Miracle-dz/UniFAR-FTUs huggingface dataset:
The non-Landauer Bound for the Dissipation of Bit Writing Operation

We propose a novel bound on the min-imum dissipation required in any circumstances to transfer a certain amount of charge through any re-sistive device. We illustrate it on the task of writing a logical 1 (encoded as a prescribed voltage) into a capacitance, through various linear or nonlinear devices. We show that, even though the celebrated Landauer bound (which only applies to bit erasure) does not apply here, one can still formulate a “non-Landauer” lower bound on dissipation, that crucially depends on the time budget to perform the operation, as well as the average conductance of the driving device. We compare our bound with empirical results reported in the literature and realistic simulations of CMOS pass and transmission gates in decananometer technology. Our non-Landauer bound turns out to be a quantitative benchmark to assess the (non-)optimality of a writing operation.
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient

Restricting the variance of a policy's return is a popular choice in risk-averse Reinforcement Learning (RL) due to its clear mathematical definition and easy interpretability. Traditional methods directly restrict the total return variance. Recent methods restrict the per-step reward variance as a proxy. We thoroughly examine the limitations of these variance-based methods, such as sensitivity to numerical scale and hindering of policy learning, and propose to use an alternative risk measure, Gini deviation, as a substitute. We study various properties of this new risk measure and derive a policy gradient algorithm to minimize it. Empirical evaluation in domains where risk-aversion can be clearly defined, shows that our algorithm can mitigate the limitations of variance-based risk measures and achieves high return with low risk in terms of variance and Gini deviation when others fail to learn a reasonable policy.
Soundly Handling Linearity

We propose a novel approach to soundly combining linear types with multi-shot effect handlers. circear type systems statically ensure that resources such as file handles and communication channels are used exactly once. Effect handlers provide a rich modular programming abstraction for implementing features ranging from exceptions to concurrency to backtracking. Whereas conventional linear type systems bake in the assumption that continuations are invoked exactly once, effect handlers allow continuations to be discarded (e.g. for exceptions) or invoked more than once (e.g. for backtracking). This mismatch leads to soundness bugs in existing systems such as the programming language Links, which combines linearity (for session types) with effect handlers. We introduce control-flow linearity as a means to ensure that continuations are used in accordance with the linearity of any resources they capture, ruling out such soundness bugs. We formalise the notion of control-flow linearity in a System F-style core calculus Feff∘ equipped with linear types, an effect type system, and effect handlers. We define a linearity-aware semantics in order to formally prove that Feff∘ preserves the integrity of linear values in the sense that no linear value is discarded or duplicated. In order to show that control-flow linearity can be made practical, we adapt circks based on the design of Feff∘, in doing so fixing a long-standing soundness bug. Finally, to better expose the potential of control-flow linearity, we define an ML-style core calculus Qeff∘, based on qualified types, which requires no programmer provided annotations, and instead relies entirely on type inference to infer control-flow linearity. Both linearity and effects are captured by qualified types. Qeff∘ overcomes a number of practical limitations of Feff∘, supporting abstraction over linearity, linearity dependencies between type variables, and a much more fine-grained notion of control-flow linearity.
Deterministic Objective Bayesian Analysis for Spatial Models

Berger et al. (2001) and Ren et al. (2012) derived noninformative priors for Gaussian process models of spatially correlated data using the reference prior approach (Berger, Bernardo, 1991). The priors have good statistical properties and provide a basis for objective Bayesian analysis (Berger, 2006). Using a trust-region algorithm for optimization with exact equations for posterior derivatives and an adaptive sparse grid at Chebyshev nodes, this paper develops deterministic algorithms for fully Bayesian prediction and inference with the priors. Implementations of the algorithms are available at https://github.com/rnburn/bbai.
EgoVM: Achieving Precise Ego-Localization using Lightweight Vectorized Maps

Accurate and reliable ego-localization is critical for autonomous driving. In this paper, we present EgoVM, an end-to-end localization network that achieves comparable localization accuracy to prior state-of-the-art methods, but uses lightweight vectorized maps instead of heavy point-based maps. To begin with, we extract BEV features from online multi-view images and LiDAR point cloud. Then, we employ a set of learnable semantic embeddings to encode the semantic types of map elements and supervise them with semantic segmentation, to make their feature representation consistent with BEV features. After that, we feed map queries, composed of learnable semantic embeddings and coordinates of map elements, into a transformer decoder to perform cross-modality matching with BEV features. Finally, we adopt a robust histogram-based pose solver to estimate the optimal pose by searching exhaustively over candidate poses. We comprehensively validate the effectiveness of our method using both the nuScenes dataset and a newly collected dataset. The experimental results show that our method achieves centimeter-level localization accuracy, and outperforms existing methods using vectorized maps by a large margin. Furthermore, our model has been extensively tested in a large fleet of autonomous vehicles under various challenging urban scenes.
Inclusion of organ motion in IMRT optimization using probabilistic treatment planning

