Miracle-dz/UniFAR-FTUs huggingface dataset:
A Trade-off Analysis of Replacing Proprietary LLMs with Open Source SLMs in Production

Many companies rely on APIs of managed AI models such as OpenAI's GPT-4 to create AI-enabled experiences in their products. Along with the benefits of ease of use and shortened time to production, this reliance on proprietary APIs has downsides in terms of model control, performance reliability, up-time predictability, and cost. At the same time, there has been a flurry of open source small language models (SLMs) that have been made available for commercial use. However, their readiness to replace existing capabilities remains unclear, and a systematic approach to test these models is not readily available. In this paper, we present a systematic evaluation methodology for, and characterization of, modern open source SLMs and their trade-offs when replacing a proprietary LLM APIs for a real-world product feature. We have designed SLaM, an automated analysis tool that enables the quantitative and qualitative testing of product features utilizing arbitrary SLMs. Using SLaM, we examine both the quality and the performance characteristics of modern SLMs relative to an existing customer-facing OpenAI-based implementation. We find that across 9 SLMs and 29 variants, we observe competitive quality-of-results for our use case, significant performance consistency improvement, and a cost reduction of 5x-29x when compared to OpenAI GPT-4.
Mass and Isospin Breaking Effects in the Skyrme Model and in Holographic QCD

We discuss how the quark masses and their mass splitting affect the baryons in the Skyrme model as well as the Witten-Sakai-Sugimoto (WSS) model. In both cases baryons are described by solitonic objects, i.e. Skyrmions and instantons, respectively. After the quantization of their zeromodes the nucleons become quantum states of a rotor. We show how the quark mass affects the moment of inertia and we provide a semianalytic approach valid in the small mass limit. Additionally, we show how the two lightest quarks' mass splitting affects the moments of inertia of the Skyrmion and induces an isospin breaking effect. This effect turns out not to be enough to split the degeneracy in the neutron-proton multiplet, but it splits some of the states in the Delta multiplet. Unlike the basic Skyrme model, the WSS model already includes vector mesons and another mechanism to transfer isospin breaking from quark masses to the solitons is known. We compute the splitting of the moment of inertia in the small-mass limit in the WSS model and combine the two effects on the spectrum of baryons.
The Zeta ($\zeta$) Notation for Complex Asymptotes

Time Complexity is an important metric to compare algorithms based on their cardinality. The commonly used, trivial notations to qualify the same are the Big-Oh, Big-Omega, Big-Theta, Small-Oh, and Small-Omega Notations. All of them, consider time a part of the real entity, i.e., Time coincides with the horizontal axis in the argand plane. But what if the Time rather than completely coinciding with the real axis of the argand plane, makes some angle with it? We are trying to focus on the case when the Time Complexity will have both real and imaginary components. For Instance, if $T\left(n\right)=\ n\log{n}$, the existing asymptomatic notations are capable of handling that in real time But, if we come across a problem where, $T\left(n\right)=\ n\log{n}+i\cdot n^2$, where, $i=\sqrt[2]{-1}$, the existing asymptomatic notations will not be able to catch up. To mitigate the same, in this research, we would consider proposing the Zeta Notation ($\zeta$), which would qualify Time in both the Real and Imaginary Axis, as per the Argand Plane.
Four-dimensional Floquet topological insulator with an emergent second Chern number

Floquet topological insulators have been widely investigated in lower-dimensional systems. However, Floquet topological insulators induced by time-periodic driving in higher-dimensional systems remain unexplored. In this work, we study the effects of time-periodic driving in a four-dimensional (4D) normal insulator, focusing on topological phase transitions at the resonant quasienergy gap. We consider two types of time-periodic driving, including a time-periodic onsite potential and a time-periodic vector potential. We reveal that both types of time-periodic driving can transform the 4D normal insulator into a 4D Floquet topological insulator characterized by an emergent second Chern number. Moreover, it is found that the topological phase of the 4D system can be modulated by tuning the strength and frequency of the time-periodic driving. Our work will be helpful for the future investigation of Floquet topological insulators in higher dimensions.
Routing on Heavy Path WSPD Spanners

