Miracle-dz/UniFAR-FTUs huggingface dataset:
Fourier Electron Optics with Massless Dirac Fermions Scattered by Quantum Dot Lattice

The field of electron optics exploits the analogy between the movement of electrons or charged quasiparticles, primarily in two-dimensional materials subjected to electric and magnetic (EM) fields and the propagation of electromagnetic waves in a dielectric medium with varied refractive index. We significantly extend this analogy by introducing Fourier electron optics (FEO) with massless Dirac fermions (MDF), namely the charge carriers of single-layer graphene under ambient conditions, by considering their scattering from a two-dimensional quantum dot lattice (TDQDL) treated within Lippmann-Schwinger formalism. By considering the scattering of MDF from TDQDL with a cavity, as well as the moir\'{e} pattern of twisted TDQDLs, we establish an electronic analogue of Babinet's principle in optics. Exploiting the similarity of the resulting differential scattering cross-section with the Fraunhofer diffraction pattern, we construct a dictionary for such FEO. Subsequently, we evaluate the resistivity of such scattered MDF using the Boltzmann approach as a function of the angle made between the direction of propagation of these charge-carriers and the symmetry axis of the dot-lattice, and Fourier analyze them to show that the spatial frequency associated with the angle-resolved resistivity gets filtered according to the structural changes in the dot lattice, indicating wider applicability of FEO of MDF.
A comparative study of explicit and implicit Large Eddy Simulations using a high-order discontinuous Galerkin solver: application to a Formula 1 front wing

This paper explores two Large Eddy Simulation (LES) approaches within the framework of the high-order discontinuous Galerkin solver, Horses3D. The investigation focuses on an Inverted Multi-element Wing in Ground Effect (i.e. 2.5D Imperial Front Wing section) representing a Formula 1 front wing, and compares the strengths and limitations of the two LES methods. The explicit LES formulation relies on the Vreman model, that adapts to laminar, transitional and turbulent regimes. The numerical formulation uses nodal basis functions and Gauss points. The implicit LES formulation, does not require explicit turbulence modeling but relies in the discretization scheme. We use the Kennedy-Gruber entropy stable formulation to enhance stability in under resolved simulations, since we recover the continuous properties such as entropy conservation at a discrete level. This formulation employs Gauss-Lobatto points, which downgrades the accuracy of integration but allows for larger time steps in explicit time integration. We compare our results to Nektar++ [1] showing that both LES techniques provide results that agree well with the reference values. The implicit LES shows to better capture transition and allows for larger time steps at a similar cost per iteration. We conclude that this implicit LES formulation is very attractive for complex simulations.
Cobra Effect in Reference-Free Image Captioning Metrics

Evaluating the compatibility between textual descriptions and corresponding images represents a core endeavor within multi-modal research. In recent years, a proliferation of reference-free methods, leveraging visual-language pre-trained models (VLMs), has emerged. Empirical evidence has substantiated that these innovative approaches exhibit a higher correlation with human judgment, marking a significant advancement in the field. However, does a higher correlation with human evaluations alone sufficiently denote the complete of a metric? In response to this question, in this paper, we study if there are any deficiencies in reference-free metrics. Specifically, inspired by the Cobra Effect, we utilize metric scores as rewards to direct the captioning model toward generating descriptions that closely align with the metric's criteria. If a certain metric has flaws, it will be exploited by the model and reflected in the generated sentences. Our findings reveal that descriptions guided by these metrics contain significant flaws, e.g. incoherent statements and excessive repetition. Subsequently, we propose a novel method termed Self-Improving to rectify the identified shortcomings within these metrics. We employ GPT-4V as an evaluative tool to assess generated sentences and the result reveals that our approach achieves state-of-the-art (SOTA) performance. In addition, we also introduce a challenging evaluation benchmark called Flaws Caption to evaluate reference-free image captioning metrics comprehensively. Our code is available at https://github.com/aaronma2020/robust_captioning_metric
Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers

