Miracle-dz/UniFAR-FTUs huggingface dataset:
A Clique-Based Separator for Intersection Graphs of Geodesic Disks in $\mathbb{R}^2$

Let $d$ be a (well-behaved) shortest-path metric defined on a path-connected subset of $\mathbb{R}^2$ and let $\mathcal{D}=\{D_1,\ldots,D_n\}$ be a set of geodesic disks with respect to the metric $d$. We prove that $\mathcal{G}^{\times}(\mathcal{D})$, the intersection graph of the disks in $\mathcal{D}$, has a clique-based separator consisting of $O(n^{3/4+\varepsilon})$ cliques. This significantly extends the class of objects whose intersection graphs have small clique-based separators. Our clique-based separator yields an algorithm for $q$-COLORING that runs in time $2^{O(n^{3/4+\varepsilon})}$, assuming the boundaries of the disks $D_i$ can be computed in polynomial time. We also use our clique-based separator to obtain a simple, efficient, and almost exact distance oracle for intersection graphs of geodesic disks. Our distance oracle uses $O(n^{7/4+\varepsilon})$ storage and can report the hop distance between any two nodes in $\mathcal{G}^{\times}(\mathcal{D})$ in $O(n^{3/4+\varepsilon})$ time, up to an additive error of one. So far, distance oracles with an additive error of one that use subquadratic storage and sublinear query time were not known for such general graph classes.
Quasiclassical theory of superconductivity: a multiple interface geometry

This Thesis is concerned with the quasiclassical theory of meso-scopic superconductivity. The aim of the Thesis is to introduce the boundary conditions for a quasiclassical Green’s function on partially transparent interfaces in mesoscopic superconducting structures and to analyze the range of applicability of the quasiclassical theory. The linear boundary conditions for Andreev amplitudes, factoring the quasiclassical Green’s function, are presented. The quasiclassical theory on classical trajectories is reviewed and then generalized to include knots with paths intersections. The main focus of the Thesis is on the range of validity of the quasiclassical theory. This goal is achieved by comparison of quasiclassical and exact Green’s functions. The exact Gor’kov Greens function cannot be directly used for the comparison because of its strong microscopic variations on the length-scale of λF. It is the coarse-grain averaged exact Green’s function which is appropriate for the comparison. In most of the typical cases the calculations show very good agreement between both theories. Only for certain special situations, where the classical trajectory contains loops, one encounters discrepancies. The numerical and analytical analysis of the role of the loop-like structures and their influence on discrepancies between both exact and quasiclassical approaches is one of the main results of the Thesis. It is shown that the terms missing in the quasiclassical theory can be attributed to the loops formed by the interfering paths. In typical real samples any imperfection on the scale larger than the Fermi wavelength disconnects the loops and the path is transformed into the tree-like graph. It is concluded that the quasiclassical theory is fully applicable in most of real mesoscopic samples. In the situations where the conventional quasiclassical theory is inapplicable due to contribution of the interfering path, one can use the modification of the quasiclassical technique suggested in the Thesis.
Tune without Validation: Searching for Learning Rate and Weight Decay on Training Sets

We introduce Tune without Validation (Twin), a pipeline for tuning learning rate and weight decay without validation sets. We leverage a recent theoretical framework concerning learning phases in hypothesis space to devise a heuristic that predicts what hyper-parameter (HP) combinations yield better generalization. Twin performs a grid search of trials according to an early-/non-early-stopping scheduler and then segments the region that provides the best results in terms of training loss. Among these trials, the weight norm strongly correlates with predicting generalization. To assess the effectiveness of Twin, we run extensive experiments on 20 image classification datasets and train several families of deep networks, including convolutional, transformer, and feed-forward models. We demonstrate proper HP selection when training from scratch and fine-tuning, emphasizing small-sample scenarios.
Cyclic Polygon Plots

In this paper, we introduce the cyclic polygon plot, a representation based on a novel projection concept for multi-dimensional values. Cyclic polygon plots combine the typically competing requirements of quantitativeness, image-space efficiency, and readability. Our approach is complemented with a placement strategy based on its intrinsic features, resulting in a dimensionality reduction strategy that is consistent with our overall concept. As a result, our approach combines advantages from dimensionality reduction techniques and quantitative plots, supporting a wide range of tasks in multi-dimensional data analysis. We examine and discuss the overall properties of our approach, and demonstrate its utility with a user study and selected examples.
A 28.6 mJ/iter Stable Diffusion Processor for Text-to-Image Generation with Patch Similarity-based Sparsity Augmentation and Text-based Mixed-Precision