In this article, we present a construction of a spanner on a set of $n$ points in $\mathbf{R}^d$ that we call a heavy path WSPD spanner. The construction is parameterized by a constant $s>2$ called the separation ratio. The size of the graph is $O(s^dn)$ and the spanning ratio is at most $1 + 2/s + 2/(s - 1)$. We also show that this graph has a hop spanning ratio of at most $2\lg n + 1$. We present a memoryless local routing algorithm for heavy path WSPD spanners. The routing algorithm requires a vertex $v$ of the graph to store $O(\mathrm{deg}(v)\log n)$ bits of information, where $\mathrm{deg}(v)$ is the degree of $v$. The routing ratio is at most $1 + 4/s + 1/(s - 1)$ and at least $1 + 4/s$ in the worst case. The number of edges on the routing path is bounded by $2\lg n + 1$. We then show that the heavy path WSPD spanner can be constructed in metric spaces of bounded doubling dimension. These metric spaces have been studied in computational geometry as a generalization of Euclidean space. We show that, in a metric space with doubling dimension $\lambda$, the heavy path WSPD spanner has size $O(s^\lambda n)$ where $s$ is the separation ratio. The spanning ratio and hop spanning ratio are the same as in the Euclidean case. Finally, we show that the local routing algorithm works in the bounded doubling dimension case. The vertices require the same amount of storage, but the routing ratio becomes at most $1 + (2 + \frac{\tau}{\tau-1})/s + 1/(s - 1)$ in the worst case, where $\tau \ge 11$ is a constant related to the doubling dimension.
Hierarchical Multigrid Ansatz for Variational Quantum Algorithms

Quantum computing is an emerging topic in engineering that promises to enhance supercomputing using fundamental physics. In the near term, the best candidate algorithms for achieving this advantage are variational quantum algorithms (VQAs). We design and numerically evaluate a novel ansatz for VQAs, focusing in particular on the variational quantum eigensolver (VQE). As our ansatz is inspired by classical multigrid hierarchy methods, we call it"multigrid'' ansatz. The multigrid ansatz creates a parameterized quantum circuit for a quantum problem on $n$ qubits by successively building and optimizing circuits for smaller qubit counts $j<n$, reusing optimized parameter values as initial solutions to next level hierarchy at $j+1$. We show through numerical simulation that the multigrid ansatz outperforms the standard hardware-efficient ansatz in terms of solution quality for the Laplacian eigensolver as well as for a large class of combinatorial optimization problems with specific examples for MaxCut and Maximum $k$-Satisfiability. Our studies establish the multi-grid ansatz as a viable candidate for many VQAs and in particular present a promising alternative to the QAOA approach for combinatorial optimization problems.
Semantic Importance-Aware Based for Multi-User Communication Over MIMO Fading Channels

Semantic communication, as a novel communication paradigm, has attracted the interest of many scholars, with multi-user, multi-input multi-output (MIMO) scenarios being one of the critical contexts. This paper presents a semantic importance-aware based communication system (SIA-SC) over MIMO Rayleigh fading channels. Combining the semantic symbols' inequality and the equivalent subchannels of MIMO channels based on Singular Value Decomposition (SVD) maximizes the end-to-end semantic performance through the new layer mapping method. For multi-user scenarios, a method of semantic interference cancellation is proposed. Furthermore, a new metric, namely semantic information distortion (SID), is established to unify the expressions of semantic performance, which is affected by channel bandwidth ratio (CBR) and signal-to-noise ratio (SNR). With the help of the proposed metric, we derived performance expressions and Semantic Outage Probability (SOP) of SIA-SC for Single-User Single-Input Single-Output (SU-SISO), Single-User MIMO (SU-MIMO), Multi-Users SISO (MU-MIMO) and Multi-Users MIMO (MU-MIMO) scenarios. Numerical experiments show that SIA-SC can significantly improve semantic performance across various scenarios.
Exploiting dynamic bifurcation in elastic ribbons for mode skipping and selection

