Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk-Averse Control of Markov Systems with Value Function Learning

We consider a control problem for a finite-state Markov system whose performance is evaluated by a coherent Markov risk measure. For each policy, the risk of a state is approximated by a function of its features, thus leading to a lower-dimensional policy evaluation problem, which involves non-differentiable stochastic operators. We introduce mini-batch transition risk mappings, which are particularly suited to our approach, and we use them to derive a robust learning algorithm for Markov policy evaluation. Finally, we discuss structured policy improvement in the feature-based risk-averse setting. The considerations are illustrated with an underwater robot navigation problem in which several waypoints must be visited and the observation results must be reported from selected transmission locations. We identify the relevant features, we test the simulation-based learning method, and we optimize a structured policy in a hyperspace containing all problems with the same number of relevant points.
Normed Spaces for Graph Embedding

Theoretical results from discrete geometry suggest that normed spaces can abstractly embed finite metric spaces with surprisingly low theoretical bounds on distortion in low dimensions. In this paper, inspired by this theoretical insight, we highlight normed spaces as a more flexible and computationally efficient alternative to several popular Riemannian manifolds for learning graph embeddings. Normed space embeddings significantly outperform several popular manifolds on a large range of synthetic and real-world graph reconstruction benchmark datasets while requiring significantly fewer computational resources. We also empirically verify the superiority of normed space embeddings on growing families of graphs associated with negative, zero, and positive curvature, further reinforcing the flexibility of normed spaces in capturing diverse graph structures as graph sizes increase. Lastly, we demonstrate the utility of normed space embeddings on two applied graph embedding tasks, namely, link prediction and recommender systems. Our work highlights the potential of normed spaces for geometric graph representation learning, raises new research questions, and offers a valuable tool for experimental mathematics in the field of finite metric space embeddings. We make our code and data publically available.
On Merits of Faster-than-Nyquist Signaling in the Finite Blocklength Regime

We identify potential merits of faster-than-Nyquist (FTN) signaling in the finite blocklength (FBL) regime. A unique aspect of FTN signaling is that it can increase the blocklength by packing more data symbols within the same time and frequency to yield strictly higher number of independent signaling dimensions than that of Nyquist rate signaling. Using the finite-blocklength information theory, we provide tight bounds on the maximum channel coding rate (MCCR) of FTN signaling for any finite time-bandwidth product. The merits are categorized into two operating regions of FTN, i.e., when the time-acceleration factor of FTN, $\tau$, is above or below a certain threshold $\tau_{0}$. When $\tau>\tau_{0}$, FTN has both higher channel capacity and MCCR than that of Nyquist rate signaling, when the utilized pulse shape is non-sinc. Since the issues associated with the ideal sinc pulse only get exacerbated when packets are short, the benefit of FTN becomes more significant in the FBL regime. On the other hand, when $\tau<\tau_{0}$, the channel capacity is fixed but MCCR of FTN can continue to increase to a certain degree, thereby reducing the gap between the capacity and MCCR. This benefit is present regardless of the utilized pulse shape, including the ideal sinc-pulse, and is unique to the FBL regime. Instead of increasing MCCR for fixed block error rates, FTN can alternatively lower the block error rates for fixed channel coding rates. These results imply that FTN can lower the penalty from limited channel coding over short blocklength and can improve the performance and reliability of short packet communications.
Higgs Portal Interpretation of the Belle II $B^+ \to K^+ \nu \nu$ Measurement

The Belle II experiment recently observed the decay $B^+ \to K^+ \nu \nu$ for the first time, with a measured value for the branching ratio of $ (2.3 \pm 0.7) \times 10^{-5}$. This result exhibits a $\sim 3\sigma$ deviation from the Standard Model (SM) prediction. The observed enhancement with respect to the Standard Model could indicate the presence of invisible light new physics. In this paper, we investigate whether this result can be accommodated in a minimal Higgs portal model, where the SM is extended by a singlet Higgs scalar that decays invisibly to dark sector states. We find that current and future bounds on invisible decays of the 125 GeV Higgs boson completely exclude a new scalar with a mass $\gtrsim 10$ GeV. On the other hand, the Belle II results can be successfully accommodated if the new scalar is lighter than $B$ mesons but heavier than kaons. We also investigate the cosmological implications of the new states and explore the possibility that they are part of an abelian Higgs extension of the SM. Future Higgs factories are expected to place stringent bounds on the invisible branching ratio of the 125 GeV Higgs boson, and will be able to definitively test the region of parameter space favored by the Belle II results.
Fixed-point methods for long-term power control and beamforming design in large-scale MIMO

