW) equations at the pole of a rotating planet to study the emergence and evolution of vortices starting from an initial random pattern of small-scale turbulence. The flow is in a single layer with a free surface whose slope produces the horizontal pressure gradient force. We explored three parameters in the problem: the mean kinetic energy of the initial turbulence, the horizontal scale of the initial turbulence, and the radius of deformation of the undisturbed fluid layer. We find that some regions of this parameter space lead to vortex crystals and others lead to chaotic behavior and mergers. Our results identified that the relative change of the layer thickness is the key quantity that determines whether the vortex crystal or chaotic patterns emerge.
Unveiling the physics of the spin-orbit coupling at the surface of a model topological insulator: from theory to experiments

Spin-orbit interaction affects the band structure of topological insulators beyond the opening of an inverted gap in the bulk bands, and the understanding of its effects on the surface states is of primary importance to access the underlying physics of these exotic states. Here, we propose an $\textit{ab initio}$ approach benchmarked by pump-probe angle-resolved photoelectron spectroscopy data to model the effect of spin-orbit coupling on the surface states of a topological insulator. The critical novelty of our approach lies in the possibility of accounting for a partial transfer of the spin-orbit coupling to the surface states, mediated by the hybridization with the surface resonance states. In topological insulators, the fraction of transferred spin-orbit coupling influences the strength of the hexagonal warping of the surface states, which we use as a telltale of the capability of our model to reproduce the experimental dispersion. The comparison between calculations and measurements, of both the unoccupied and part of the occupied Dirac cone, indicates that the fraction of spin-orbit coupling transferred to the surface states by hybridization with the resonance states is between 70% and 85% of its full atomic value. This offers a valuable insight to improve the modeling of surface state properties in topological insulators for both scientific purposes and technological applications.
Characterizing Signalling: Connections between Causal Inference and Space-time Geometry

Causality is pivotal to our understanding of the world, presenting itself in different forms: information-theoretic and relativistic, the former linked to the flow of information, the latter to the structure of space-time. Leveraging a framework introduced in PRA, 106, 032204 (2022), which formally connects these two notions in general physical theories, we study their interplay. Here, information-theoretic causality is defined through a causal modelling approach. First, we improve the characterization of information-theoretic signalling as defined through so-called affects relations. Specifically, we provide conditions for identifying redundancies in different parts of such a relation, introducing techniques for causal inference in unfaithful causal models (where the observable data does not"faithfully"reflect the causal dependences). In particular, this demonstrates the possibility of causal inference using the absence of signalling between certain nodes. Second, we define an order-theoretic property called conicality, showing that it is satisfied for light cones in Minkowski space-times with $d>1$ spatial dimensions but violated for $d=1$. Finally, we study the embedding of information-theoretic causal models in space-time without violating relativistic principles such as no superluminal signalling (NSS). In general, we observe that constraints imposed by NSS in a space-time and those imposed by purely information-theoretic causal inference behave differently. We then prove a correspondence between conical space-times and faithful causal models: in both cases, there emerges a parallel between these two types of constraints. This indicates a connection between informational and geometric notions of causality, and offers new insights for studying the relations between the principles of NSS and no causal loops in different space-time geometries and theories of information processing.
Distributional Dataset Distillation with Subtask Decomposition

What does a neural network learn when training from a task-specific dataset? Synthesizing this knowledge is the central idea behind Dataset Distillation, which recent work has shown can be used to compress large datasets into a small set of input-label pairs ($\textit{prototypes}$) that capture essential aspects of the original dataset. In this paper, we make the key observation that existing methods distilling into explicit prototypes are very often suboptimal, incurring in unexpected storage cost from distilled labels. In response, we propose $\textit{Distributional Dataset Distillation}$ (D3), which encodes the data using minimal sufficient per-class statistics and paired with a decoder, we distill dataset into a compact distributional representation that is more memory-efficient compared to prototype-based methods. To scale up the process of learning these representations, we propose $\textit{Federated distillation}$, which decomposes the dataset into subsets, distills them in parallel using sub-task experts and then re-aggregates them. We thoroughly evaluate our algorithm on a three-dimensional metric and show that our method achieves state-of-the-art results on TinyImageNet and ImageNet-1K. Specifically, we outperform the prior art by $6.9\%$ on ImageNet-1K under the storage budget of 2 images per class.
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models

