Miracle-dz/UniFAR-FTUs huggingface dataset:
Minority electron transport in InP/InGaAs heterojunction bipolar transistors

Electron transport across the base of InP/InGaAs heterojunction bipolar transistors is examined by Monte Carlo simulation. The base transit times and electron distribution functions are examined as a function of basewidth. Clear ballistic behavior is observed only for extremely thin bases (much less than 100 A). Over the range of basewidths of interest for devices, base transport appears diffusive, but the electrons are very far from thermal equilibrium. The diffusive behavior is shown to arise from the sensitivity of the steady‐state carrier population to small amounts of large‐angle scattering.
Synaptic Stripping: How Pruning Can Bring Dead Neurons Back to Life

Rectified Linear Units (ReLU) are the default choice for activation functions in deep neural networks. While they demonstrate excellent empirical performance, ReLU activations can fall victim to the dead neuron problem. In these cases, the weights feeding into a neuron end up being pushed into a state where the neuron outputs zero for all inputs. Consequently, the gradient is also zero for all inputs, which means that the weights which feed into the neuron cannot update. The neuron is not able to recover from direct back propagation and model capacity is reduced as those parameters can no longer be further optimized. Inspired by a neurological process of the same name, we introduce Synaptic Stripping as a means to combat this dead neuron problem. By automatically removing problematic connections during training, we can regenerate dead neurons and significantly improve model capacity and parametric utilization. Synaptic Stripping is easy to implement and results in sparse networks that are more efficient than the dense networks they are derived from. We conduct several ablation studies to investigate these dynamics as a function of network width and depth and we conduct an exploration of Synaptic Stripping with Vision Transformers on a variety of benchmark datasets.
Divergence-Based Domain Transferability for Zero-Shot Classification

Transferring learned patterns from pretrained neural language models has been shown to significantly improve effectiveness across a variety of language-based tasks, meanwhile further tuning on intermediate tasks has been demonstrated to provide additional performance benefits, provided the intermediate task is sufficiently related to the target task. However, how to identify related tasks is an open problem, and brute-force searching effective task combinations is prohibitively expensive. Hence, the question arises, are we able to improve the effectiveness and efficiency of tasks with no training examples through selective fine-tuning? In this paper, we explore statistical measures that approximate the divergence between domain representations as a means to estimate whether tuning using one task pair will exhibit performance benefits over tuning another. This estimation can then be used to reduce the number of task pairs that need to be tested by eliminating pairs that are unlikely to provide benefits. Through experimentation over 58 tasks and over 6,600 task pair combinations, we demonstrate that statistical measures can distinguish effective task pairs, and the resulting estimates can reduce end-to-end runtime by up to 40%.
Efficient and Compact Spreadsheet Formula Graphs

Spreadsheets are one of the most popular data analysis tools, wherein users can express computation as formulae alongside data. The ensuing dependencies are tracked as formula graphs. Efficiently querying and maintaining these formula graphs is critical for interactivity across multiple settings. Unfortunately, formula graphs are often large and complex such that querying and maintaining them is time-consuming, reducing interactivity. We propose TACO, a framework for efficiently compressing formula graphs, thereby reducing the time for querying and maintenance. The efficiency of TACO stems from a key spreadsheet property: tabular locality, which means that cells close to each other are likely to have similar formula structures. We leverage four such tabular locality-based patterns, and develop algorithms for compressing formula graphs using these patterns, directly querying the compressed graph without decompression, and incrementally maintaining the graph during updates. We integrate TACO into an open-source spreadsheet system and show that TACO can significantly reduce formula graph sizes. For querying formula graphs, the speedups of TACO over a baseline implemented in our framework and a commercial spreadsheet system are up to 34,972 × and 632 ×, respectively.
Kernel Ridge Regression Inference

We provide uniform inference and confidence bands for kernel ridge regression (KRR), a widely-used non-parametric regression estimator for general data types including rankings, images, and graphs. Despite the prevalence of these data -- e.g., ranked preference lists in school assignment -- the inferential theory of KRR is not fully known, limiting its role in economics and other scientific domains. We construct sharp, uniform confidence sets for KRR, which shrink at nearly the minimax rate, for general regressors. To conduct inference, we develop an efficient bootstrap procedure that uses symmetrization to cancel bias and limit computational overhead. To justify the procedure, we derive finite-sample, uniform Gaussian and bootstrap couplings for partial sums in a reproducing kernel Hilbert space (RKHS). These imply strong approximation for empirical processes indexed by the RKHS unit ball with logarithmic dependence on the covering number. Simulations verify coverage. We use our procedure to construct a novel test for match effects in school assignment, an important question in education economics with consequences for school choice reforms.
A Human-Centered Review of Algorithms in Decision-Making in Higher Education