We present a novel bi-directional Transformer architecture (BiXT) which scales linearly with input size in terms of computational cost and memory consumption, but does not suffer the drop in performance or limitation to only one input modality seen with other efficient Transformer-based approaches. BiXT is inspired by the Perceiver architectures but replaces iterative attention with an efficient bi-directional cross-attention module in which input tokens and latent variables attend to each other simultaneously, leveraging a naturally emerging attention-symmetry between the two. This approach unlocks a key bottleneck experienced by Perceiver-like architectures and enables the processing and interpretation of both semantics (`what') and location (`where') to develop alongside each other over multiple layers -- allowing its direct application to dense and instance-based tasks alike. By combining efficiency with the generality and performance of a full Transformer architecture, BiXT can process longer sequences like point clouds or images at higher feature resolutions and achieves competitive performance across a range of tasks like point cloud part segmentation, semantic image segmentation and image classification.
Optimal feature rescaling in machine learning based on neural networks

This paper proposes a novel approach to improve the training efficiency and the generalization performance of Feed Forward Neural Networks (FFNNs) resorting to an optimal rescaling of input features (OFR) carried out by a Genetic Algorithm (GA). The OFR reshapes the input space improving the conditioning of the gradient-based algorithm used for the training. Moreover, the scale factors exploration entailed by GA trials and selection corresponds to different initialization of the first layer weights at each training attempt, thus realizing a multi-start global search algorithm (even though restrained to few weights only) which fosters the achievement of a global minimum. The approach has been tested on a FFNN modeling the outcome of a real industrial process (centerless grinding).
HunFlair2 in a cross-corpus evaluation of named entity recognition and normalization tools

With the exponential growth of the life sciences literature, biomedical text mining (BTM) has become an essential technology for accelerating the extraction of insights from publications. Entity extraction, i.e., the identification of entities in texts, such as diseases, drugs, or genes, and entity normalization, i.i. , their linkage to a reference knowledge base, are crucial steps in any BTM pipeline to enable information aggregation and integration of information contained in many different documents. However, tools for these two steps are rarely applied in the same context in which they were developed. Instead, they are applied “in the wild”, i.e., on application-dependent text collections ranging from moderately to extremely different from those used for the tools’ training, varying, e.g., in focus, genre, style, and text type. This raises the question of whether the reported performance of BTM tools, usually obtained by training and evaluating on different partitions of the same corpus, can be trusted for downstream applications. Here, we report on the results of a carefully designed cross-corpus benchmark for named entity extraction, where tools were applied systematically to corpora not used during their training. Based on a survey of 28 published systems, we selected five, based on pre-defined criteria like feature richness and availability, for an in-depth analysis on three publicly available corpora encompassing four different entity types. Comparison between tools results in a mixed picture and shows that the performance in a cross-corpus setting is significantly lower than in an in-corpus setting. HunFlair2, the redesigned and extended successor of the HunFlair tool, showed the best performance on average, being closely followed by PubTator. Our results indicate that users of BTM tools should expect diminishing performances when applying them in “the wild” compared to original publications and show that further research is necessary to make BTM tools more robust
DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation

Distribution shifts and adversarial examples are two major challenges for deploying machine learning models. While these challenges have been studied individually, their combination is an important topic that remains relatively under-explored. In this work, we study the problem of adversarial robustness under a common setting of distribution shift - unsupervised domain adaptation (UDA). Specifically, given a labeled source domain $D_S$ and an unlabeled target domain $D_T$ with related but different distributions, the goal is to obtain an adversarially robust model for $D_T$. The absence of target domain labels poses a unique challenge, as conventional adversarial robustness defenses cannot be directly applied to $D_T$. To address this challenge, we first establish a generalization bound for the adversarial target loss, which consists of (i) terms related to the loss on the data, and (ii) a measure of worst-case domain divergence. Motivated by this bound, we develop a novel unified defense framework called Divergence Aware adveRsarial Training (DART), which can be used in conjunction with a variety of standard UDA methods; e.g., DANN [Ganin and Lempitsky, 2015]. DART is applicable to general threat models, including the popular $\ell_p$-norm model, and does not require heuristic regularizers or architectural changes. We also release DomainRobust: a testbed for evaluating robustness of UDA models to adversarial attacks. DomainRobust consists of 4 multi-domain benchmark datasets (with 46 source-target pairs) and 7 meta-algorithms with a total of 11 variants. Our large-scale experiments demonstrate that on average, DART significantly enhances model robustness on all benchmarks compared to the state of the art, while maintaining competitive standard accuracy. The relative improvement in robustness from DART reaches up to 29.2% on the source-target domain pairs considered.
Meta Ranking: Less Capable Language Models are Capable for Single Response Judgement

Although Large Language Models (LLMs) have demonstrated strong performance on a wide range of tasks, they still face reliability challenges such as hallucination. Previous studies reveal that highly capable LLMs like GPT-4 are effective in judging the reliability of individual responses, while less capable ones are often tuned to evaluate the relative reliability of responses to the same query. To enable less capable LLMs to effectively judge the reliability of individual responses, we propose a novel method named $\textit{Meta}$ $\textit{Ranking}$ (MR). Unlike previous methods, which assess the response directly, we achieve the judgement by comparing the target query-response pair with reference query-response pairs. We found its remarkable effectiveness in error detection for LLM responses on reasoning tasks, where less capable LLMs could outperform strong baselines, even without fine-tuning. We further demonstrate that MR can be used to enhance the performance of LLMs in two practical applications: query routing and iterative training data filtering. The former achieves GPT-4-turbo comparable performance with less than half the token consumption, while the latter makes the instruction-tuned LLaMA-7B and Phi-2, a 2.7B model, significantly surpass Alpaca-13B over fewer training samples, underscoring the high potential of our proposed method.
LEIA: Facilitating Cross-Lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation

Adapting English-based large language models (LLMs) to other languages has become increasingly popular due to the efficiency and potential of cross-lingual transfer. However, existing language adaptation methods often overlook the benefits of cross-lingual supervision. In this study, we introduce LEIA, a language adaptation tuning method that utilizes Wikipedia entity names aligned across languages. This method involves augmenting the target language corpus with English entity names and training the model using left-to-right language modeling. We assess LEIA on diverse question answering datasets using 7B-parameter LLMs, demonstrating significant performance gains across various non-English languages. The source code is available at https://github.com/studio-ousia/leia.
B OLT : A Natural Language Interface for Dashboard Authoring

Authoring dashboards is often a complex process, requiring expertise in both data analysis and visualization design. With current tools, authors lack the means to express their objectives for creating a dashboard (e.g., summarizing data changes or comparing data categories), making it difficult to discover and assemble content relevant to the dashboard. Addressing this challenge, we propose the idea of employing natural language (NL) for dashboard authoring with a prototype interface, B OLT . In this paper, we detail B OLT ’s design and implementation, describing how the system maps NL utterances to prevalent dashboard objectives and generates dashboard recommendations. Utilizing B OLT as a design probe, we validate the proposed idea of NL-based dashboard authoring through a preliminary user study. Based on the study feedback, we highlight promising application scenarios and future directions to support richer dashboard authoring workflows.
Studying Differential Mental Health Expressions in India

