Miracle-dz/UniFAR-FTUs huggingface dataset:
Photonic Crystals

Course Content As a result of recent remarkable theoretical advances in understanding and technological progress in fabricating artificial periodic structures, engineerable control of fundamental optical properties of an optical medium is becoming practically possible. Apart from providing " designer " materials with desired and significantly enhanced optical characteristics these photonic crystals are exhibiting a variety of novel and unusual phenomena, which are leading to completely new technologies and unexpected applications. This course is an introduction to this flourishing and rapidly expanding field. Topics include: ! Fundamentals of electromagnetic wave propagation in periodic structures (Bloch's theorem, eigenmodes of propagation, symmetries and photonic band structure in 1D, 2D and 3D, origin of photonic bandgaps) ! Confinement and guiding of electromagnetic waves by 1D and 2D defects in the periodicity of photonic crystals, unique properties of resonant cavities and optical waveguides formed by these defects, analogies to donor and acceptor levels in electronic crystals ! Novel optical phenomena associated with in-band propagation effects such as superprisms, negative refraction, superlenses, multirefringence etc. ! Controllable enhancement and prohibition of spontaneous emission in photonic crystals ! Nonlinear Optics in photonic bandgap structures, gap solitons, enchancement of nonlinear interactions on the band edge ! Nonlinear Photonic Crystal structures using 2D quasi-phase-matched nonlinear interactions and nonlinear physical optics in such structures ! Photonic Crystal fabrication techniques ! Emerging new devices and applications associated with photonic crystals, integrated optics based on photonic crystals, photonic crystal and Bragg fibers ! Computational methods of modeling electromagnetic wave propagation in periodic structures, Prerequisites: EECS 537 (Classical Optics), EECS 538 (Optical Waves in Crystals) or permission by the instructor. A solid background in optics is essential. Coursework: (a) 2 homework assignments at the beginning of the course (during the first ~4 weeks). (b) Midterm project – write a report and present it in class on a literature survey on a particular topic. Topic will be suggested either by a student or by the instructor. (c) Term Project: using available software, each student will model/explore a particular photonic crystal problem (selected either by a student or by the instructor), write a report, and present it in class. Reference books: No textbook exists for this course. Course will be based on a combination of published papers and a few existing books on photonic crystals. The book that will be used as the main text, since it provides the broadest available coverage, is:
Nucleon-Nucleon elastic and inelastic scattering using quantum field theory: a comparative study

In this paper, we use Yukawa theory to calculate differential and total cross-sections for elastic and inelastic scattering in nucleon-nucleon interactions. We start from the fundamental Lagrangian and derive the $T$-matrix and hence the invariant scattering matrix leading towards the differential and total cross section. We perform calculations utilizing two types of Yukawa interaction terms: 1) scalar current, and 2) pseudoscalar current. We also carry out calculations in a laboratory frame. Calculated results using scalar current exhibits the exact features of elastic and inelastic scattering. The results agree very well with the differential cross-sections for experimental data. The pseudoscalar current calculations do not offer reasonable results and features of the NN interactions. The simplicity of the theory makes it all the more impressive, thus it can be used in place of more complicated field theories to describe NN interactions.
Can Large Language Models Capture Dissenting Human Voices?

Large language models (LLMs) have shown impressive achievements in solving a broad range of tasks. Augmented by instruction fine-tuning, LLMs have also been shown to generalize in zero-shot settings as well. However, whether LLMs closely align with the human disagreement distribution has not been well-studied, especially within the scope of natural language inference (NLI). In this paper, we evaluate the performance and alignment of LLM distribution with humans using two different techniques to estimate the multinomial distribution: Monte Carlo Estimation (MCE) and Log Probability Estimation (LPE). As a result, we show LLMs exhibit limited ability in solving NLI tasks and simultaneously fail to capture human disagreement distribution. The inference and human alignment performances plunge even further on data samples with high human disagreement levels, raising concerns about their natural language understanding (NLU) ability and their representativeness to a larger human population. The source code for the experiments is available at https://github.com/xfactlab/emnlp2023-LLM-Disagreement
Mean-field theory of chaotic insect swarms.