Die vorliegende Arbeit beschreibt ein Verfahren zur Berucksichtigung von Organbewegungen in der Bestrahlungsplanung fur die fraktionierte intensitatsmodulierte Strahlentherapie (IMRT). Organbewegungen werden durch ein mathematisches Modell beschrieben welches die Grundlage der Bestrahlungsplan-Optimierung darstellt. Das Modell enthalt Zufallsvariablen um die stochastische Natur von Organbewegungen zu beschreiben. Die vorausgesagte Dosisverteilung im Patienten muss daher ebenfalls als Zufallsvariable aufgefasst werden und wird durch einen Erwartungswert der Dosis und dessen Varianz charakterisiert. Zur Optimierung des Bestrahlungsplans unter Berucksichtigung des Bewegungsmodells wird der Erwartungswert einer quadratischen Kostenfunktion minimiert, der sich als Summe der Dosisvarianz und der quadratischen Differenz von Solldosis und Erwartungswert der Dosis darstellen lasst. Die daraus resultierenden Bestrahlungsplane haben die Eigenschaft, dass Bereiche in denen Tumorgewebe nur relativ selten lokalisiert ist mit einer geringeren Dosis bestrahlt werden. Dies wird ausgeglichen durch eine Dosisuberhohung in benachbarten Bereichen, so dass sich durch den Einfluss der Bewegung im Verlauf der gesamten Therapie eine naherungsweise homogene Gesamtdosisverteilung im Tumor ergibt. Das Verfahren erlaubt eine potentiell bessere Schonung von angrenzenden gesunden Geweben im Vergleich zur Sicherheitsrand-Methode. %%%%The presented thesis describes an off-line approach to incorporate organ motion into the treatment plan optimization for fractionated intensity modulated radiotherapy (IMRT). Organ movement is described in terms of a mathematical model that represents the basis of the treatment plan optimization process. The motion model contains random variables in order to describe the stochastic nature of organ movements. As a consequence, the predicted dose distribution in the patient must be considered as a random variable as well. It is characterized by the expectation value and the variance of the dose. For treatment plan optimization incorporating the motion model, the expectation value of a quadratic cost function is minimized, which can be expressed as the sum of the variance of the dose and the quadratic difference of expected and prescribed dose. The resulting treatment plans show a reduction of the dose in regions where tumor tissue is only rarely present. This is compensated for by delivering a higher dose to neighboring regions that are mostly occupied by tumor tissue. Due to organ movement during the course of treatment, a widely homogeneous cumulative dose distribution is delivered to the tumor. This method, compared to the standard safety margin approach, potentially allows for a better sparing of healthy tissues from dose burden.
Discretization-based ensemble model for robust learning in IoT

IoT device identification is the process of recognizing and verifying connected IoT devices to the network. This is an essential process for ensuring that only authorized devices can access the network, and it is necessary for network management and maintenance. In recent years, machine learning models have been used widely for automating the process of identifying devices in the network. However, these models are vulnerable to adversarial attacks that can compromise their accuracy and effectiveness. To better secure device identification models, discretization techniques enable reduction in the sensitivity of machine learning models to adversarial attacks contributing to the stability and reliability of the model. On the other hand, Ensemble methods combine multiple heterogeneous models to reduce the impact of remaining noise or errors in the model. Therefore, in this paper, we integrate discretization techniques and ensemble methods and examine it on model robustness against adversarial attacks. In other words, we propose a discretization-based ensemble stacking technique to improve the security of our ML models. We evaluate the performance of different ML-based IoT device identification models against white box and black box attacks using a real-world dataset comprised of network traffic from 28 IoT devices. We demonstrate that the proposed method enables robustness to the models for IoT device identification.
Polynomial Time and Dependent Types

We combine dependent types with linear type systems that soundly and completely capture polynomial time computation. We explore two systems for capturing polynomial time: one system that disallows construction of iterable data, and one, based on the LFPL system of Martin Hofmann, that controls construction via a payment method. Both of these are extended to full dependent types via Quantitative Type Theory, allowing for arbitrary computation in types alongside guaranteed polynomial time computation in terms. We prove the soundness of the systems using a realisability technique due to Dal Lago and Hofmann. Our long-term goal is to combine the extensional reasoning of type theory with intensional reasoning about the resources intrinsically consumed by programs. This paper is a step along this path, which we hope will lead both to practical systems for reasoning about programs’ resource usage, and to theoretical use as a form of synthetic computational complexity theory.
Forecasting the steam mass flow in a powerplant using the parallel hybrid network