This paper introduces Bespoke Non-Stationary (BNS) Solvers, a solver distillation approach to improve sample efficiency of Diffusion and Flow models. BNS solvers are based on a family of non-stationary solvers that provably subsumes existing numerical ODE solvers and consequently demonstrate considerable improvement in sample approximation (PSNR) over these baselines. Compared to model distillation, BNS solvers benefit from a tiny parameter space ($<$200 parameters), fast optimization (two orders of magnitude faster), maintain diversity of samples, and in contrast to previous solver distillation approaches nearly close the gap from standard distillation methods such as Progressive Distillation in the low-medium NFE regime. For example, BNS solver achieves 45 PSNR / 1.76 FID using 16 NFE in class-conditional ImageNet-64. We experimented with BNS solvers for conditional image generation, text-to-image generation, and text-2-audio generation showing significant improvement in sample approximation (PSNR) in all.
The Science of Data Collection: Insights from Surveys can Improve Machine Learning Models

Whether future AI models make the world safer or less safe for humans rests in part on our ability to efficiently collect accurate data from people about what they want the models to do. However, collecting high quality data is difficult, and most AI/ML researchers are not trained in data collection methods. The growing emphasis on data-centric AI highlights the potential of data to enhance model performance. It also reveals an opportunity to gain insights from survey methodology, the science of collecting high-quality survey data. In this position paper, we summarize lessons from the survey methodology literature and discuss how they can improve the quality of training and feedback data, which in turn improve model performance. Based on the cognitive response process model, we formulate specific hypotheses about the aspects of label collection that may impact training data quality. We also suggest collaborative research ideas into how possible biases in data collection can be mitigated, making models more accurate and human-centric.
Sharp bounds for max-sliced Wasserstein distances

We obtain essentially matching upper and lower bounds for the expected max-sliced 1-Wasserstein distance between a probability measure on a separable Hilbert space and its empirical distribution from $n$ samples. By proving a Banach space version of this result, we also obtain an upper bound, that is sharp up to a log factor, for the expected max-sliced 2-Wasserstein distance between a symmetric probability measure $\mu$ on a Euclidean space and its symmetrized empirical distribution in terms of the operator norm of the covariance matrix of $\mu$ and the diameter of the support of $\mu$.
Dynamic Mode Decomposition for Transient Cavitation Bubbles Imaging in Pulsed High Intensity Focused Ultrasound Therapy

Pulsed high-intensity focused ultrasound (pHIFU) can induce sparse de novo inertial cavitation without the introduction of exogenous contrast agents, promoting mild mechanical disruption in targeted tissue. Because the bubbles are small and rapidly dissolve after each HIFU pulse, mapping transient bubbles and obtaining real-time quantitative metrics correlated to tissue damage are challenging. Prior work introduced Bubble Doppler, an ultrafast power Doppler imaging method as a sensitive means to map cavitation bubbles. The main limitation of that method was its reliance on conventional wall filters used in Doppler imaging and optimized for imaging blood flow rather than transient scatterers. This study explores Bubble Doppler enhancement using dynamic mode decomposition (DMD) of a matrix created from a Doppler ensemble for mapping and extracting the characteristics of transient cavitation bubbles. DMD was first tested in silico with a numerical dataset mimicking the spatiotemporal characteristics of backscattered signal from tissue and bubbles. The performance of DMD filter was compared to other widely used Doppler wall filters - singular value decomposition (SVD) and infinite impulse response (IIR) highpass filter. DMD was then applied to an ex vivo tissue dataset where each HIFU pulse was immediately followed by a plane wave Doppler ensemble. In silico DMD outperformed SVD and IIR high pass filter and ex vivo provided physically interpretable images of the modes associated with bubbles and their corresponding temporal decay rates. These DMD modes can be trackable over the duration of pHIFU treatment using k-means clustering method, resulting in quantitative indicators of treatment progression.
Eocene Pachynolophinae (Perissodactyla, Palaeotheriidae) from China, and their palaeobiogeographical implications

