Miracle-dz/UniFAR-FTUs huggingface dataset:
Solving the inverse potential problem in the parabolic equation by the deep neural networks method

In this work, we consider an inverse potential problem in the parabolic equation, where the unknown potential is a space-dependent function and the used measurement is the final time data. The unknown potential in this inverse problem is parameterized by deep neural networks (DNNs) for the reconstruction scheme. First, the uniqueness of the inverse problem is proved under some regularities assumption on the input sources. Then we propose a new loss function with regularization terms depending on the derivatives of the residuals for partial differential equations (PDEs) and the measurements. These extra terms effectively induce higher regularity in solutions so that the ill-posedness of the inverse problem can be handled. Moreover, we establish the corresponding generalization error estimates rigorously. Our proofs exploit the conditional stability of the classical linear inverse source problems, and the mollification on the noisy measurement data which is set to reduce the perturbation errors. Finally, the numerical algorithm and some numerical results are provided.
Gravitational form factors of $\Delta$ baryon via QCD sum rules

The gravitational form factors of a hadron are defined through the matrix elements of the energy-momentum tensor current, which can be decomposed into the quark and gluonic parts, between the hadronic states. These form factors provide important information for answering fundamental questions about the distribution of the energy, the spin, the pressure and the shear forces inside the hadrons. Theoretical and experimental studies of these form factors provide exciting insights on the inner structure and geometric shapes of hadrons. Inspired by this, the gravitational form factors of $\Delta$ resonance are calculated by employing the QCD sum rule approach. The acquired gravitational form factors are used to calculate the composite gravitational form factors like the energy and angular momentum multipole form factors, D-terms related to the mechanical properties like the internal pressure and shear forces as well as the mass radius of the system. The predictions are compared with the existing results in the literature.
Semi-Grant-Free Orthogonal Multiple Access With Partial-Information for Short Packet Transmissions

Traditional multiple access schemes, as well as more recent preamble-based schemes, cannot achieve the extremely low latency, complexity, and collision probability required by the next-generation Internet-of-Things (IoT) networks to operate. To address such issues and further reduce the latency and packet loss, we introduce a novel semi-grant-free multiple access protocol for short packet transmission, the partial-information multiple access (PIMA) scheme. PIMA transmissions are organized in frames, and in the partial information acquisition (PIA) sub-frame of each frame, the base station (BS) estimates the number of active devices, i.e., the devices having packets waiting for transmission in their queue. Based on this estimate, the BS chooses both the total number of slots to be allocated in the data transmission (DT) sub-frame and the respective user-to-slot assignment. Although collisions may still occur due to multiple users assigned to the same slot, they are drastically reduced with respect to slotted ALOHA-based schemes, while achieving lower latency than both time-division multiple-access (TDMA) and preamble-based protocols, due to the extremely reduced overhead of the PIA sub-frame. We assess the performance of PIMA under various activation statistics, proving the robustness of the proposed solution to the traffic intensity, also with traffic bursts.
The BARN Challenge 2023 - Autonomous Navigation in Highly Constrained Spaces - Inventec Team

Navigation in the real-world is hard and filled with complex scenarios. The Benchmark Autonomous Robot Navigation (BARN) Challenge is a competition that focuses on highly constrained spaces. Teams compete using a standard platform in a simulation and a real-world stage, with scenarios ranging from easy to challenging. This technical report presents the system and methods employed by the Inventec Team during the BARN Challenge 2023 (https://cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge23.html). At its core, our method uses the baseline learning-based controller LfLH. We developed extensions using a finite state machine to trigger recovery behaviors, and introduced two alternatives for forward safety collision checks, based on footprint inflation and model-predictive control. Moreover, we also present a backtrack safety check based on costmap region-of-interest. Compared to the original baseline, we managed a significant increase in the navigation score, from 0.2334 to 0.2445 (4.76%). Overall, our team ranked second place both in simulation and in the real-world stage. Our code is publicly available at: (https://github.com/inventec-ai-center/inventec-team-barn-challenge-2023.git)
Phenotype-preserving metric design for high-content image reconstruction by generative inpainting