Efficient and sustainable power generation is a crucial concern in the energy sector. In particular, thermal power plants grapple with accurately predicting steam mass flow, which is crucial for operational efficiency and cost reduction. In this study, we use a parallel hybrid neural network architecture that combines a parametrized quantum circuit and a conventional feed-forward neural network specifically designed for time-series prediction in industrial settings to enhance predictions of steam mass flow 15 minutes into the future. Our results show that the parallel hybrid model outperforms standalone classical and quantum models, achieving more than 5.7 and 4.9 times lower mean squared error (MSE) loss on the test set after training compared to pure classical and pure quantum networks, respectively. Furthermore, the hybrid model demonstrates smaller relative errors between the ground truth and the model predictions on the test set, up to 2 times better than the pure classical model. These findings contribute to the broader scientific understanding of how integrating quantum and classical machine learning techniques can be applied to real-world challenges faced by the energy sector, ultimately leading to optimized power plant operations.
Multi-Player Zero-Sum Markov Games with Networked Separable Interactions

We study a new class of Markov games, \emph(multi-player) zero-sum Markov Games} with \emph{Networked separable interactions} (zero-sum NMGs), to model the local interaction structure in non-cooperative multi-agent sequential decision-making. We define a zero-sum NMG as a model where {the payoffs of the auxiliary games associated with each state are zero-sum and} have some separable (i.e., polymatrix) structure across the neighbors over some interaction network. We first identify the necessary and sufficient conditions under which an MG can be presented as a zero-sum NMG, and show that the set of Markov coarse correlated equilibrium (CCE) collapses to the set of Markov Nash equilibrium (NE) in these games, in that the product of per-state marginalization of the former for all players yields the latter. Furthermore, we show that finding approximate Markov \emph{stationary} CCE in infinite-horizon discounted zero-sum NMGs is \texttt{PPAD}-hard, unless the underlying network has a ``star topology''. Then, we propose fictitious-play-type dynamics, the classical learning dynamics in normal-form games, for zero-sum NMGs, and establish convergence guarantees to Markov stationary NE under a star-shaped network structure. Finally, in light of the hardness result, we focus on computing a Markov \emph{non-stationary} NE and provide finite-iteration guarantees for a series of value-iteration-based algorithms. We also provide numerical experiments to corroborate our theoretical results.
Imposing Consistency Properties on Blackbox Systems with Applications to SVD-Based Recommender Systems

In this paper we discuss pre- and post-processing methods to induce desired consistency and/or invariance properties in blackbox systems, e.g., AI-based. We demonstrate our approach in the context of blackbox SVD-based matrix-completion methods commonly used in recommender system (RS) applications. We provide empirical results showing that enforcement of unit-consistency and shift-consistency, which have provable RS-relevant properties relating to robustness and fairness, also lead to improved performance according to generic RMSE and MAE performance metrics, irrespective of the initial chosen hyperparameter.
Cut Sparsification and Succinct Representation of Submodular Hypergraphs

In cut sparsification, all cuts of a hypergraph $H=(V,E,w)$ are approximated within $1\pm\epsilon$ factor by a small hypergraph $H'$. This widely applied method was generalized recently to a setting where the cost of cutting each hyperedge $e$ is provided by a splitting function $g_e: 2^e\to\mathbb{R}_+$. This generalization is called a submodular hypergraph when the functions $\{g_e\}_{e\in E}$ are submodular, and it arises in machine learning, combinatorial optimization, and algorithmic game theory. Previous work studied the setting where $H'$ is a reweighted sub-hypergraph of $H$, and measured the size of $H'$ by the number of hyperedges in it. In this setting, we present two results: (i) all submodular hypergraphs admit sparsifiers of size polynomial in $n=|V|$ and $\epsilon^{-1}$; (ii) we propose a new parameter, called spread, and use it to obtain smaller sparsifiers in some cases. We also show that for a natural family of splitting functions, relaxing the requirement that $H'$ be a reweighted sub-hypergraph of $H$ yields a substantially smaller encoding of the cuts of $H$ (almost a factor $n$ in the number of bits). This is in contrast to graphs, where the most succinct representation is attained by reweighted subgraphs. A new tool in our construction of succinct representation is the notion of deformation, where a splitting function $g_e$ is decomposed into a sum of functions of small description, and we provide upper and lower bounds for deformation of common splitting functions.
Optimal Vehicle Trajectory Planning for Static Obstacle Avoidance using Nonlinear Optimization

Vehicle trajectory planning is a key component for an autonomous driving system. A practical system not only requires the component to compute a feasible trajectory, but also a comfortable one given certain comfort metrics. Nevertheless, computation efficiency is critical for the system to be deployed as a commercial product. In this paper, we present a novel trajectory planning algorithm based on nonlinear optimization. The algorithm computes a kinematically feasible and comfort-optimal trajectory that achieves collision avoidance with static obstacles. Furthermore, the algorithm is time efficient. It generates an 6-second trajectory within 10 milliseconds on an Intel i7 machine or 20 milliseconds on an Nvidia Drive Orin platform.
Fixed-Parameter Algorithms for Fair Hitting Set Problems