This study presents novel applications of fixed-point methods to solve previously open joint power control and beamforming design problems in modern large-scale MIMO systems, e.g., based on the cell-free massive MIMO and XL-MIMO concepts. In particular, motivated by the need for scalable system architectures, we revisit the classical sum power minimization and max-min fair design criteria by considering long-term power control and beamforming design based on channel statistics and possibly limited channel state information (CSI) sharing across distributed processing units. This approach is believed to mitigate the severe scalability issues of competing short-term optimal algorithms in the literature, which must be executed for every channel realization by a central controller endowed with global CSI, hence imposing very demanding requirements in terms of computation and interconnection capabilities. The obtained optimal algorithms are then illustrated and compared against existing short-term and long-term approaches via numerical simulations in a cell-free massive MIMO setup.
An extensive analysis of the sub-parsec region of 3C 84

The study of jet launching in AGN is an important research method to better understand supermassive black holes (SMBHs) and their immediate surroundings. The main theoretical jet launching scenarios invoke either magnetic field lines anchored to the black hole's (BH) accretion disc (Blandford&Payne 1982) or a magnetic field, which is directly connected to its rotating ergosphere (Blandford&Znajek 1977). The nearby and bright radio galaxy 3C84 (NGC1275) is a very suitable target for testing different jet launching mechanisms, as well as for the study of the innermost, sub-parsec scale AGN structure and the jet origin. Very long baseline interferometry (VLBI) - specifically at millimetre wavelengths - offers an unparalleled view into the physical processes in action, in the close vicinity of SMBHs. Utilising such mm-VLBI observations of 3C84, we study the jet kinematics of the VLBI core region of 3C84 by employing all available, high sensitivity 3 mm-VLBI data sets of this source. As part of this analysis we associate the component ejection events with the variability light-curves at different radio frequencies and in the $\gamma$-rays. Furthermore, by cross-correlating these light-curves, we determine their time-lags and draw conclusions regarding the location of the high energy emission close to the jet base.
Grounding Everything: Emerging Localization Properties in Vision-Language Transformers

Vision-language foundation models have shown remarkable performance in various zero-shot settings such as image retrieval, classification, or captioning. But so far, those models seem to fall behind when it comes to zero-shot localization of referential expressions and objects in images. As a result, they need to be fine-tuned for this task. In this paper, we show that pretrained vision-language (VL) models allow for zero-shot open-vocabulary object localization without any fine-tuning. To leverage those capabilities, we propose a Grounding Everything Module (GEM) that generalizes the idea of value-value attention introduced by CLIPSurgery to a self-self attention path. We show that the concept of self-self attention corresponds to clustering, thus enforcing groups of tokens arising from the same object to be similar while preserving the alignment with the language space. To further guide the group formation, we propose a set of regularizations that allows the model to finally generalize across datasets and backbones. We evaluate the proposed GEM framework on various benchmark tasks and datasets for semantic segmentation. It shows that GEM not only outperforms other training-free open-vocabulary localization methods, but also achieves state-of-the-art results on the recently proposed OpenImagesV7 large-scale segmentation benchmark.
Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion

Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation. Current state-of-the-art automatic MS lesion segmentation methods utilize modified U-Net-like architectures. However, in the literature, dedicated architecture modifications were always required to maximize their performance. In addition, the best-performing methods have not proven to be generalizable to diverse test datasets with contrast variations and image artifacts. In this work, we developed an accurate and generalizable MS lesion segmentation model using the well-known U-Net architecture without further modification. A novel test-time self-ensembled lesion fusion strategy is proposed that not only achieved the best performance using the ISBI 2015 MS segmentation challenge data but also demonstrated robustness across various self-ensemble parameter choices. Moreover, equipped with instance normalization rather than batch normalization widely used in literature, the model trained on the ISBI challenge data generalized well on clinical test datasets from different scanners.
The CURE To Vulnerabilities in RPKI Validation