In the past decades, automated high-content microscopy demonstrated its ability to deliver large quantities of image-based data powering the versatility of phenotypic drug screening and systems biology applications. However, as the sizes of image-based datasets grew, it became infeasible for humans to control, avoid and overcome the presence of imaging and sample preparation artefacts in the images. While novel techniques like machine learning and deep learning may address these shortcomings through generative image inpainting, when applied to sensitive research data this may come at the cost of undesired image manipulation. Undesired manipulation may be caused by phenomena such as neural hallucinations, to which some artificial neural networks are prone. To address this, here we evaluate the state-of-the-art inpainting methods for image restoration in a high-content fluorescence microscopy dataset of cultured cells with labelled nuclei. We show that architectures like DeepFill V2 and Edge Connect can faithfully restore microscopy images upon fine-tuning with relatively little data. Our results demonstrate that the area of the region to be restored is of higher importance than shape. Furthermore, to control for the quality of restoration, we propose a novel phenotype-preserving metric design strategy. In this strategy, the size and count of the restored biological phenotypes like cell nuclei are quantified to penalise undesirable manipulation. We argue that the design principles of our approach may also generalise to other applications.
Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions

This work introduces TRON, a scalable session-based Transformer Recommender using Optimized Negative-sampling. Motivated by the scalability and performance limitations of prevailing models such as SASRec and GRU4Rec+, TRON integrates top-k negative sampling and listwise loss functions to enhance its recommendation accuracy. Evaluations on relevant large-scale e-commerce datasets show that TRON improves upon the recommendation quality of current methods while maintaining training speeds similar to SASRec. A live A/B test yielded an 18.14% increase in click-through rate over SASRec, highlighting the potential of TRON in practical settings. For further research, we provide access to our source code1 and an anonymized dataset2.
Reappearance of Thermalization Dynamics in the Late-Time Spectral Form Factor

The spectral form factor (SFF) is an important diagnostic of energy level repulsion in random matrix theory (RMT) and quantum chaos. The short-time behavior of the SFF as it approaches the RMT result acts as a diagnostic of the ergodicity of the system as it approaches the thermal state. In this work we observe that for systems without time-reversal symmetry, there is a second break from the RMT result at late time around the Heisenberg time. Long after thermalization has taken hold, and after the SFF has agreed with the RMT result to high precision for a time of order the Heisenberg time, the SFF of a large system will briefly deviate from the RMT behavior in a way exactly determined by its early time thermalization properties. The conceptual reason for this second deviation is the Riemann-Siegel lookalike formula, a resummed expression for the spectral determinant relating late time behavior to early time spectral statistics. We use the lookalike formula to derive a precise expression for the late time SFF for semi-classical quantum chaotic systems, and then confirm our results numerically for more general systems.
Sharp hypercontractivity for symmetric groups and its applications

A recently fertile strand of research in Group Theory is developing non-abelian analogues of classical combinatorial results for arithmetic Cayley graphs, describing properties such as growth, expansion, mixing, diameter, etc. We consider these problems for the symmetric and alternating groups. The case of normal Cayley graphs (those generated by unions of conjugacy classes) has seen significant progress via character theory (whereby Larsen and Shalev resolved several open problems), but the general case still remains poorly understood. In this paper we generalise the background assumption from being normal to being global (a pseudorandomness condition), replacing character bounds by spectral estimates for convolution operators of global functions, thus obtaining qualitative generalisations of several results on normal Cayley graphs. Furthermore, our theory in the pseudorandom setting can be applied (via density increment arguments) to several results for general sets that are not too sparse, including analogues of Polynomial Freiman-Ruzsa, Bogolyubov's lemma, Roth's theorem, the Waring problem and essentially sharp estimates for the diameter problem of Cayley graphs whose density is at least exponential in -n. Our main tool is a sharp new hypercontractive inequality for global functions on the symmetric group.
Matching Patients to Clinical Trials with Large Language Models