Selection of a group of representatives satisfying certain fairness constraints, is a commonly occurring scenario. Motivated by this, we initiate a systematic algorithmic study of a \emph{fair} version of \textsc{Hitting Set}. In the classical \textsc{Hitting Set} problem, the input is a universe $\mathcal{U}$, a family $\mathcal{F}$ of subsets of $\mathcal{U}$, and a non-negative integer $k$. The goal is to determine whether there exists a subset $S \subseteq \mathcal{U}$ of size $k$ that \emph{hits} (i.e., intersects) every set in $\mathcal{F}$. Inspired by several recent works, we formulate a fair version of this problem, as follows. The input additionally contains a family $\mathcal{B}$ of subsets of $\mathcal{U}$, where each subset in $\mathcal{B}$ can be thought of as the group of elements of the same \emph{type}. We want to find a set $S \subseteq \mathcal{U}$ of size $k$ that (i) hits all sets of $\mathcal{F}$, and (ii) does not contain \emph{too many} elements of each type. We call this problem \textsc{Fair Hitting Set}, and chart out its tractability boundary from both classical as well as multivariate perspective. Our results use a multitude of techniques from parameterized complexity including classical to advanced tools, such as, methods of representative sets for matroids, FO model checking, and a generalization of best known kernels for \textsc{Hitting Set}.
Emotional Intelligence of Large Language Models

Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and complex reasoning. However, their alignment with human emotions and values, which is critical for real-world applications, has not been systematically evaluated. Here, we assessed LLMs' Emotional Intelligence (EI), encompassing emotion recognition, interpretation, and understanding, which is necessary for effective communication and social interactions. Specifically, we first developed a novel psychometric assessment focusing on Emotion Understanding (EU), a core component of EI. This test is an objective, performance-driven, and text-based evaluation, which requires evaluating complex emotions in realistic scenarios, providing a consistent assessment for both human and LLM capabilities. With a reference frame constructed from over 500 adults, we tested a variety of mainstream LLMs. Most achieved above-average Emotional Quotient (EQ) scores, with GPT-4 exceeding 89% of human participants with an EQ of 117. Interestingly, a multivariate pattern analysis revealed that some LLMs apparently did not rely on the human-like mechanism to achieve human-level performance, as their representational patterns were qualitatively distinct from humans. In addition, we discussed the impact of factors such as model size, training method, and architecture on LLMs' EQ. In summary, our study presents one of the first psychometric evaluations of the human-like characteristics of LLMs, which may shed light on the future development of LLMs aiming for both high intellectual and emotional intelligence. Project website: https://emotional-intelligence.github. io/
A Noninterpolated Estimate of Horizontal Spatial Covariance from Nonorthogonally and Irregularly Sampled Scalar Velocities

AbstractThis paper presents a least squares method to estimate the horizontal (isotropic or anisotropic) spatial covariance of two-dimensional orthogonal vector components, without introducing an intervening mapping step and biases, from the spatial covariance of the nonorthogonally and irregularly sampled raw scalar velocities. The field is assumed to be locally homogeneous in space and sampled in an ensemble so the unknown spatial covariance is a function of spatial lag only. The transformation between the irregular grid on which nonorthogonal scalar projections of the vector are sampled and the regular orthogonal grid on which they will be mapped is created using the geometry of the problem. The spatial covariance of the orthogonal velocity components of the field is parameterized by either the energy (power) spectrum in the wavenumber domain or the lagged covariance in the spatial domain. The energy spectrum is constrained to be nonnegative definite as part of the solution of the inverse problem. This.
On the detection of transitive clusters in undirected networks

ABSTRACT A network cluster is defined as a set of nodes with ‘strong’ within group ties and ‘weak’ between group ties. Most clustering methods focus on finding groups of ‘densely connected’ nodes, where the dyad (or tie between two nodes) serves as the building block for forming clusters. However, since the unweighted dyad cannot distinguish strong relationships from weak ones, it then seems reasonable to consider an alternative building block, i.e. one involving more than two nodes. In the simplest case, one can consider the triad (or three nodes), where the fully connected triad represents the basic unit of transitivity in an undirected network. In this effort we propose a clustering framework for finding highly transitive subgraphs in an undirected/unweighted network, where the fully connected triad (or triangle configuration) is used as the building block for forming clusters. We apply our methodology to four real networks with encouraging results. Monte Carlo simulation results suggest that, on average, the proposed method yields good clustering performance on synthetic benchmark graphs, relative to other popular methods.