Psychosocial stressors and the symptomatology of mental disorders are known to vary with socio-cultural environment. Mental health expressions on social media, however, are primarily informed by studies in the WEIRD (Western, Educated, Industrial, Rich, and Democratic) contexts. In this paper, we analyze mental health posts on Reddit made by individuals in India, to identify variations in online depression language specific to the Indian context compared to users from the Rest of the World (ROW). Unlike in Western samples, mental health discussions in India additionally express sadness, use negation, are present-focused, and are related to work and achievement. {Illness} is exclusively correlated to India, reaffirming the link between somatic symptoms and mental disorders in Indian patients. Two clinical psychologists validated the findings from social media posts and found 95\% of the top-20 topics associated with mental health discussions as {prevalent} in Indians. Significant linguistic variations in online mental health-related language in India compared to ROW, highlight the need for precision culturally-aware mental health models. These findings have important implications for designing culturally appropriate interventions to reduce the growing diagnosis and treatment gap for mental disorders in India.
Odd Cycle Transversal on P5-free Graphs in Polynomial Time

An independent set in a graph G is a set of pairwise non-adjacent vertices. A graph $G$ is bipartite if its vertex set can be partitioned into two independent sets. In the Odd Cycle Transversal problem, the input is a graph $G$ along with a weight function $w$ associating a rational weight with each vertex, and the task is to find a smallest weight vertex subset $S$ in $G$ such that $G - S$ is bipartite; the weight of $S$, $w(S) = \sum_{v\in S} w(v)$. We show that Odd Cycle Transversal is polynomial-time solvable on graphs excluding $P_5$ (a path on five vertices) as an induced subgraph. The problem was previously known to be polynomial-time solvable on $P_4$-free graphs and NP-hard on $P_6$-free graphs [Dabrowski, Feghali, Johnson, Paesani, Paulusma and Rz\k{a}\. zewski, Algorithmica 2020]. Bonamy, Dabrowski, Feghali, Johnson and Paulusma [Algorithmica 2019] posed the existence of a polynomial-time algorithm on $P_5$-free graphs as an open problem, this was later re-stated by Rz\k{a}\. zewski [Dagstuhl Reports, 9(6): 2019] and by Chudnovsky, King, Pilipczuk, Rz\k{a}\. zewski, and Spirkl [SIDMA 2021], who gave an algorithm with running time $n^{O(\sqrt{n})}$.
On the Role of Similarity in Detecting Masquerading Files

Similarity has been applied to a wide range of security applications, typically used in machine learning models. We examine the problem posed by masquerading samples; that is samples crafted by bad actors to be similar or near identical to legitimate samples. We find that these samples potentially create significant problems for machine learning solutions. The primary problem being that bad actors can circumvent machine learning solutions by using masquerading samples. We then examine the interplay between digital signatures and machine learning solutions. In particular, we focus on executable files and code signing. We offer a taxonomy for masquerading files. We use a combination of similarity and clustering to find masquerading files. We use the insights gathered in this process to offer improvements to similarity based and machine learning security solutions.
Linear bandits with polylogarithmic minimax regret

We study a noise model for linear stochastic bandits for which the subgaussian noise parameter vanishes linearly as we select actions on the unit sphere closer and closer to the unknown vector. We introduce an algorithm for this problem that exhibits a minimax regret scaling as $\log^3(T)$ in the time horizon $T$, in stark contrast the square root scaling of this regret for typical bandit algorithms. Our strategy, based on weighted least-squares estimation, achieves the eigenvalue relation $\lambda_{\min} ( V_t ) = \Omega (\sqrt{\lambda_{\max}(V_t ) })$ for the design matrix $V_t$ at each time step $t$ through geometrical arguments that are independent of the noise model and might be of independent interest. This allows us to tightly control the expected regret in each time step to be of the order $O(\frac1{t})$, leading to the logarithmic scaling of the cumulative regret.
Topological Phase Diagram of Optimally Shaken Honeycomb Lattices: A Dual Perspective from Stroboscopic and Non-Stroboscopic Floquet Hamiltonians

