Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing universal phenomenology for biological cellular systems: an idiosyncratic review on evolutionary dimensional reduction

The possibility of establishing a macroscopic phenomenological theory for biological systems, akin to the well-established framework of thermodynamics, is briefly reviewed. We introduce the concept of an evolutionary fluctuation–response relationship, which highlights the tight correlation between the variance in phenotypic traits caused by genetic mutations and by internal noise. We provide a distribution theory that allows us to derive these relationships, which suggests that the changes in traits resulting from adaptation and evolution are considerably constrained within a lower-dimensional space. We explore the reasons behind this dimensional reduction, focusing on the constraints posed by the requirements for steady growth and robustness achieved through the evolutionary process. We draw support from recent laboratory and numerical experiments to substantiate our claims. The universality of evolutionary dimensional reduction is presented and potential theoretical formulations for it are discussed. We conclude by briefly considering the prospects of establishing a macroscopic framework that characterizes biological robustness and irreversibility in cell differentiation, as well as an ideal cell model.
Reflection-Equivariant Diffusion for 3D Structure Determination from Isotopologue Rotational Spectra in Natural Abundance

Structure determination is necessary to identify unknown organic molecules, such as those in natural products, forensic samples, the interstellar medium, and laboratory syntheses. Rotational spectroscopy enables structure determination by providing accurate 3D information about small organic molecules via their moments of inertia. Using these moments, Kraitchman analysis determines isotopic substitution coordinates, which are the unsigned $|x|,|y|,|z|$ coordinates of all atoms with natural isotopic abundance, including carbon, nitrogen, and oxygen. While unsigned substitution coordinates can verify guesses of structures, the missing $+/-$ signs make it challenging to determine the actual structure from the substitution coordinates alone. To tackle this inverse problem, we develop KREED (Kraitchman REflection-Equivariant Diffusion), a generative diffusion model that infers a molecule's complete 3D structure from its molecular formula, moments of inertia, and unsigned substitution coordinates of heavy atoms. KREED's top-1 predictions identify the correct 3D structure with>98% accuracy on the QM9 and GEOM datasets when provided with substitution coordinates of all heavy atoms with natural isotopic abundance. When substitution coordinates are restricted to only a subset of carbons, accuracy is retained at 91% on QM9 and 32% on GEOM. On a test set of experimentally measured substitution coordinates gathered from the literature, KREED predicts the correct all-atom 3D structure in 25 of 33 cases, demonstrating experimental applicability for context-free 3D structure determination with rotational spectroscopy.
Downscaling Using CDAnet Under Observational and Model Noises: The Rayleigh-Benard Convection Paradigm

Efficient downscaling of large ensembles of coarse-scale information is crucial in several applications, such as oceanic and atmospheric modeling. The determining form map is a theoretical lifting function from the low-resolution solution trajectories of a dissipative dynamical system to their corresponding fine-scale counterparts. Recently, a physics-informed deep neural network ("CDAnet") was introduced, providing a surrogate of the determining form map for efficient downscaling. CDAnet was demonstrated to efficiently downscale noise-free coarse-scale data in a deterministic setting. Herein, the performance of well-trained CDAnet models is analyzed in a stochastic setting involving (i) observational noise, (ii) model noise, and (iii) a combination of observational and model noises. The analysis is performed employing the Rayleigh-Benard convection paradigm, under three training conditions, namely, training with perfect, noisy, or downscaled data. Furthermore, the effects of noises, Rayleigh number, and spatial and temporal resolutions of the input coarse-scale information on the downscaled fields are examined. The results suggest that the expected l2-error of CDAnet behaves quadratically in terms of the standard deviations of the observational and model noises. The results also suggest that CDAnet responds to uncertainties similar to the theorized and numerically-validated CDA behavior with an additional error overhead due to CDAnet being a surrogate model of the determining form map.
Malicious Agent Detection for Robust Multi-Agent Collaborative Perception

Recently, multi-agent collaborative (MAC) perception has been proposed and outperformed the traditional single-agent perception in many applications, such as autonomous driving. However, MAC perception is more vulnerable to adversarial attacks than single-agent perception due to the information exchange. The attacker can easily degrade the performance of a victim agent by sending harmful information from a malicious agent nearby. In this paper, we extend adversarial attacks to an important perception task -- MAC object detection, where generic defenses such as adversarial training are no longer effective against these attacks. More importantly, we propose Malicious Agent Detection (MADE), a reactive defense specific to MAC perception that can be deployed by each agent to accurately detect and then remove any potential malicious agent in its local collaboration network. In particular, MADE inspects each agent in the network independently using a semi-supervised anomaly detector based on a double-hypothesis test with the Benjamini-Hochberg procedure to control the false positive rate of the inference. For the two hypothesis tests, we propose a match loss statistic and a collaborative reconstruction loss statistic, respectively, both based on the consistency between the agent to be inspected and the ego agent where our detector is deployed. We conduct comprehensive evaluations on a benchmark 3D dataset V2X-sim and a real-road dataset DAIR-V2X and show that with the protection of MADE, the drops in the average precision compared with the best-case"oracle"defender against our attack are merely 1.28% and 0.34%, respectively, much lower than 8.92% and 10.00% for adversarial training, respectively.
Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks

In this work, we present a post-processing solution to address the hubness problem in cross-modal retrieval, a phenomenon where a small number of gallery data points are frequently retrieved, resulting in a decline in retrieval performance. We first theoretically demonstrate the necessity of incorporating both the gallery and query data for addressing hubness as hubs always exhibit high similarity with gallery and query data. Second, building on our theoretical results, we propose a novel framework, Dual Bank Normalization (DBNorm). While previous work has attempted to alleviate hubness by only utilizing the query samples, DBNorm leverages two banks constructed from the query and gallery samples to reduce the occurrence of hubs during inference. Next, to complement DBNorm, we introduce two novel methods, dual inverted softmax and dual dynamic inverted softmax, for normalizing similarity based on the two banks. Specifically, our proposed methods reduce the similarity between hubs and queries while improving the similarity between non-hubs and queries. Finally, we present extensive experimental results on diverse language-grounded benchmarks, including text-image, text-video, and text-audio, demonstrating the superior performance of our approaches compared to previous methods in addressing hubness and boosting retrieval performance. Our code is available at https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval.
Detuning the BSW Effect with Longitudinal String Spreading

Black holes are interesting astrophysical objects that have been studied as systems sensitive to quantum gravitational data. The accelerated geometry in the exterior of extremal black holes can induce large center-of-mass energies between particles with particular momenta at the horizon. This is known as the Ba\~nados-Silk-West (BSW) effect. For point particles, the BSW effect requires tuning to have the collision coincide with the horizon. However, this tuning is relaxed for string-theoretic objects. String scattering amplitudes are large in the Regge limit, occurring at large center-of-mass energies and shallow scattering angles, parametrically surpassing quantum field theoretic amplitudes. In this limit, longitudinal string spreading is induced between strings with a large difference in light-cone momenta, and this spread can be used to 'detune' the BSW effect. With this in mind, quantum gravitational data, as described by string theory, may play an important role in near horizon dynamics of extremal Kerr black holes. Further, though it may be hard to realize astrophysically, this system acts as a natural particle accelerator for probing the nature of small-scale physics at Planckian energies.
Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling

Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on a wide range of tasks by scaling up parameter counts from O(10^9) to O(10^{12}) levels and further beyond. These large scales make it impossible to adapt and deploy fully specialized models given a task of interest. Parameter-efficient fine-tuning (PEFT) emerges as a promising direction to tackle the adaptation and serving challenges for such large models. We categorize PEFT techniques into two types: intrusive and non-intrusive. Intrusive PEFT techniques directly change a model's internal architecture. Though more flexible, they introduce significant complexities for training and serving. Non-intrusive PEFT techniques leave the internal architecture unchanged and only adapt model-external parameters, such as embeddings for input. In this work, we describe AdaLink as a non-intrusive PEFT technique that achieves competitive performance compared to SoTA intrusive PEFT (LoRA) and full model fine-tuning (FT) on various tasks. We evaluate using both text-only and multimodal tasks, with experiments that account for both parameter-count scaling and training regime (with and without instruction tuning).
Comparison of detachment in Ohmic plasmas with positive and negative triangularity

In recent years, negative triangularity (NT) has emerged as a potential high-confinement L-mode reactor solution. In this work, detachment is investigated using core density ramps in lower single null Ohmic L-mode plasmas across a wide range of upper, lower, and average triangularity (the mean of upper and lower triangularity: $\delta$) in the TCV tokamak. It is universally found that detachment is more difficult to access for NT shaping. The outer divertor leg of discharges with $\delta\approx -0.3$ could not be cooled to below $5~\mathrm{eV}$ through core density ramps alone. The behavior of the upstream plasma and geometrical divertor effects (e.g. a reduced connection length with negative lower triangularity) do not fully explain the challenges in detaching NT plasmas. Langmuir probe measurements of the target heat flux widths ($\lambda_q$) were constant to within 30% across an upper triangularity scan, while the spreading factor $S$ was lower by up to 50% for NT, indicating a generally lower integral Scrape-Off Layer width, $\lambda_{int}$. The line-averaged core density was typically higher for NT discharges for a given fuelling rate, possibly linked to higher particle confinement in NT. Conversely, the divertor neutral pressure and integrated particle fluxes to the targets were typically lower for the same line-averaged density, indicating that NT configurations may be closer to the sheath-limited regime than their PT counterparts, which may explain why NT is more challenging to detach.
Testing the cosmological principle with the Pantheon+ sample and the region-fitting method