This paper presents an energy-efficient stable diffusion processor for text-to-image generation. While stable diffusion attained attention for high-quality image synthesis results, its inherent characteristics hinder its deployment on mobile platforms. The proposed processor achieves high throughput and energy efficiency with three key features as solutions: 1) Patch similarity-based sparsity augmentation (PSSA) to reduce external memory access (EMA) energy of self-attention score by 60.3 %, leading to 37.8 % total EMA energy reduction. 2) Text-based important pixel spotting (TIPS) to allow 44.8 % of the FFN layer workload to be processed with low-precision activation. 3) Dual-mode bit-slice core (DBSC) architecture to enhance energy efficiency in FFN layers by 43.0 %. The proposed processor is implemented in 28 nm CMOS technology and achieves 3.84 TOPS peak throughput with 225.6 mW average power consumption. In sum, 28.6 mJ/iteration highly energy-efficient text-to-image generation processor can be achieved at MS-COCO dataset.
GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM

Key-value (KV) caching has become the de-facto to accelerate generation speed for large language models (LLMs) inference. However, the growing cache demand with increasing sequence length has transformed LLM inference to be a memory bound problem, significantly constraining the system throughput. Existing methods rely on dropping unimportant tokens or quantizing all entries uniformly. Such methods, however, often incur high approximation errors to represent the compressed matrices. The autoregressive decoding process further compounds the error of each step, resulting in critical deviation in model generation and deterioration of performance. To tackle this challenge, we propose GEAR, an efficient KV cache compression framework that achieves near-lossless high-ratio compression. GEAR first applies quantization to majority of entries of similar magnitudes to ultra-low precision. It then employs a low rank matrix to approximate the quantization error, and a sparse matrix to remedy individual errors from outlier entries. By adeptly integrating three techniques, GEAR is able to fully exploit their synergistic potentials. Our experiments demonstrate that compared to alternatives, GEAR achieves near-lossless 4-bit KV cache compression with up to 2.38x throughput improvement, while reducing peak-memory size up to 2.29x. Our code is publicly available at https://github.com/HaoKang-Timmy/GEAR.
Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models

While large language models have achieved remarkable performance on various code generation benchmarks, there have been growing concerns regarding potential contamination of these benchmarks as they may be leaked into pretraining and finetuning data. While recent work has investigated contamination in natural language generation and understanding tasks, there has been less extensive research into how data contamination impacts the evaluation of code generation, which is critical for understanding the robustness and reliability of LLMs in programming contexts. In this work, we perform a comprehensive study of data contamination of popular code generation benchmarks, and precisely quantify their overlap with pretraining corpus through both surface-level and semantic-level matching. In our experiments, we show that there are substantial overlap between popular code generation benchmarks and open training corpus, and models perform significantly better on the subset of the benchmarks where similar solutions are seen during training. We also conduct extensive analysis on the factors that affects model memorization and generalization, such as model size, problem difficulty, and question length. We release all resulting files from our matching pipeline for future research.
Semicoherent Symmetric Quantum Processes: Theory and Applications

Discovering pragmatic and efficient approaches to synthesize $\varepsilon$-approximations to quantum operators such as real (imaginary) time-evolution propagators in terms of the basic quantum operations (gates) is challenging. These invaluable $\varepsilon$-approximations enable the compilation of classical and quantum algorithms modeling, e.g., dynamical properties. In parallel, symmetries are powerful tools concisely describing the fundamental laws of nature; the symmetrical underpinnings of physical laws having consistently provided profound insights and substantially increased predictive power. In this work, we consider the interplay between $\varepsilon$-approximations processes and symmetries in a semi-coherent context--where measurements occur at each logical clock cycle. We draw inspiration from Pascual Jordan's groundbreaking formulation of non-associative, but commutative, algebraic forms. Our symmetrized formalism is applied in various domains such as quantum random walks, real-time-evolutions, variational algorithms ansatzes, and efficient entanglement verification. Our work paves the way for a deeper understanding and greater appreciation of how symmetries can be used to control quantum dynamics in the near-term.
Synthetic Privileged Information Enhances Medical Image Representation Learning

Multimodal self-supervised representation learning has consistently proven to be a highly effective method in medical image analysis, offering strong task performance and producing biologically informed insights. However, these methods heavily rely on large, paired datasets, which is prohibitive for their use in scenarios where paired data does not exist, or there is only a small amount available. In contrast, image generation methods can work well on very small datasets, and can find mappings between unpaired datasets, meaning an effectively unlimited amount of paired synthetic data can be generated. In this work, we demonstrate that representation learning can be significantly improved by synthetically generating paired information, both compared to training on either single-modality (up to 4.4x error reduction) or authentic multi-modal paired datasets (up to 5.6x error reduction).
A Detection and Filtering Framework for Collaborative Localization