Clinical trials are vital in advancing drug development and evidence-based medicine, but their success is often hindered by challenges in patient recruitment. In this work, we investigate the potential of large language models (LLMs) to assist individual patients and referral physicians in identifying suitable clinical trials from an extensive selection. Specifically, we introduce TrialGPT, a novel architecture employing LLMs to predict criterion-level eligibility with detailed explanations, which are then aggregated for ranking and excluding candidate clinical trials based on free-text patient notes. We evaluate TrialGPT on three publicly available cohorts of 184 patients and 18,238 annotated clinical trials. The experimental results demonstrate several key findings: First, TrialGPT achieves high criterion-level prediction accuracy with faithful explanations. Second, the aggregated trial-level TrialGPT scores are highly correlated with expert eligibility annotations. Third, these scores prove effective in ranking clinical trials and exclude ineligible candidates. Our error analysis suggests that current LLMs still make some mistakes due to limited medical knowledge and domain-specific context understanding. Nonetheless, we believe the explanatory capabilities of LLMs are highly valuable. Future research is warranted on how such AI assistants can be integrated into the routine trial matching workflow in real-world settings to improve its efficiency.
A Predictive Model of Digital Information Engagement: Forecasting User Engagement With English Words by Incorporating Cognitive Biases, Computational Linguistics and Natural Language Processing

This study introduces and empirically tests a novel predictive model for digital information engagement (IE) - the READ model, an acronym for the four pivotal attributes of engaging information: Representativeness, Ease-of-use, Affect, and Distribution. Conceptualized within the theoretical framework of Cumulative Prospect Theory, the model integrates key cognitive biases with computational linguistics and natural language processing to develop a multidimensional perspective on information engagement. A rigorous testing protocol was implemented, involving 50 randomly selected pairs of synonymous words (100 words in total) from the WordNet database. These words' engagement levels were evaluated through a large-scale online survey (n = 80,500) to derive empirical IE metrics. The READ attributes for each word were then computed and their predictive efficacy examined. The findings affirm the READ model's robustness, accurately predicting a word's IE level and distinguishing the more engaging word from a pair of synonyms with an 84% accuracy rate. The READ model's potential extends across various domains, including business, education, government, and healthcare, where it could enhance content engagement and inform AI language model development and generative text work. Future research should address the model's scalability and adaptability across different domains and languages, thereby broadening its applicability and efficacy.
A Strategic Framework for Optimal Decisions in Football 1-vs-1 Shot-Taking Situations: An Integrated Approach of Machine Learning, Theory-Based Modeling, and Game Theory

Complex interactions between two opposing agents frequently occur in domains of machine learning, game theory, and other application domains. Quantitatively analyzing the strategies involved can provide an objective basis for decision-making. One such critical scenario is shot-taking in football, where decisions, such as whether the attacker should shoot or pass the ball and whether the defender should attempt to block the shot, play a crucial role in the outcome of the game. However, there are currently no effective data-driven and/or theory-based approaches to analyzing such situations. To address this issue, we proposed a novel framework to analyze such scenarios based on game theory, where we estimate the expected payoff with machine learning (ML) models, and additional features for ML models were extracted with a theory-based shot block model. Conventionally, successes or failures (1 or 0) are used as payoffs, while a success shot (goal) is extremely rare in football. Therefore, we proposed the Expected Probability of Shot On Target (xSOT) metric to evaluate players' actions even if the shot results in no goal; this allows for effective differentiation and comparison between different shots and even enables counterfactual shot situation analysis. In our experiments, we have validated the framework by comparing it with baseline and ablated models. Furthermore, we have observed a high correlation between the xSOT and existing metrics. This alignment of information suggests that xSOT provides valuable insights. Lastly, as an illustration, we studied optimal strategies in the World Cup 2022 and analyzed a shot situation in EURO 2020.
IPv6 Hitlists at Scale: Be Careful What You Wish For