Over recent years, the Resource Public Key Infrastructure (RPKI) has seen increasing adoption, with now 37.8% of the major networks filtering bogus BGP routes. Systems interact with the RPKI over Relying Party (RP) implementations that fetch RPKI objects and feed BGP routers with the validated prefix-ownership data. Consequently, any vulnerabilities or flaws within the RP software can substantially threaten the stability and security of Internet routing. We uncover severe flaws in all popular RP implementations, making them susceptible to path traversal attacks, remotely triggered crashes, and inherent inconsistencies, violating RPKI standards. We report a total of 18 vulnerabilities that canbe exploited to downgrade RPKI validation in border routers or, worse, enable poisoning of the validation process, resulting in malicious prefixes being wrongfully validated and legitimate RPKI-covered prefixes failing validation. Furthermore, our research discloses inconsistencies in the validation process, with two popular implementations leaving 8149 prefixes unprotected from hijacks, 6405 of which belong to Amazon. While these findings are significant in their own right, our principal contribution lies in developing CURE, the first-of-its-kind system to systematically detect bugs, vulnerabilities, and RFC compliance issues in RP implementations via automated test generation. CURE is a powerful RPKI publication point emulator that enables easy and efficient fuzzing of complex RP validation pipelines. It is designed with a set of novel techniques, utilizing differential and stateful fuzzing. We generated over 600 million test cases and tested all popular RPs on them. Following our disclosure, the vendors already assigned CVEs to the vulnerabilities we found.
Electronic properties of nickelate superconductor R3Ni2O7 with oxygen vacancies

The discovery of superconductivity in La3Ni2O7 has attracted significant research interest in the field of nickelate superconductors. Despite extensive studies on pristine La3Ni2O7, the impact of oxygen vacancies (VO), a common type of intrinsic defect in oxides, on electronic structures and superconductivity in La3Ni2O7 remains unclear. In this article, we identify the most energetically favorable location for VO formation as the oxygen atom connecting the NiO6 bilayer, resulting in a significant reduction in the lattice constant along the c-axis. Interestingly, the electronic structure undergoes notable changes, particularly for the Ni dz2 and Ni dx2-y2 orbitals. The Ni dz2 orbitals change from partially filled in the pristine La3Ni2O7 to completely filled in the presence of VO, leading to a considerable decrease of its proportion near the Fermi level. Conversely, the proportion of Ni dx2-y2 states increases due to the orbital localization and slight upward shift. Additionally, we observe a significant increase in the hopping of intra-bilayer Ni dz2 orbitals when the VO exists, but with an opposite sign, which differs greatly from the previous understanding. The inter-orbital hopping between Ni dz2 and Ni dx2-y2 orbitals also changes its sign in the presence of VO. Our results indicate that the formation of VO may be harmful to the superconductivity in La3Ni2O7, given the general assumption for the critical role of Ni dz2 in generating superconductivity. Furthermore, we suggest that Ce3Ni2O7, which shares similar electronic structures to La3Ni2O7 but has a larger lattice volume, may be a better candidate for nickelate superconductor due to its lower VO concentration.
Shallow Water Table Fluctuations in Relation to Soil Penetration Resistance

Hydrologic modeling of catchments is frequently hampered by lack of information on subsurface stratigraphy and zones of preferred flow. We evaluated the usefulness of soil penetration resistance, easily measured by a dynamic cone penetrometer, together with measurements of ground water level fluctuations, as a cost‐effective means to infer subsurface flow patterns. At our field site at Sleepers River, Vermont, penetration resistance was lowest in the surfi‐cial 10 to 30 cm, then typically increased to a local maximum at 60 to 80 cm, which we interpreted as the soil/till interface. Below this depth usually lies a zone of decreased resistance in the till, giving way to either a gradual or abrupt increase in resistance toward the bedrock surface at 1 to 4.5 m depth. Penetration resistance had a weak but significant negative correlation with saturated hydraulic conductivity determined by bail tests (r2= 0.25, p < 0.05). At many wells, monthly ground water levels tended to cluster at or just above the resistant zone near the soil/till interface. Chemical and isotopic dynamics in nested wells finished above and below the resistant zone suggest that the zone may temporarily isolate the deeper ground water reservoir from meltwater inputs, which were clearly identified by low δ18O values. In ground water discharge zones, δ18O values tended to converge throughout the profile. In contrast, dissolved organic carbon (DOC) maintained a gradient of increasing concentration toward land surface, even in otherwise well‐mixed waters, reflecting its rapid release from organic horizons. Understanding the effect of soil penetration resistance on ground water behavior may be useful in future catchment modeling efforts.
PipeOptim: Ensuring Effective 1F1B Schedule with Optimizer-Dependent Weight Prediction