The cosmological principle is fundamental to the standard cosmological model. It assumes that the Universe is homogeneous and isotropic on very large scales. As the basic assumption, it must stand the test of various observations. In this work, we investigated the properties of the Pantheon+ sample, including redshift distribution and position distribution, and we give its constraint on the flat Lambda CDM model: $ m $ = 0.36pm 0.02 and $H_ $ = 72.83pm 0.23 km s$^ $ Mpc$^ $. Then, using the region fitting (RF) method, we mapped the all-sky distribution of cosmological parameters ($ m $ and $H_ $) and find that the distribution significantly deviates from isotropy. A local matter underdensity region exists toward ($ circ circ $) as well as a preferred direction of the cosmic anisotropy ($ circ circ $) in galactic coordinates. Similar directions may imply that local matter density might be responsible for the anisotropy of the accelerated expansion of the Universe. Results of statistical isotropy analyses including Isotropy and Isotropy with real-data positions (RP) show high confidence levels. For the local matter underdensity, the statistical significances are 2.78sigma (isotropy) and 2.34sigma (isotropy RP). For the cosmic anisotropy, the statistical significances are 3.96sigma (isotropy) and 3.15sigma (isotropy RP). The comparison of these two kinds of statistical isotropy analyses suggests that inhomogeneous spatial distribution of real sample can increase the deviation from isotropy. The similar results and findings are also found from reanalyses of the low-redshift sample (lp+) and the lower screening angle ( = 60degr ), but with a slight decrease in statistical significance. Overall, our results provide clear indications for a possible cosmic anisotropy. This possibility must be taken seriously. Further testing is needed to better understand this signal.
Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling

Topic segmentation is critical for obtaining structured documents and improving downstream tasks such as information retrieval. Due to its ability of automatically exploring clues of topic shift from abundant labeled data, recent supervised neural models have greatly promoted the development of long document topic segmentation, but leaving the deeper relationship between coherence and topic segmentation underexplored. Therefore, this paper enhances the ability of supervised models to capture coherence from both logical structure and semantic similarity perspectives to further improve the topic segmentation performance, proposing Topic-aware Sentence Structure Prediction (TSSP) and Contrastive Semantic Similarity Learning (CSSL). Specifically, the TSSP task is proposed to force the model to comprehend structural information by learning the original relations between adjacent sentences in a disarrayed document, which is constructed by jointly disrupting the original document at topic and sentence levels. Moreover, we utilize inter- and intra-topic information to construct contrastive samples and design the CSSL objective to ensure that the sentences representations in the same topic have higher similarity, while those in different topics are less similar. Extensive experiments show that the Longformer with our approach significantly outperforms old state-of-the-art (SOTA) methods. Our approach improve $F_1$ of old SOTA by 3.42 (73.74 ->77.16) and reduces $P_k$ by 1.11 points (15.0 ->13.89) on WIKI-727K and achieves an average relative reduction of 4.3% on $P_k$ on WikiSection. The average relative $P_k$ drop of 8.38% on two out-of-domain datasets also demonstrates the robustness of our approach.
Improved Convergence Rate of Nested Simulation with LSE on Sieve

Nested simulation encompasses the estimation of functionals linked to conditional expectations through simulation techniques. In this paper, we treat conditional expectation as a function of the multidimensional conditioning variable and provide asymptotic analyses of general Least Squared Estimators on sieve, without imposing specific assumptions on the function's form. Our study explores scenarios in which the convergence rate surpasses that of the standard Monte Carlo method and the one recently proposed based on kernel ridge regression. We also delve into the conditions that allow for achieving the best possible square root convergence rate among all methods. Numerical experiments are conducted to support our statements.
T3P: Demystifying Low-Earth Orbit Satellite Broadband

The Internet is going through a massive infrastructural revolution with the advent of low-flying satellite networks, 5/6G, WiFi7, and hollow-core fiber deployments. While these networks could unleash enhanced connectivity and new capabilities, it is critical to understand the performance characteristics to efficiently drive applications over them. Low-Earth orbit (LEO) satellite mega-constellations like SpaceX Starlink aim to offer broad coverage and low latencies at the expense of high orbital dynamics leading to continuous latency changes and frequent satellite hand-offs. This paper aims to quantify Starlink's latency and its variations and components using a real testbed spanning multiple latitudes from the North to the South of Europe. We identify tail latencies as a problem. We develop predictors for latency and throughput and show their utility in improving application performance by up to 25%. We also explore how transport protocols can be optimized for LEO networks and show that this can improve throughput by up to 115% (with only a 5% increase in latency). Also, our measurement testbed with a footprint across multiple locations offers unique trigger-based scheduling capabilities that are necessary to quantify the impact of LEO dynamics.
On the estimation of persistence intensity functions and linear representations of persistence diagrams