In this paper, we systematically study the dynamic snap-through behavior of a pre-deformed elastic ribbon by combining theoretical analysis, discrete numerical simulations, and experiments. By rotating one of its clamped ends with controlled angular speed, we observe two snap-through transition paths among the multiple stable configurations of a ribbon in three-dimensional (3D) space, which is different from the classical snap-through of a two-dimensional (2D) bistable beam. Our theoretical model for the static bifurcation analysis is derived based on the Kirchhoff equations, and dynamical numerical simulations are conducted using the Discrete Elastic Rods (DER) algorithm. The planar beam model is also employed for the asymptotic analysis of dynamic snap-through behaviors. The results show that, since the snap-through processes of both planar beams and 3D ribbons are governed by the saddle-node bifurcation, the same scaling law for the delay applies. We further demonstrate that, in elastic ribbons, by controlling the rotating velocity at the end, distinct snap-through pathways can be realized by selectively skipping specific modes, moreover, particular final modes can be strategically achieved. Through a parametric study using numerical simulations, we construct general phase diagrams for both mode skipping and selection of snapping ribbons. The work serves as a benchmark for future investigations on dynamic snap-through of thin elastic structures and provides guidelines for the novel design of intelligent mechanical systems.
Localization in Quantum Field Theory

We review the issue of localization in quantum field theory and detail the nonrelativistic limit. Three distinct localization schemes are examined: the Newton-Wigner, the algebraic quantum field theory, and the modal scheme. Among these, the algebraic quantum field theory provides a fundamental concept of localization, rooted in its axiomatic formulation. In contrast, the Newton-Wigner scheme draws inspiration from the Born interpretation, applying mainly to the nonrelativistic regime. The modal scheme, relying on the representation of single particles as positive frequency modes of the Klein-Gordon equation, is found to be incompatible with the algebraic quantum field theory localization. This review delves into the distinctive features of each scheme, offering a comparative analysis. A specific focus is placed on the property of independence between state preparations and observable measurements in spacelike separated regions. Notably, the notion of localization in algebraic quantum field theory violates this independence due to the Reeh-Schlieder theorem. Drawing parallels with the quantum teleportation protocol, it is argued that causality remains unviolated. Additionally, we consider the nonrelativistic limit of quantum field theory, revealing the emergence of the Born scheme as the fundamental concept of localization. Consequently, the nonlocality associated with the Reeh-Schlieder theorem is shown to be suppressed under nonrelativistic conditions.
Towards the creation of a multidisciplinary and multi-institution research platform / Consortium in the EA Highlands : Concept note

The objective of this document is to provide some background and the framework for the scientific project of the proposed Research Platform (or Consortium) in East Africa, and determine its contours. This platform is dedicated to the management of functional diversity of landscapes in the context of the High Plateaus of East Africa (HPEA) for enhancing agricultural production and adaptation of rural communities to changes. This document reflects the discussions and comments made by participants at the seminar held in Nairobi in November 2011 “Management of functional diversity of landscapes in the context of the High Plateaus of East Africa”
Lifting by Image - Leveraging Image Cues for Accurate 3D Human Pose Estimation