Asynchronous pipeline model parallelism with a"1F1B"(one forward, one backward) schedule generates little bubble overhead and always provides quite a high throughput. However, the"1F1B"schedule inevitably leads to weight inconsistency and weight staleness issues due to the cross-training of different mini-batches across GPUs. To simultaneously address these two problems, in this paper, we propose an optimizer-dependent weight prediction strategy (a.k.a PipeOptim) for asynchronous pipeline training. The key insight of our proposal is that we employ a weight prediction strategy in the forward pass to ensure that each mini-batch uses consistent and staleness-free weights to compute the forward pass. To be concrete, we first construct the weight prediction scheme based on the update rule of the used optimizer when training the deep neural network models. Then throughout the"1F1B"pipelined training, each mini-batch is mandated to execute weight prediction ahead of the forward pass, subsequently employing the predicted weights to perform the forward pass. As a result, PipeOptim 1) inherits the advantage of the"1F1B"schedule and generates pretty high throughput, and 2) can ensure effective parameter learning regardless of the type of the used optimizer. To verify the effectiveness of our proposal, we conducted extensive experimental evaluations using eight different deep-learning models spanning three machine-learning tasks including image classification, sentiment analysis, and machine translation. The experiment results demonstrate that PipeOptim outperforms the popular pipelined approaches including GPipe, PipeDream, PipeDream-2BW, and SpecTrain. The code of PipeOptim can be accessible at https://github.com/guanleics/PipeOptim.
A generalized framework to transport geophysical fields: a differential geometry perspective

To estimate displacements of physical fields, a general framework is proposed. Considering that for each state variable, a tensor field can be associated, ways these displacements act on different state variables will differ according to the tensor field definitions. This perspective provides a differential-geometry-based reformulation of the generalized optical flow (OF) algorithm. Using the proposed framework, optimisation procedures can explicitly ensure the conservation of certain physical quantities (total mass, total vorticity, total kinetic energy, etc.). Existence and uniqueness of the solutions to the local optimisation problem are demonstrated, leading to a new nudging strategy using all-available observations to infer displacements of both observed and unobserved state variables. Using the proposed nudging method before EnKF, numerical results show that ensemble data assimilation better preserves the intrinsic structure of underline physical processes if the ensemble members are aligned with the observations.
Near-real-time monitoring of global ocean carbon sink

Mitigation of climate change will highly rely on a carbon emission trajectory that achieves carbon neutrality by the 2050s. The ocean plays a critical role in modulating climate change by sequestering CO2 from the atmosphere. Relying on the multidisciplinary cutting-edge methodologies and technologies, the near-real-time monitoring of global ocean carbon sinks from January 2022 to July 2023 aims to provide the world's latest assessment of monthly and gridded global ocean carbon sinks based on machine learning and other data science technologies. The project will help us find a robust route to deal with climate change, which will significantly promote the ocean carbon sinks research and will be of great interest for policy makers, researchers, and the public. This research aims to build up an integrated machine learning framework and methodology for assessing global ocean carbon neutral process; development of near-real-time dataset; development of visualization platform; research papers published in international prestigious journals; an executive report openly accessible to policy makers and the public.
Driven transparent quantum graphs

In this paper, we discuss the concept of quantum graphs with transparent vertices by considering the case where the graph interacts with an external time-independent field. In particular, we address the problem of transparent boundary conditions for quantum graphs, building on previous work on transparent boundary conditions for the stationary Schrodinger equation on a line. Physically relevant constraints making the vertex transparent under boundary conditions in the form of (weight) continuity and Kirchhoff rules are derived using two methods, the scattering approach and transparent boundary conditions for the time-independent Schrodinger equation. The latter is derived by extending the transparent boundary condition concept to the time-independent Schrodinger equation on driven quantum graphs. We also discuss how the eigenvalues and eigenfunctions of a quantum graph are influenced not only by its topology, but also by the shape(type) of a potential when an external field is involved.
Joint Task Partitioning and Parallel Scheduling in Device-Assisted Mobile Edge Networks

With the development of the Internet of Things (IoT), certain IoT devices have the capability to not only accomplish their own tasks but also simultaneously assist other resource-constrained devices. Therefore, this paper considers a device-assisted mobile edge computing system that leverages auxiliary IoT devices to alleviate the computational burden on the edge computing server and enhance the overall system performance. In this study, computationally intensive tasks are decomposed into multiple partitions, and each task partition can be processed in parallel on an IoT device or the edge server. The objective of this research is to develop an efficient online algorithm that addresses the joint optimization of task partitioning and parallel scheduling under time-varying system states, posing challenges to conventional numerical optimization methods. To address these challenges, a framework called online task partitioning action and parallel scheduling policy generation (OTPPS) is proposed, which is based on deep reinforcement learning (DRL). Specifically, the framework leverages a deep neural network (DNN) to learn the optimal partitioning action for each task by mapping input states. Furthermore, it is demonstrated that the remaining parallel scheduling problem exhibits NP-hard complexity when considering a specific task partitioning action. To address this subproblem, a fair and delay-minimized task scheduling (FDMTS) algorithm is designed. Extensive evaluation results demonstrate that OTPPS achieves near-optimal average delay performance and consistently high fairness levels in various environmental states compared to other baseline schemes.