The prevailing statistical approach to analyzing persistence diagrams is concerned with filtering out topological noise. In this paper, we adopt a different viewpoint and aim at estimating the actual distribution of a random persistence diagram, which captures both topological signal and noise. To that effect, Chazel and Divol (2019) proved that, under general conditions, the expected value of a random persistence diagram is a measure admitting a Lebesgue density, called the persistence intensity function. In this paper, we are concerned with estimating the persistence intensity function and a novel, normalized version of it -- called the persistence density function. We present a class of kernel-based estimators based on an i.i. d. sample of persistence diagrams and derive estimation rates in the supremum norm. As a direct corollary, we obtain uniform consistency rates for estimating linear representations of persistence diagrams, including Betti numbers and persistence surfaces. Interestingly, the persistence density function delivers stronger statistical guarantees.
Investigating Uncertainty Calibration of Aligned Language Models under the Multiple-Choice Setting

Despite the significant progress made in practical applications of aligned language models (LMs), they tend to be overconfident in output answers compared to the corresponding pre-trained LMs. In this work, we systematically evaluate the impact of the alignment process on logit-based uncertainty calibration of LMs under the multiple-choice setting. We first conduct a thoughtful empirical study on how aligned LMs differ in calibration from their pre-trained counterparts. Experimental results reveal that there are two distinct uncertainties in LMs under the multiple-choice setting, which are responsible for the answer decision and the format preference of the LMs, respectively. Then, we investigate the role of these two uncertainties on aligned LM's calibration through fine-tuning in simple synthetic alignment schemes and conclude that one reason for aligned LMs' overconfidence is the conflation of these two types of uncertainty. Furthermore, we examine the utility of common post-hoc calibration methods for aligned LMs and propose an easy-to-implement and sample-efficient method to calibrate aligned LMs. We hope our findings could provide insights into the design of more reliable alignment processes for LMs.
Rather a Nurse than a Physician - Contrastive Explanations under Investigation

Contrastive explanations, where one decision is explained in contrast to another, are supposed to be closer to how humans explain a decision than non-contrastive explanations, where the decision is not necessarily referenced to an alternative. This claim has never been empirically validated. We analyze four English text-classification datasets (SST2, DynaSent, BIOS and DBpedia-Animals). We fine-tune and extract explanations from three different models (RoBERTa, GTP-2, and T5), each in three different sizes and apply three post-hoc explainability methods (LRP, GradientxInput, GradNorm). We furthermore collect and release human rationale annotations for a subset of 100 samples from the BIOS dataset for contrastive and non-contrastive settings. A cross-comparison between model-based rationales and human annotations, both in contrastive and non-contrastive settings, yields a high agreement between the two settings for models as well as for humans. Moreover, model-based explanations computed in both settings align equally well with human rationales. Thus, we empirically find that humans do not necessarily explain in a contrastive manner.9 pages, long paper at ACL 2022 proceedings.
IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks

We introduce a novel approach to counter adversarial attacks, namely, image resampling. Image resampling transforms a discrete image into a new one, simulating the process of scene recapturing or rerendering as specified by a geometrical transformation. The underlying rationale behind our idea is that image resampling can alleviate the influence of adversarial perturbations while preserving essential semantic information, thereby conferring an inherent advantage in defending against adversarial attacks. To validate this concept, we present a comprehensive study on leveraging image resampling to defend against adversarial attacks. We have developed basic resampling methods that employ interpolation strategies and coordinate shifting magnitudes. Our analysis reveals that these basic methods can partially mitigate adversarial attacks. However, they come with apparent limitations: the accuracy of clean images noticeably decreases, while the improvement in accuracy on adversarial examples is not substantial. We propose implicit representation-driven image resampling (IRAD) to overcome these limitations. First, we construct an implicit continuous representation that enables us to represent any input image within a continuous coordinate space. Second, we introduce SampleNet, which automatically generates pixel-wise shifts for resampling in response to different inputs. Furthermore, we can extend our approach to the state-of-the-art diffusion-based method, accelerating it with fewer time steps while preserving its defense capability. Extensive experiments demonstrate that our method significantly enhances the adversarial robustness of diverse deep models against various attacks while maintaining high accuracy on clean images.