The Eocene perissodactyl family Palaeotheriidae has traditionally been considered to be a nearly endemic European group within Equoidea, but a few palaeotheres have been reported from Asia. Here, I reanalyse a maxilla containing M1–3 from the Lunan Basin, Yunnan Province, China. This element was initially assigned to a new tapiromorph species, Lophialetes yunnanensis, but is here placed in a new genus Lophiohippus within Pachynolophinae based mainly on the absence of mesostyles, the strongly oblique metalophs, the strong development of lophodonty, parastyles overlapping metastyles of preceding teeth and situated mesial to the paracone, and the fact that M3 is longer than wide and has a large and buccally deflected metastyle. Lophiohippus differs from European Anchilophus and Paranchilophus in that the parastyles are situated mesial or even slightly lingual, rather than mesiobuccal, to the paracones, and M3 is markedly relatively larger than M1. I further reanalyse Qianohippus magicus from the Shinao Basin of Guizhou Province, China, in which the complete dentition is known. Qianohippus is characterized by a molariform P2 and non‐molariform P3–4; a relatively high degree of lophodonty; the absence of mesostyles; an angular bending in the protoloph on P3‐M3 and the metaloph on M1–3 at the paraconule and metaconule, respectively; and weakly developed ‘metastylid’ on the lower cheek teeth. A cladistic analysis supports a close relationship between Lophiohippus yunnanensis and Paranchilophus, and suggests that Qianohippus is closely related to some derived pachynolophs. The appearance of the pachynolophins Lophiohippus and Qianohippus in China supports the existence of a biogeographical connection between Europe and Asia in the Middle‐Late Eocene, and the dispersal route was probably along the Tethyan microcontinents in the south.
Label-free imaging of cellular organization in living mammalian cells via external apodization phase-contrast microscopy

Developing techniques to visualize the dynamics of cellular organization, which impacts the spatiotemporal functionality of biomolecules, is essential for elucidating mechanisms that govern cellular behavior. In this study, we demonstrated that label-free external apodization phase-contrast (ExAPC) microscopy is a valuable modality for this purpose, as this method can be used to visualize refractive index changes in intracellular structures, achieves high spatiotemporal resolution and suppresses halos. Utilizing the ability of ExAPC microscopy to visualize various intracellular structures simultaneously, we successfully captured detailed alterations in cellular organization during diverse cellular behaviors. Furthermore, through quantitatively analyzing the images obtained by combining ExAPC microscopy with fluorescence microscopy, we discovered characteristic heterogeneities in biomolecular condensates, lipid droplets, and mitochondria. This study underscores the potential of ExAPC microscopy for providing detailed images of alterations in cellular organization associated with various cellular behaviors, thereby corroborating the existing knowledge and potentially revealing novel discoveries.
Rational structure-guided design of a blood stage malaria vaccine immunogen presenting a single epitope from PfRH5

Despite recent success in licencing of two malaria vaccines, there continues to be an urgent need for improved vaccine immunogens as the world aims for malaria eradication. The invasion of erythrocytes by Plasmodium falciparum is an essential step in the life cycle of the parasite, preceding symptoms of disease and parasite transmission. Antibodies which target the PfRH5 protein are highly effective at preventing erythrocyte invasion and the most effective growth-inhibitory antibodies bind to a single epitope. Here we used Rosetta-based protein design to produce a focused synthetic immunogen on which this epitope is presented on a small scaffold. Structural biology and biophysics were used to demonstrate that the immunogen is correctly folded and binds neutralising monoclonal antibodies with at least nanomolar affinity. In immunised rats, the immunogen induced PfRH5-targeting antibodies that inhibit parasite growth at a thousand-fold lower concentration that those induced through immunisation with PfRH5. Finally, we show that priming with the focused immunogen and boosting with PfRH5 achieves the best balance between antibody quality and quantity and induces the most effective growth-inhibitory response. This rationally designed vaccine immunogen is now available for use as part of future malaria vaccines, alone or in combination with other immunogens. One Sentence Summary Rational, structure-guided design has been used to produce a vaccine immunogen in which the epitope for the most growth-inhibitory monoclonal antibody targeting PfRH5 has been correctly presented on a synthetic scaffold for use in a vaccine to protect from blood-stage malaria.
Determining the rate-limiting processes for cell division in Escherichia coli

A critical cell cycle checkpoint for most bacteria is the onset of constriction when the septal peptidoglycan synthesis starts. According to the current understanding, the arrival of FtsN to midcell triggers this checkpoint in Escherichia coli. Recent structural and in vitro data suggests that recruitment of FtsN to the Z-ring leads to a conformational switch in actin-like FtsA, which links FtsZ protofilaments to the cell membrane and acts as a hub for the late divisome proteins. Here, we investigate this putative pathway using in vivo measurements and stochastic cell cycle modeling. Quantitatively upregulating protein concentrations and determining the resulting division timings shows that FtsN and FtsA numbers are not rate-limiting for the division in E. coli. However, at higher overexpression levels, they affect divisions: FtsN by accelerating and FtsA by inhibiting them. At the same time, we find that the numbers of FtsZ in the cell are rate-limiting for cell divisions in addition to other processes. Altogether, these findings suggest that instead of FtsN, FtsZ protofilaments drive the conformational switch of FtsA and lead to the onset of constriction. Our data is also suggestive that FtsA minirings are not present at any significant numbers in wild-type cells.