We present a direct comparison between the stroboscopic and non-stroboscopic effective approaches for ultracold atoms in shaken honeycomb lattices, focusing specifically on the optimal driving introduced by A. Verdeny and F. Mintert [Phys. Rev. A 92, 063615 (2015)]. In the fast-driving regime, we compare the effective non-stroboscopic Hamiltonian derived through a perturbative expansion with a non-perturbative calculation of the stroboscopic Floquet Hamiltonian, obtained through a simple non-perturbative numerical approach. We show that while some of the tunneling parameters are inherently model-dependent, the topological properties of the system remains robust, as expected. Using the same numerical approach we compute the topological phase diagram, arguing that it is most effectively represented in terms of the physical parameters characterizing the driving and the bare Hamiltonian -- parameters directly accessible in experiments -- rather than the emergent tunneling parameters, that depend on the model representation.
SlopeSeeker: A Search Tool for Exploring a Dataset of Quantifiable Trends

Natural language and search interfaces intuitively facilitate data exploration and provide visualization responses to diverse analytical queries based on the underlying datasets. However, these interfaces often fail to interpret more complex analytical intents, such as discerning subtleties and quantifiable differences between terms like"bump"and"spike"in the context of COVID cases, for example. We address this gap by extending the capabilities of a data exploration search interface for interpreting semantic concepts in time series trends. We first create a comprehensive dataset of semantic concepts by mapping quantifiable univariate data trends such as slope and angle to crowdsourced, semantically meaningful trend labels. The dataset contains quantifiable properties that capture the slope-scalar effect of semantic modifiers like"sharply"and"gradually,"as well as multi-line trends (e.g.,"peak,""valley"). We demonstrate the utility of this dataset in SlopeSeeker, a tool that supports natural language querying of quantifiable trends, such as"show me stocks that tanked in 2010. "The tool incorporates novel scoring and ranking techniques based on semantic relevance and visual prominence to present relevant trend chart responses containing these semantic trend concepts. In addition, SlopeSeeker provides a faceted search interface for users to navigate a semantic hierarchy of concepts from general trends (e.g.,"increase") to more specific ones (e.g.,"sharp increase"). A preliminary user evaluation of the tool demonstrates that the search interface supports greater expressivity of queries containing concepts that describe data trends. We identify potential future directions for leveraging our publicly available quantitative semantics dataset in other data domains and for novel visual analytics interfaces.
Refining Minimax Regret for Unsupervised Environment Design

In unsupervised environment design, reinforcement learning agents are trained on environment configurations (levels) generated by an adversary that maximises some objective. Regret is a commonly used objective that theoretically results in a minimax regret (MMR) policy with desirable robustness guarantees; in particular, the agent's maximum regret is bounded. However, once the agent reaches this regret bound on all levels, the adversary will only sample levels where regret cannot be further reduced. Although there are possible performance improvements to be made outside of these regret-maximising levels, learning stagnates. In this work, we introduce Bayesian level-perfect MMR (BLP), a refinement of the minimax regret objective that overcomes this limitation. We formally show that solving for this objective results in a subset of MMR policies, and that BLP policies act consistently with a Perfect Bayesian policy over all levels. We further introduce an algorithm, ReMiDi, that results in a BLP policy at convergence. We empirically demonstrate that training on levels from a minimax regret adversary causes learning to prematurely stagnate, but that ReMiDi continues learning.
Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations

Existing retrieval-based methods have made significant strides in maintaining long-term conversations. However, these approaches face challenges in memory database management and accurate memory retrieval, hindering their efficacy in dynamic, real-world interactions. This study introduces a novel framework, COmpressive Memory-Enhanced Dialogue sYstems (COMEDY), which eschews traditional retrieval modules and memory databases. Instead, COMEDY adopts a ''One-for-All'' approach, utilizing a single language model to manage memory generation, compression, and response generation. Central to this framework is the concept of compressive memory, which intergrates session-specific summaries, user-bot dynamics, and past events into a concise memory format. To support COMEDY, we curated a large-scale Chinese instruction-tuning dataset, Dolphin, derived from real user-chatbot interactions. Comparative evaluations demonstrate COMEDY's superiority over traditional retrieval-based methods in producing more nuanced and human-like conversational experiences. Our codes are available at https://github.com/nuochenpku/COMEDY.