The use of algorithms for decision-making in higher education is steadily growing, promising cost-savings to institutions and personalized service for students but also raising ethical challenges around surveillance, fairness, and interpretation of data. To address the lack of systematic understanding of how these algorithms are currently designed, we reviewed an extensive corpus of papers proposing algorithms for decision-making in higher education. We categorized them based on input data, computational method, and target outcome, and then investigated the interrelations of these factors with the application of human-centered lenses: theoretical, participatory, or speculative design. We found that the models are trending towards deep learning, and increased use of student personal data and protected attributes, with the target scope expanding towards automated decisions. However, despite the associated decrease in interpretability and explainability, current development predominantly fails to incorporate human-centered lenses. We discuss the challenges with these trends and advocate for a human-centered approach.
CANDIDATE TRIPLE-STAR SYSTEM WITH ELLIPSOIDAL COMPONENTS DETECTED IN VULPECULA THROUGH TESS PHOTOMETRY

MaGiV-1 is a candidate triple ellipsoidal star system in Vulpecula at coordinates RA(J2000) 19 : 52 : 19.13 and DEC(J2000) +23 : 29 : 59.7 classified as ELL+ELL, number 2344411 in AAVSO VSX database. Through photometry from the TESS Space Telescope, two significant periods describing the orbital times of the components were identified using the Fourier transform. The analysis led to determining PA-BC = (4.269 � 0.213) d the orbital period of the A-BC pair, the primary component with the secondary component described by another pair, and PBC = (0.610 � 0.031) d the orbital period of the BC pair, the inner ellipsoidal system. However, it cannot be completely ruled out that the shorter period can be explained by pulsations of one of the two components (e.g. by the GDOR type).
HGMatch: A Match-by-Hyperedge Framework for Efﬁcient and Parallel Subhypergraph Matching (Complete Version)

—Hypergraphs are generalisation of graphs in which a hyperedge can connect any number of vertices. It can describe n-ary relationships and high-order information among entities compared to conventional graphs. In this paper, we study the fundamental problem of subhypergraph matching in hyper-graphs. Existing methods directly extend subgraph matching algorithms to the case of hypergraphs. However, this approach delays hyperedge veriﬁcation and underutilises the high-order information in hypergraphs, which leads to large search space and high enumeration cost. Furthermore, with the growing size of hypergraphs, it is becoming hard to compute subhypergraph matching sequentially. Thus, we propose an efﬁcient and parallel subhypergraph matching system, HGMatch , to handle subhyper-graph matching in massive hypergraphs. We proposes a novel match-by-hyperedge framework to utilise high-order information in hypergraphs and uses set operations for efﬁcient candidates generation. Moreover, we develop an optimised parallel execution engine in HGMatch based on the dataﬂow model, which features a task-based scheduler and ﬁne-grained dynamic work stealing to achieve bounded memory execution and better load balancing. Experimental evaluation on 10 real-world datasets shows that HGMatch outperforms the extended version of the state-of-the-art subgraph matching algorithms ( CFL , DAF , CECI , and RapidMatch ) by orders of magnitude when using a single thread, and achieves almost linear scalability when the number of threads increases.
3D Colored Shape Reconstruction from a Single RGB Image through Diffusion

We propose a novel 3d colored shape reconstruction method from a single RGB image through diffusion model. Diffusion models have shown great development potentials for high-quality 3D shape generation. However, most existing work based on diffusion models only focus on geometric shape generation, they cannot either accomplish 3D reconstruction from a single image, or produce 3D geometric shape with color information. In this work, we propose to reconstruct a 3D colored shape from a single RGB image through a novel conditional diffusion model. The reverse process of the proposed diffusion model is consisted of three modules, shape prediction module, color prediction module and NeRF-like rendering module. In shape prediction module, the reference RGB image is first encoded into a high-level shape feature and then the shape feature is utilized as a condition to predict the reverse geometric noise in diffusion model. Then the color of each 3D point updated in shape prediction module is predicted by color prediction module. Finally, a NeRF-like rendering module is designed to render the colored point cloud predicted by the former two modules to 2D image space to guide the training conditioned only on a reference image. As far as the authors know, the proposed method is the first diffusion model for 3D colored shape reconstruction from a single RGB image. Experimental results demonstrate that the proposed method achieves competitive performance on colored 3D shape reconstruction, and the ablation study validates the positive role of the color prediction module in improving the reconstruction quality of 3D geometric point cloud.
Probing modified plasma waves in non-linear electrodynamics