The harmonically confined Vicsek model displays qualitative and quantitative features observed in natural insect swarms. It exhibits a scale-free transition between single and multicluster chaotic phases. Finite-size scaling indicates that this unusual phase transition occurs at zero confinement [Phys. Rev. E 107, 014209 (2023)2470-004510.1103/PhysRevE.107. 014209]. While the evidence of the scale-free-chaos phase transition comes from numerical simulations, here we present its mean-field theory. Analytically determined critical exponents are those of the Landau theory of equilibrium phase transitions plus dynamical critical exponent z=1 and a new critical exponent φ=0.5 for the largest Lyapunov exponent. The phase transition occurs at zero confinement and noise in the mean-field theory. The noise line of zero largest Lyapunov exponents informs observed behavior: (i) the qualitative shape of the swarm (on average, the center of mass rotates slowly at the rate marked by the winding number and its trajectory fills compactly the space, similarly to the observed condensed nucleus surrounded by vapor) and (ii) the critical exponents resemble those observed in natural swarms. Our predictions include power laws for the frequency of the maximal spectral amplitude and the winding number.
MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African languages

In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages.
SE-Bridge: Speech Enhancement with Consistent Brownian Bridge

We propose SE-Bridge, a novel method for speech enhancement (SE). After recently applying the diffusion models to speech enhancement, we can achieve speech enhancement by solving a stochastic differential equation (SDE). Each SDE corresponds to a probabilistic flow ordinary differential equation (PF-ODE), and the trajectory of the PF-ODE solution consists of the speech states at different moments. Our approach is based on consistency model that ensure any speech states on the same PF-ODE trajectory, correspond to the same initial state. By integrating the Brownian Bridge process, the model is able to generate high-intelligibility speech samples without adversarial training. This is the first attempt that applies the consistency models to SE task, achieving state-of-the-art results in several metrics while saving 15 x the time required for sampling compared to the diffusion-based baseline. Our experiments on multiple datasets demonstrate the effectiveness of SE-Bridge in SE. Furthermore, we show through extensive experiments on downstream tasks, including Automatic Speech Recognition (ASR) and Speaker Verification (SV), that SE-Bridge can effectively support multiple downstream tasks.
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Evaluating the factuality of long-form text generated by large language models (LMs) is non-trivial because (1) generations often contain a mixture of supported and unsupported pieces of information, making binary judgments of quality inadequate, and (2) human evaluation is time-consuming and costly. In this paper, we introduce FACTSCORE, a new evaluation that breaks a generation into a series of atomic facts and computes the percentage of atomic facts supported by a reliable knowledge source. We conduct an extensive human evaluation to obtain FACTSCOREs of people biographies generated by several state-of-the-art commercial LMs -- InstructGPT, ChatGPT, and the retrieval-augmented PerplexityAI -- and report new analysis demonstrating the need for such a fine-grained score (e.g., ChatGPT only achieves 58%). Since human evaluation is costly, we also introduce an automated model that estimates FACTSCORE using retrieval and a strong language model, with less than a 2% error rate. Finally, we use this automated metric to evaluate 6,500 generations from a new set of 13 recent LMs that would have cost $26K if evaluated by humans, with various findings: GPT-4 and ChatGPT are more factual than public models, and Vicuna and Alpaca are some of the best public models. FACTSCORE is available for public use via `pip install factscore`.
Toward spike-based stochastic neural computing

Inspired by the highly irregular spiking activity of cortical neurons, stochastic neural computing is an attractive theory for explaining the operating principles of the brain and the ability to represent uncertainty by intelligent agents. However, computing and learning with high-dimensional joint probability distributions of spiking neural activity across large populations of neurons present as a major challenge. To overcome this, we develop a novel moment embedding approach to enable gradient-based learning in spiking neural networks accounting for the propagation of correlated neural variability. We show under the supervised learning setting a spiking neural network trained this way is able to learn the task while simultaneously minimizing uncertainty, and further demonstrate its application to neuromorphic hardware. Built on the principle of spike-based stochastic neural computing, the proposed method opens up new opportunities for developing machine intelligence capable of computing uncertainty and for designing unconventional computing architectures.
Simulating News Recommendation Ecosystem for Fun and Profit

Understanding the evolution of online news communities is essential for designing more effective news recommender systems. However, due to the lack of appropriate datasets and platforms, the existing literature is limited in understanding the impact of recommender systems on this evolutionary process and the underlying mechanisms, resulting in sub-optimal system designs that may affect long-term utilities. In this work, we propose SimuLine, a simulation platform to dissect the evolution of news recommendation ecosystems and present a detailed analysis of the evolutionary process and underlying mechanisms. SimuLine first constructs a latent space well reflecting the human behaviors, and then simulates the news recommendation ecosystem via agent-based modeling. Based on extensive simulation experiments and the comprehensive analysis framework consisting of quantitative metrics, visualization, and textual explanations, we analyze the characteristics of each evolutionary phase from the perspective of life-cycle theory, and propose a relationship graph illustrating the key factors and affecting mechanisms. Furthermore, we explore the impacts of recommender system designing strategies, including the utilization of cold-start news, breaking news, and promotion, on the evolutionary process, which shed new light on the design of recommender systems.
INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback

The field of automatic evaluation of text generation made tremendous progress in the last few years. In particular, since the advent of neural metrics, like COMET, BLEURT and SEScore2, the newest generation of metrics show a high correlation with human judgment. Unfortunately, quality scores generated with neural metrics are not interpretable and it is unclear which part of the generation output is criticized by the metrics. To address this limitation, we present I NSTRUCT S CORE , an open-source, explainable evaluation metric for text generation. By harnessing both explicit human instruction and the implicit knowledge of GPT4, we fine-tune a LLAMA model to create an evaluative metric that can produce a diagnostic report aligned with human judgment. We evaluate I NSTRUCT S CORE on the WMT22 Zh-En translation task, where our 7B model surpasses other LLM-based baselines, including those based on 175B GPT3. Impressively, our I NSTRUCT S CORE , even without direct super-vision from human-rated data, achieves performance levels on par with state-of-the-art metrics like COMET22, which was fine-tuned on human ratings. 1
Weighted Context-Free-Language Ordered Binary Decision Diagrams

Over the years, many variants of Binary Decision Diagrams (BDDs) have been developed to address the deficiencies of vanilla BDDs. A recent innovation is the Context-Free-Language Ordered BDD (CFLOBDD), a hierarchically structured decision diagram, akin to BDDs enhanced with a procedure-call mechanism, which allows substructures to be shared in ways not possible with BDDs. For some functions, CFLOBDDs are exponentially more succinct than BDDs. Unfortunately, the multi-terminal extension of CFLOBDDs, like multi-terminal BDDs, cannot efficiently represent functions of type B^n ->D, when the function's range has many different values. This paper addresses this limitation through a new data structure called Weighted CFLOBDDs (WCFLOBDDs). WCFLOBDDs extend CFLOBDDs using insights from the design of Weighted BDDs (WBDDs) -- BDD-like structures with weights on edges. We show that WCFLOBDDs can be exponentially more succinct than both WBDDs and CFLOBDDs. We also evaluate WCFLOBDDs for quantum-circuit simulation, and find that they perform better than WBDDs and CFLOBDDs on most benchmarks. With a 15-minute timeout, the number of qubits that can be handled by WCFLOBDDs is 1,048,576 for GHZ (1x over CFLOBDDs, 256x over WBDDs); 262,144 for BV and DJ (2x over CFLOBDDs, 64x over WBDDs); and 2,048 for QFT (128x over CFLOBDDs, 2x over WBDDs).
Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters

Research in self-supervised learning (SSL) with natural images has progressed rapidly in recent years and is now increasingly being applied to and benchmarked with datasets containing remotely sensed imagery. A common benchmark case is to evaluate SSL pre-trained model embeddings on datasets of remotely sensed imagery with small patch sizes, e.g., 32x32 pixels, whereas standard SSL pre-training takes place with larger patch sizes, e.g., 224x224. Furthermore, pre-training methods tend to use different image normalization preprocessing steps depending on the dataset. In this paper, we show, across seven satellite and aerial imagery datasets of varying resolution, that by simply following the preprocessing steps used in pre-training (precisely, image sizing and normalization methods), one can achieve significant performance improvements when evaluating the extracted features on downstream tasks -- an important detail overlooked in previous work in this space. We show that by following these steps, ImageNet pre-training remains a competitive baseline for satellite imagery based transfer learning tasks -- for example we find that these steps give +32.28 to overall accuracy on the So2Sat random split dataset and +11.16 on the EuroSAT dataset. Finally, we report comprehensive benchmark results with a variety of simple baseline methods for each of the seven datasets, forming an initial benchmark suite for remote sensing imagery.
On the Optimal Batch Size for Byzantine-Robust Distributed Learning