Today's network measurements rely heavily on Internet-wide scanning, employing tools like ZMap that are capable of quickly iterating over the entire IPv4 address space. Unfortunately, IPv6's vast address space poses an existential threat for Internet-wide scans and traditional network measurement techniques. To address this reality, efforts are underway to develop "hitlists" of known-active IPv6 addresses to reduce the search space for would-be scanners. As a result, there is an inexorable push for constructing as large and complete a hitlist as possible. This paper asks: what are the potential benefits and harms when IPv6 hitlists grow larger? To answer this question, we obtain the largest IPv6 active-address list to date: 7.9 billion addresses, 898 times larger than the current state-of-the-art hitlist. Although our list is not comprehensive, it is a significant step forward and provides a glimpse into the type of analyses possible with more complete hitlists. We compare our dataset to prior IPv6 hitlists and show both benefits and dangers. The benefits include improved insight into client devices (prior datasets consist primarily of routers), outage detection, IPv6 roll-out, previously unknown aliased networks, and address assignment strategies. The dangers, unfortunately, are severe: we expose widespread instances of addresses that permit user tracking and device geolocation, and a dearth of firewalls in home networks. We discuss ethics and security guidelines to ensure a safe path towards more complete hitlists.
Efficient Interaction-Aware Interval Analysis of Neural Network Feedback Loops

In this paper, we propose a computationally efficient framework for interval reachability of systems with neural network controllers. Our approach leverages inclusion functions for the open-loop system and the neural network controller to embed the closed-loop system into a larger-dimensional embedding system, where a single trajectory over-approximates the original system's behavior under uncertainty. We propose two methods for constructing closed-loop embedding systems, which account for the interactions between the system and the controller in different ways. The interconnection-based approach considers the worst-case evolution of each coordinate separately by substituting the neural network inclusion function into the open-loop inclusion function. The interaction-based approach uses novel Jacobian-based inclusion functions to capture the first-order interactions between the open-loop system and the controller by leveraging state-of-the-art neural network verifiers. Finally, we implement our approach in a Python framework called ReachMM to demonstrate its efficiency and scalability on benchmarks and examples ranging to $200$ state dimensions.
The Variable Detection of Atmospheric Escape around the Young, Hot Neptune AU Mic b

Photoevaporation is a potential explanation for several features within exoplanet demographics. Atmospheric escape observed in young Neptune-sized exoplanets can provide insight into and characterize which mechanisms drive this evolution and at what times they dominate. AU Mic b is one such exoplanet, slightly larger than Neptune (4.19 R ⊕). It closely orbits a 23 Myr pre-main-sequence M dwarf with an orbital period of 8.46 days. We obtained two visits of AU Mic b at Lyα with Hubble Space Telescope (HST)/Space Telescope Imaging Spectrograph. One flare within the first HST visit is characterized and removed from our search for a planetary transit. We present a nondetection in our first visit, followed by the detection of escaping neutral hydrogen ahead of the planet in our second visit. The outflow absorbed ∼30% of the star’s Lyα blue wing 2.5 hr before the planet’s white-light transit. We estimate that the highest-velocity escaping material has a column density of 1013.96 cm−2 and is moving 61.26 km s−1 away from the host star. AU Mic b’s large high-energy irradiation could photoionize its escaping neutral hydrogen in 44 minutes, rendering it temporarily unobservable. Our time-variable Lyα transit ahead of AU Mic b could also be explained by an intermediate stellar wind strength from AU Mic that shapes the escaping material into a leading tail. Future Lyα observations of this system will confirm and characterize the unique variable nature of its Lyα transit, which, combined with modeling, will tune the importance of stellar wind and photoionization.
Real-Time Dynamic Atomic Spectroscopy Using Electro-Optic Frequency Combs