Increasingly, autonomous vehicles (AVs) are becoming a reality, such as the Advanced Driver Assistance Systems (ADAS) in vehicles that assist drivers in driving and parking functions with vehicles today. The localization problem for AVs relies primarily on multiple sensors, including cameras, LiDARs, and radars. Manufacturing, installing, calibrating, and maintaining these sensors can be very expensive, thereby increasing the overall cost of AVs. This research explores the means to improve localization on vehicles belonging to the ADAS category in a platooning context, where an ADAS vehicle follows a lead"Smart"AV equipped with a highly accurate sensor suite. We propose and produce results by using a filtering framework to combine pose information derived from vision and odometry to improve the localization of the ADAS vehicle that follows the smart vehicle.
Foundational propositions of hesitant fuzzy sets and parameter reductions of hesitant fuzzy information systems

Hesitant fuzzy sets are widely used in the instances of uncertainty and hesitation. The inclusion relationship is an important and foundational deﬁnition for sets. Hesitant fuzzy set, as a kind of set, needs explicit deﬁnition of inclusion relationship. Base on the hesitant fuzzy membership degree of discrete form, several kinds of inclusion relationships for hesitant fuzzy sets are proposed. And then some foundational propositions of hesitant fuzzy sets and the families of hesitant fuzzy sets are presented. Finally, some foundational propositions of hesitant fuzzy information systems with respect to parameter reductions are put forward, and an example and an algorithm are given to illustrate the processes of parameter reductions
A Decoupled Approach for Composite Sparse-plus-Smooth Penalized Optimization

We consider a linear inverse problem whose solution is expressed as a sum of two components, one of them being smooth while the other presents sparse properties. This problem is solved by minimizing an objective function with a least square data-fidelity term and a different regularization term applied to each of the components. Sparsity is promoted with a $\ell_1$ norm, while the other component is penalized by means of a $\ell_2$ norm. We characterize the solution set of this composite optimization problem by stating a Representer Theorem. Consequently, we identify that solving the optimization problem can be decoupled, first identifying the sparse solution as a solution of a modified single-variable problem, then deducing the smooth component. We illustrate that this decoupled solving method can lead to significant computational speedups in applications, considering the problem of Dirac recovery over a smooth background with two-dimensional partial Fourier measurements.
Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation

Vanilla text-to-image diffusion models struggle with generating accurate human images, commonly resulting in imperfect anatomies such as unnatural postures or disproportionate limbs.Existing methods address this issue mostly by fine-tuning the model with extra images or adding additional controls -- human-centric priors such as pose or depth maps -- during the image generation phase. This paper explores the integration of these human-centric priors directly into the model fine-tuning stage, essentially eliminating the need for extra conditions at the inference stage. We realize this idea by proposing a human-centric alignment loss to strengthen human-related information from the textual prompts within the cross-attention maps. To ensure semantic detail richness and human structural accuracy during fine-tuning, we introduce scale-aware and step-wise constraints within the diffusion process, according to an in-depth analysis of the cross-attention layer. Extensive experiments show that our method largely improves over state-of-the-art text-to-image models to synthesize high-quality human images based on user-written prompts. Project page: \url{https://hcplayercvpr2024.github. io}.
Identifying Causal Effects Under Functional Dependencies

We study the identification of causal effects, motivated by two improvements to identifiability which can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions). First, an unidentifiable causal effect may become identifiable when certain variables are functional. Second, certain functional variables can be excluded from being observed without affecting the identifiability of a causal effect, which may significantly reduce the number of needed variables in observational data. Our results are largely based on an elimination procedure which removes functional variables from a causal graph while preserving key properties in the resulting causal graph, including the identifiability of causal effects.
Multiple Instance Learning with random sampling for Whole Slide Image Classification

In computational pathology, random sampling of patches during training of Multiple Instance Learning (MIL) methods is computationally efficient and serves as a regularization strategy. Despite its promising benefits, questions concerning performance trends for varying sample sizes and its influence on model interpretability remain. Addressing these, we reach an optimal performance enhancement of 1.7% using thirty percent of patches on the CAMELYON16 dataset, and 3.7% with only eight samples on the TUPAC16 dataset. We also find interpretability effects are strongly dataset-dependent, with interpretability impacted on CAMELYON16, while remaining unaffected on TUPAC16. This reinforces that both the performance and interpretability relationships with sampling are closely task-specific. End-to-end training with 1024 samples reveals improvements across both datasets compared to pre-extracted features, further highlighting the potential of this efficient approach.
From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs

With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support. However, existing studies primarily focused on learning chain-like rules, which limit their semantic expressions and accurate prediction abilities. As a result, chain-like rules usually fire on the incorrect grounding values, producing inaccurate or even erroneous reasoning results. In this paper, we propose the concept of tree-like rules on knowledge graphs to expand the application scope and improve the reasoning ability of rule-based methods. Meanwhile, we propose an effective framework for refining chain-like rules into tree-like rules. Experimental comparisons on four public datasets show that the proposed framework can easily adapt to other chain-like rule induction methods and the refined tree-like rules consistently achieve better performances than chain-like rules on link prediction. The data and code of this paper can be available at https://anonymous.4open. science/r/tree-rule-E3CD/.
ERASOR++: Height Coding Plus Egocentric Ratio Based Dynamic Object Removal for Static Point Cloud Mapping

Mapping plays a crucial role in location and navigation within automatic systems. However, the presence of dynamic objects in 3D point cloud maps generated from scan sensors can introduce map distortion and long traces, thereby posing challenges for accurate mapping and navigation. To address this issue, we propose ERASOR++, an enhanced approach based on the Egocentric Ratio of Pseudo Occupancy for effective dynamic object removal. To begin, we introduce the Height Coding Descriptor, which combines height difference and height layer information to encode the point cloud. Subsequently, we propose the Height Stack Test, Ground Layer Test, and Surrounding Point Test methods to precisely and efficiently identify the dynamic bins within point cloud bins, thus overcoming the limitations of prior approaches. Through extensive evaluation on open-source datasets, our approach demonstrates superior performance in terms of precision and efficiency compared to existing methods. Furthermore, the techniques described in our work hold promise for addressing various challenging tasks or aspects through subsequent migration.
Generic ETH: Eigenstate Thermalization beyond the Microcanonical

The Eigenstate Thermalization Hypothesis (ETH) has played a key role in recent advances in the high energy and condensed matter communities. It explains how an isolated quantum system in a far-from-equilibrium initial state can evolve to a state that is indistinguishable from thermal equilibrium, with observables relaxing to almost time-independent results that can be described using traditional statistical mechanics ensembles. In this work we probe the limits of ETH, pushing it outside its prototypical applications in several directions. We design a qutrit lattice system with conserved quasilocal charge, in which we verify a form of generalized eigenstate thermalization. We also observe signatures of thermalization in states well outside microcanonical windows of both charge and energy, which we dub `generic ETH. '
DT-SIM: Property-Based Testing for MPC Security

Formal methods for guaranteeing that a protocol satisfies a cryptographic security definition have advanced substantially, but such methods are still labor intensive and the need remains for an automated tool that can positively identify an insecure protocol. In this work, we demonstrate that property-based testing,"run it a bunch of times and see if it breaks", is effective for detecting security bugs in secure protocols. We specifically target Secure Multi-Party Computation (MPC), because formal methods targeting this security definition for bit-model implementations are particularly difficult. Using results from the literature for Probabilistic Programming Languages and statistical inference, we devise a test that can detect various flaws in a bit-level implementation of an MPC protocol. The test is grey-box; it requires only transcripts of randomness consumed by the protocol and of the inputs, outputs, and messages. It successfully detects several different mistakes and biases introduced into two different implementations of the classic GMW protocol. Applied to hundreds of randomly generated protocols, it identifies nearly all of them as insecure. We also include an analysis of the parameters of the test, and discussion of what makes detection of MPC (in)security difficult.
A Study on Effect of Job Satisfaction, Stress and Emotional Intelligence on Job Performance among HEI Faculty Members

Work satisfaction, stress, and emotional intelligence are important characteristics that have an impact on n individual’s performance. The purpose of this research is to gain a better understanding of the relationships between faculty members' stress levels, emotional state, and performance. Faculty members can be more effective and successful by understanding the elements that determine work performance. In the academic context, job performance is a crucial factor. One of the most important factors influencing faculty members' research productivity and success as teachers is their emotional intelligence. The study is descriptive research and questionnaire method was used to collect the data. Correlation analysis was used to explore relationships between variables.Regression analysis is used to identify predictors of job performance.This research study aims to shed light on the intricate relationship between job satisfaction, stress, emotional intelligence, and job performance among HEI faculty members. By understanding these dynamics, institutions can implement strategies to support faculty members in their roles and, in turn, enhance the overall quality of higher education.