Byzantine-robust distributed learning (BRDL), in which computing devices are likely to behave abnormally due to accidental failures or malicious attacks, has recently become a hot research topic. However, even in the independent and identically distributed (i.i.d.) case, existing BRDL methods will suffer from a significant drop on model accuracy due to the large variance of stochastic gradients. Increasing batch sizes is a simple yet effective way to reduce the variance. However, when the total number of gradient computation is fixed, a too-large batch size will lead to a too-small iteration number (update number), which may also degrade the model accuracy. In view of this challenge, we mainly study the optimal batch size when the total number of gradient computation is fixed in this work. In particular, we theoretically and empirically show that when the total number of gradient computation is fixed, the optimal batch size in BRDL increases with the fraction of Byzantine workers. Therefore, compared to the case without attacks, the batch size should be set larger when under Byzantine attacks. However, for existing BRDL methods, large batch sizes will lead to a drop on model accuracy, even if there is no Byzantine attack. To deal with this problem, we propose a novel BRDL method, called Byzantine-robust stochastic gradient descent with normalized momentum (ByzSGDnm), which can alleviate the drop on model accuracy in large-batch cases. Moreover, we theoretically prove the convergence of ByzSGDnm for general non-convex cases under Byzantine attacks. Empirical results show that ByzSGDnm has a comparable performance to existing BRDL methods under bit-flipping failure, but can outperform existing BRDL methods under deliberately crafted attacks.
Signatures of hierarchical temporal processing in the mouse visual system

A core challenge for the brain is to process information across various timescales. This could be achieved by a hierarchical organization of temporal processing through intrinsic mechanisms (e.g., recurrent coupling or adaptation), but recent evidence from spike recordings of the rodent visual system seems to conflict with this hypothesis. Here, we used an optimized information-theoretic and classical autocorrelation analysis to show that information- and intrinsic timescales of spiking activity increase along the anatomical hierarchy of the mouse visual system, while information-theoretic predictability decreases. Moreover, the timescale hierarchy was invariant to the stimulus condition, whereas the decrease in predictability was strongest under natural movie stimulation. We could reproduce this effect in a basic recurrent network model with correlated sensory input. Our findings suggest that the rodent visual system indeed employs intrinsic mechanisms to achieve longer integration for higher cortical areas, while simultaneously reducing predictability for an efficient neural code.
Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization

Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques. Robust semantic features can be learned and transmitted in an analog fashion, but it poses new challenges to hardware, protocol, and encryption. In this paper, we propose a digital semantic communication system, which consists of an encoding network deployed on a resource-limited device and a decoding network deployed at the edge. To acquire better semantic representation for digital transmission, a novel non-linear quantization module is proposed to efficiently quantize semantic features with trainable quantization levels. Additionally, structured pruning is incorporated to reduce the dimension of the transmitted features. We also introduce a semantic learning loss (SLL) function to reduce semantic error. To adapt to various channel conditions and inputs under constraints of communication and computing resources, a policy network is designed to adaptively choose the split point and the dimension of the transmitted semantic features. Experiments using the CIFAR-10 and ImageNet dataset for image classification are employed to evaluate the proposed digital semantic communication network, and ablation studies are conducted to assess the proposed quantization module, structured pruning and SLL.
A Rank-Based Sequential Test of Independence

We consider the problem of independence testing for two univariate random variables in a sequential setting. By leveraging recent developments on safe, anytime-valid inference, we propose a test with time-uniform type I error control and derive explicit bounds on the finite sample performance of the test. We demonstrate the empirical performance of the procedure in comparison to existing sequential and non-sequential independence tests. Furthermore, since the proposed test is distribution free under the null hypothesis, we empirically simulate the gap due to Ville's inequality, the supermartingale analogue of Markov's inequality, that is commonly applied to control type I error in anytime-valid inference, and apply this to construct a truncated sequential test.
Vector Quarkonia at the LHC with Jethad: A High-Energy Viewpoint

In this review, we discuss and extend the study of the inclusive production of vector quarkonia, J/ψ and Υ, emitted with large transverse momenta and rapidities at the LHC. We adopt the novel ZCW19+ determination of fragmentation functions to depict the quarkonium production mechanism at the next-to-leading level of perturbative QCD. This approach is based on the nonrelativistic QCD formalism well adapted to describe the formation of a quarkonium state from the collinear fragmentation of a gluon or a constituent heavy quark at the lowest energy scale. We rely upon the NLL/NLO+ hybrid high-energy and collinear factorization for differential cross-sections, where the collinear formalism is enhanced by the BFKL resummation of next-to-leading energy logarithms arising in the t-channel. We employ the method to analyze the behavior of the rapidity distributions for double-inclusive vector quarkonium and inclusive vector quarkonium plus jet emissions. We discover that the natural stability of the high-energy series, previously seen in observables sensitive to the emission of hadrons with heavy flavor detected in the rapidity acceptance of LHC barrel calorimeters, becomes even more manifest when these particles are tagged in forward regions covered by endcaps. Our findings present the important message that vector quarkonia at the LHC via hybrid factorization offer a unique chance to perform precision studies of high-energy QCD, as well as an intriguing opportunity to shed light on the quarkonium production puzzle.