Spectroscopy is a key technology for both fundamental and applied science. A long-held desire has been the development of a means to continuously acquire broadband spectral data with simultaneous high time and frequency resolution. Frequency-comb technology can open this door: here, we use a spectroscopic technique based on an electro-optic comb to make continuous observations of cesium vapor across a 3.2-GHz spectral bandwidth with a 2-μs time resolution and with 10-MHz frequency sampling. We use a rapidly switched pump laser to burn narrow features into the spectral line and study the response to this step perturbation. This examination allows us to see a number of unexpected effects, including the temporal evolution of the bandwidth, the amplitude, and the frequency of these burnt features. We also report on the previously unobserved effect of radiation reabsorption, which slowly produces a broad pedestal of perturbation around each feature. We present models that can explain these dynamical effects.
Fixed Integral Neural Networks

It is often useful to perform integration over learned functions represented by neural networks. However, this integration is usually performed numerically, as analytical integration over learned functions (especially neural networks) is generally viewed as intractable. In this work, we present a method for representing the analytical integral of a learned function $f$. This allows the exact integral of a neural network to be computed, and enables constrained neural networks to be parametrised by applying constraints directly to the integral. Crucially, we also introduce a method to constrain $f$ to be positive, a necessary condition for many applications (e.g. probability distributions, distance metrics, etc). Finally, we introduce several applications where our fixed-integral neural network (FINN) can be utilised.
Function Value Learning: Adaptive Learning Rates Based on the Polyak Stepsize and Function Splitting in ERM

Here we develop variants of SGD (stochastic gradient descent) with an adaptive step size that make use of the sampled loss values. In particular, we focus on solving a finite sum-of-terms problem, also known as empirical risk minimization. We first detail an idealized adaptive method called $\texttt{SPS}_+$ that makes use of the sampled loss values and assumes knowledge of the sampled loss at optimality. This $\texttt{SPS}_+$ is a minor modification of the SPS (Stochastic Polyak Stepsize) method, where the step size is enforced to be positive. We then show that $\texttt{SPS}_+$ achieves the best known rates of convergence for SGD in the Lipschitz non-smooth. We then move onto to develop $\texttt{FUVAL}$, a variant of $\texttt{SPS}_+$ where the loss values at optimality are gradually learned, as opposed to being given. We give three viewpoints of $\texttt{FUVAL}$, as a projection based method, as a variant of the prox-linear method, and then as a particular online SGD method. We then present a convergence analysis of $\texttt{FUVAL}$ and experimental results. The shortcomings of our work is that the convergence analysis of $\texttt{FUVAL}$ shows no advantage over SGD. Another shortcomming is that currently only the full batch version of $\texttt{FUVAL}$ shows a minor advantages of GD (Gradient Descent) in terms of sensitivity to the step size. The stochastic version shows no clear advantage over SGD. We conjecture that large mini-batches are required to make $\texttt{FUVAL}$ competitive. Currently the new $\texttt{FUVAL}$ method studied in this paper does not offer any clear theoretical or practical advantage. We have chosen to make this draft available online nonetheless because of some of the analysis techniques we use, such as the non-smooth analysis of $\texttt{SPS}_+$, and also to show an apparently interesting approach that currently does not work.
Wait-Free Updates and Range Search using Uruv

CRUD operations, along with range queries make a highly useful abstract data type (ADT), employed by many dynamic analytics tasks. Despite its wide applications, to our knowledge, no fully wait-free data structure is known to support this ADT. In this paper, we introduce Uruv, a proactive linearizable and practical wait-free concurrent data structure that implements the ADT mentioned above. Structurally, Uruv installs a balanced search index on the nodes of a linked list. Uruv is the first wait-free and proactive solution for concurrent B+tree. Experiments show that Uruv significantly outperforms previously proposed lock-free B+trees for dictionary operations and a recently proposed lock-free method to implement the ADT mentioned above.