The "lifting from 2D pose" method has been the dominant approach to 3D Human Pose Estimation (3DHPE) due to the powerful visual analysis ability of 2D pose estimators. Widely known, there exists a depth ambiguity problem when estimating solely from 2D pose, where one 2D pose can be mapped to multiple 3D poses. Intuitively, the rich semantic and texture information in images can contribute to a more accurate "lifting" procedure. Yet, existing research encounters two primary challenges. Firstly, the distribution of image data in 3D motion capture datasets is too narrow because of the laboratorial environment, which leads to poor generalization ability of methods trained with image information. Secondly, effective strategies for leveraging image information are lacking. In this paper, we give new insight into the cause of poor generalization problems and the effectiveness of image features. Based on that, we propose an advanced framework. Specifically, the framework consists of two stages. First, we enable the keypoints to query and select the beneficial features from all image patches. To reduce the keypoints attention to inconsequential background features, we design a novel Pose-guided Transformer Layer, which adaptively limits the updates to unimportant image patches. Then, through a designed Adaptive Feature Selection Module, we prune less significant image patches from the feature map. In the second stage, we allow the keypoints to further emphasize the retained critical image features. This progressive learning approach prevents further training on insignificant image features. Experimental results show that our model achieves state-of-the-art performance on both the Human3.6M dataset and the MPI-INF-3DHP dataset.
Scalable Approximate Optimal Diagonal Preconditioning

We consider the problem of finding the optimal diagonal preconditioner for a positive definite matrix. Although this problem has been shown to be solvable and various methods have been proposed, none of the existing approaches are scalable to matrices of large dimension, or when access is limited to black-box matrix-vector products, thereby significantly limiting their practical application. In view of these challenges, we propose practical algorithms applicable to finding approximate optimal diagonal preconditioners of large sparse systems. Our approach is based on the idea of dimension reduction, and combines techniques from semi-definite programming (SDP), random projection, semi-infinite programming (SIP), and column generation. Numerical experiments demonstrate that our method scales to sparse matrices of size greater than $10^7$. Notably, our approach is efficient and implementable using only black-box matrix-vector product operations, making it highly practical for a wide variety of applications.
Formalism for Anatomy-Independent Projection and Optimization of Transcranial Magnetic Stimulation Coils

Transcranial magnetic stimulation (TMS) is a popular method for the noninvasive stimulation of neurons in the brain. It has become a standard instrument in experimental brain research and is approved for a range of diagnostic and therapeutic applications. Various applications have been established or approved for specific coil designs with their corresponding spatial electric field distributions. However, the specific coil implementation may no longer be appropriate from the perspective of material and manufacturing opportunities or considering the latest understanding of how to achieve induced electric fields in the head most efficiently. Furthermore, in some cases, field measurements of coils with unknown winding or a user-defined field are available and require an actual implementation. Similar applications exist for magnetic resonance imaging coils. This work aims at introducing a formalism that is completely free from heuristics, iterative optimization, and ad-hoc or manual steps to form practical stimulation coils with a winding consisting of individual turns to either equivalently match an existing coil or produce a given field. The target coil can reside on practically any sufficiently large or closed surface adjacent to or around the head. The method derives an equivalent field through vector projection. In contrast to other coil design or optimization approaches recently presented, the procedure is an explicit forward Hilbert-space vector projection or basis change. For demonstration, we map a commercial figure-of-eight coil as one of the most widely used devices and a more intricate coil recently approved clinically for addiction treatment (H4) onto a bent surface close to the head for highest efficiency and lowest field energy. The resulting projections are within 4% of the target field and reduce the necessary pulse energy by more than 40%.
Clustered Switchback Experiments: Near-Optimal Rates Under Spatiotemporal Interference

We consider experimentation in the presence of non-stationarity, inter-unit (spatial) interference, and carry-over effects (temporal interference), where we wish to estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control. We suppose spatial interference is described by a graph, where a unit's outcome depends on its neighborhood's treatment assignments, and that temporal interference is described by a hidden Markov decision process, where the transition kernel under either treatment (action) satisfies a rapid mixing condition. We propose a clustered switchback design, where units are grouped into clusters and time steps are grouped into blocks and each whole cluster-block combination is assigned a single random treatment. Under this design, we show that for graphs that admit good clustering, a truncated exposure-mapping Horvitz-Thompson estimator achieves $\tilde O(1/NT)$ mean-squared error (MSE), matching an $\Omega(1/NT)$ lower bound up to logarithmic terms. Our results simultaneously generalize the $N=1$ setting of Hu, Wager 2022 (and improves on the MSE bound shown therein for difference-in-means estimators) as well as the $T=1$ settings of Ugander et al 2013 and Leung 2022. Simulation studies validate the favorable performance of our approach.
iDet3D: Towards Efficient Interactive Object Detection for LiDAR Point Clouds