Properties of modified plasma waves in non-linear electrodynamics are investigated. We consider a cold, uniform, collisionless, and magnetized plasma model. Initially, we also assume small amplitude waves and the non-relativistic approximation. For electrostatic waves, we obtain a modified Trivelpiece–Gould dispersion relation with a suitable change in the plasma frequency and analyze the stability of modes. Furthermore, electromagnetic waves related to the generalized Appleton–Hartree equation are established. In this case, we discuss modifications in circularly polarized waves and ordinary and extraordinary modes. After that, we apply our results to particular cases of low-energy quantum electrodynamics and a generalized Born–Infeld model. The correspondent dispersion relations and effects on the propagation regions are determined. Finally, we include the relativistic and large amplitude effects for circularly polarized waves. We obtain the dispersion relation within effective non-linear electrodynamics and examine the behavior of the refractive index when the frequency of the propagating wave converges to the plasma frequency.
A Kurdyka-Lojasiewicz property for stochastic optimization algorithms in a non-convex setting

Stochastic differentiable approximation schemes are widely used for solving high dimensional problems. Most of existing methods satisfy some desirable properties, including conditional descent inequalities, and almost sure (a.s.) convergence guarantees on the objective function, or on the involved gradient. However, for non-convex objective functions, a.s. convergence of the iterates, i.e., the stochastic process, to a critical point is usually not guaranteed, and remains an important challenge. In this article, we develop a framework to bridge the gap between descent-type inequalities and a.s. convergence of the associated stochastic process. Leveraging a novel Kurdyka-Lojasiewicz property, we show convergence guarantees of stochastic processes under mild assumptions on the objective function. We also provide examples of stochastic algorithms benefiting from the proposed framework and derive a.s. convergence guarantees on the iterates.
Deep-Learning Quantitative Structural Characterization in Additive Manufacturing

With a goal of accelerating fabrication of additively manufactured components with precise microstructures, we developed a method for structural characterization of key features in additively manufactured materials and parts. The method utilizes deep learning based on an image-to-image translation conditional Generative Adversarial Neural Network architecture and enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing induced defects visible in etched optical images. These structural details are heterogeneous in nature. Our method specifies the microstructure state of an additive built via statistical distribution of structural details, based on an ensemble of collected images. Extensions of the method are proposed to address Artificial Intelligence implementation of developed machine learning model for in real time control of additive manufacturing.
AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework

Logic synthesis is one of the most important steps in design and implementation of digital chips with a big impact on final Quality of Results (QoR). For a most general input circuit modeled by a Directed Acyclic Graph (DAG), many logic synthesis problems such as delay or area minimization are NP-Complete, hence, no optimal solution is available. This is why many classical logic optimization functions tend to follow greedy approaches that are easily trapped in local minima that does not allow improving QoR as much as needed. We believe that Artificial Intelligence (AI) and more specifically Reinforcement Learning (RL) algorithms can help in solving this problem. This is because AI and RL can help minimizing QoR further by exiting from local minima. Our experiments on both open source and industrial benchmark circuits show that significant improvements on important metrics such as area, delay, and power can be achieved by making logic synthesis optimization functions AI-driven. For example, our RL-based rewriting algorithm could improve total cell area post-synthesis by up to 69.3% when compared to a classical rewriting algorithm with no AI awareness.
Exploring High Tower Base Stations with Multi-User Massive MIMO for Rural Connectivity

The digital divide is a key issue worldwide. Almost 3 billion people, mainly in rural areas, are still not connected. In this paper, we explore the capability of high towers base station (HTBS) with massive multiple input multiple output (mMIMO) in offering low-cost rural connectivity. We previously showed the benefits of HTBS in the downlink. We focus in this work on the uplink (UL) where we compute the UL data rate per user for different values of transmit effective isotropic radiated power (EIRP). Our results show that the HTBS solution is viable as relatively good user UL rates are achieved with reasonable EIRPs. This is of high interest for covering rural areas, characterized by low population densities and a low number of active users, as the coverage is their main constraint, rather than the capacity as in urban areas. Techno-economical aspects such as the recommended frequency for HTBS, the number of covered persons, the average population density of covered rural areas, and potential low-cost locations for HTBS are provided. Non-technological challenges for the HTBS solution are also discussed.
On the Difficulty of Characterizing Network Formation with Endogenous Behavior

Bolletta (2021, Math. Soc. Sci. 114:1-10) studies a model in which a network is strategically formed and then agents play a linear best-response investment game in it. The model is motivated by an application in which people choose both their study partners and their levels of educational effort. Agents have different one-dimensional types $\unicode{x2013}$ private returns to effort. A main result claims that pairwise Nash stable networks have a locally complete structure consisting of possibly overlapping cliques: if two agents are linked, they are part of a clique composed of all agents with types between theirs. We offer a counterexample showing that the claimed characterization is incorrect, highlight where the analysis errs, and discuss implications for network formation models.
Towards a Field-Theory based Relativistic Quantum Information

We present our program for the development of quantum informational concepts in relativistic systems in terms of the unequal-time correlation functions of quantum fields. We employ two formalisms that provide the basis for further developments. (i) The Quantum Temporal Probabilities (QTP) Method for quantum field measurements and (ii) the Closed-Time-Path (CTP) formalism for causal time evolutions. We present the main ideas of QTP and we show how it relates to the CTP formalism, allowing us to express concepts of measurement theory in terms of path-integrals. We also present many links of our program to non-equilibrium quantum field theories. Details can be found in a recent paper by the authors [1].
Imaging the Inner Astronomical Unit of the Herbig Be Star HD 190073

The inner regions of protoplanetary disks host many complex physical processes such as star–disk interactions, magnetic fields, planet formation, and the migration of new planets. To study directly this region requires milliarcsecond angular resolution, beyond the diffraction limit of the world's largest optical telescopes and even too small for the millimeter-wave interferometer Atacama Large Millimeter/submillimeter Array (ALMA). However, we can use infrared interferometers to image the inner astronomical unit. Here, we present new results from the CHARA and VLTI arrays for the young and luminous Herbig Be star HD 190073. We detect a sub-astronomical unit (sub-AU) cavity surrounded by a ring-like structure that we interpret as the dust destruction front. We model the shape with six radial profiles, three symmetric and three asymmetric, and present a model-free image reconstruction. All the models are consistent with a near face-on disk with an inclination ≲20°, and we measure an average ring radius of 1.4 ± 0.2 mas (1.14 au). Around 48% of the total flux comes from the disk with 15% of that emission appearing to emerge from inside the inner rim. The cause of emission is still unclear, perhaps due to different dust grain compositions or gas emission. The skewed models and the imaging point to an off-center star, possibly due to binarity. Our image shows sub-AU structure, which seems to move between the two epochs inconsistently with Keplerian motion and we discuss possible explanations for this apparent change.
Bregman-Wasserstein divergence: geometry and applications

Consider the Monge-Kantorovich optimal transport problem where the cost function is given by a Bregman divergence. The associated transport cost, which we call the Bregman-Wasserstein divergence, presents a natural asymmetric extension of the squared $2$-Wasserstein metric and has recently found applications in statistics and machine learning. On the other hand, Bregman divergence is a fundamental object in information geometry and induces a dually flat geometry on the underlying manifold. Using the Bregman-Wasserstein divergence, we lift this dualistic geometry to the space of probability measures, thus extending Otto's weak Riemannian structure of the Wasserstein space to statistical manifolds. We do so by generalizing Lott's formal geometric computations on the Wasserstein space. In particular, we define primal and dual connections on the space of probability measures and show that they are conjugate with respect to Otto's metric. We also define primal and dual displacement interpolations which satisfy the corresponding geodesic equations. As applications, we study displacement convexity and the Bregman-Wasserstein barycenter.
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals

We present a continuous-time probabilistic approach for estimating the chirp signal and its instantaneous frequency function when the true forms of these functions are not accessible. Our model represents these functions by non-linearly cascaded Gaussian processes represented as non-linear stochastic differential equations. The posterior distribution of the functions is then estimated with stochastic filters and smoothers. We compute a (posterior) Cramér–Rao lower bound for the Gaussian process model, and derive a theoretical upper bound for the estimation error in the mean squared sense. The experiments show that the proposed method outperforms a number of state-of-the-art methods on a synthetic data. We also show that the method works out-of-the-box for two real-world datasets.
Predicting Participants' Performance in Programming Contests using Deep Learning Techniques

In recent days, the number of technology enthusiasts is increasing day by day with the prevalence of technological products and easy access to the internet. Similarly, the amount of people working behind this rapid development is rising tremendously. Computer programmers consist of a large portion of those tech-savvy people. Codeforces, an online programming and contest hosting platform used by many competitive programmers worldwide. It is regarded as one of the most standardized platforms for practicing programming problems and participate in programming contests. In this research, we propose a framework that predicts the performance of any particular contestant in the upcoming competitions as well as predicts the rating after that contest based on their practice and the performance of their previous contests.
Robust Scheduling with GFlowNets

Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization. However, evaluating the goodness of a schedule on the target hardware can be very time-consuming. Traditional approaches as well as previous machine learning ones typically optimize proxy metrics, which are fast to evaluate but can lead to bad schedules when tested on the target hardware. In this work, we propose a new approach to scheduling by sampling proportionally to the proxy metric using a novel GFlowNet method. We introduce a technique to control the trade-off between diversity and goodness of the proposed schedules at inference time and demonstrate empirically that the pure optimization baselines can lead to subpar performance with respect to our approach when tested on a target model. Furthermore, we show that conditioning the GFlowNet on the computation graph enables generalization to unseen scheduling problems for both synthetic and real-world compiler datasets.