Accurately annotating multiple 3D objects in LiDAR scenes is laborious and challenging. While a few previous studies have attempted to leverage semi-automatic methods for cost-effective bounding box annotation, such methods have limitations in efficiently handling numerous multi-class objects. To effectively accelerate 3D annotation pipelines, we propose iDet3D, an efficient interactive 3D object detector. Supporting a user-friendly 2D interface, which can ease the cognitive burden of exploring 3D space to provide click interactions, iDet3D enables users to annotate the entire objects in each scene with minimal interactions. Taking the sparse nature of 3D point clouds into account, we design a negative click simulation (NCS) to improve accuracy by reducing false-positive predictions. In addition, iDet3D incorporates two click propagation techniques to take full advantage of user interactions: (1) dense click guidance (DCG) for keeping user-provided information throughout the network and (2) spatial click propagation (SCP) for detecting other instances of the same class based on the user-specified objects. Through our extensive experiments, we present that our method can construct precise annotations in a few clicks, which shows the practicality as an efficient annotation tool for 3D object detection.
Ternary Alkali Metal Copper Chalcogenides ACuX (A= Na, K and X= S, Se, Te): Promising Candidate for Solar Harvesting Applications

We report a comprehensive first-principles study of the relative stability of the various possible crystal structures, and the electronic and optical properties of ternary alkali metal chalcogenides ACuX (A= Na/K and X= S/Se/Te) compounds through density functional theory (DFT) calculations. The energetics and phonon spectra of greater than 700 structures were compared, and seven possible stabilized structures of six ACuX compounds were identified using the fixed composition evolutionary search method. Our electronic band structure simulation confirms that all the ternary ACuX compounds are direct band gap semiconductors, with the band gap lying between 0.83 eV to 2.88 eV. These compounds exhibit directly allowed electronic transitions from the valence band to the conduction band, which leads to a significant strength of optical transition probability. This yields a sharp rise in the optical absorption spectra (ranging between 10$^4$ to 10$^5$ cm$^{-1}$) near the energy gap. The estimated spectroscopic limited maximum efficiency (SLME) is about 18% for an 8 $\mu$m thick NaCuTe film. For other ACuX compounds, the SLME ranges between 10% to 13%. In addition, we also explored the feasibility of these ternary ACuX compounds for photocatalytic water splitting applications and found that they can be promising candidates as photocathodes for hydrogen evolution reactions. With a large spread in the band gap and interesting band topology near Fermi level, these chalcogenides can be quite fertile for other energy applications such as thermoelectric, LED, etc.
Iterative singular tube hard thresholding algorithms for tensor recovery

Due to the explosive growth of large-scale data sets, tensors have been a vital tool to analyze and process high-dimensional data. Different from the matrix case, tensor decomposition has been defined in various formats, which can be further used to define the best low-rank approximation of a tensor to significantly reduce the dimensionality for signal compression and recovery. In this paper, we consider the low-rank tensor recovery problem when the tubal rank of the underlying tensor is given or estimated a priori. We propose a novel class of iterative singular tube hard thresholding algorithms for tensor recovery based on the low-tubal-rank tensor approximation, including basic, accelerated deterministic and stochastic versions. Convergence guarantees are provided along with the special case when the measurements are linear. Numerical experiments on tensor compressive sensing and color image inpainting are conducted to demonstrate convergence and computational efficiency in practice.