Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations

Although deep neural networks can achieve human-level performance on many object recognition benchmarks, prior work suggests that these same models fail to learn simple abstract relations, such as determining whether two objects are the same or different. Much of this prior work focuses on training convolutional neural networks to classify images of two same or two different abstract shapes, testing generalization on within-distribution stimuli. In this article, we comprehensively study whether deep neural networks can acquire and generalize same-different relations both within and out-of-distribution using a variety of architectures, forms of pretraining, and fine-tuning datasets. We find that certain pretrained transformers can learn a same-different relation that generalizes with near perfect accuracy to out-of-distribution stimuli. Furthermore, we find that fine-tuning on abstract shapes that lack texture or color provides the strongest out-of-distribution generalization. Our results suggest that, with the right approach, deep neural networks can learn generalizable same-different visual relations.
Study of residual artificial neural network for particle identification in the CEPC high-granularity calorimeter prototype

Particle Identification (PID) plays a central role in associating the energy depositions in calorimeter cells with the type of primary particle in a particle flow oriented detector system. In this paper, we propose novel PID methods based on the Residual Network (ResNet) architecture which enable the training of very deep networks, bypass the need to reconstruct feature variables, and ensure the generalization ability among various geometries of detectors, to classify electromagnetic showers and hadronic showers. Using Geant4 simulation samples with energy ranging from 5 GeV to 120 GeV, the efficacy of Residual Connections is validated and the performance of our model is compared with Boosted Decision Trees (BDT) and other pioneering Artificial Neural Network (ANN) approaches. In shower classification, we observe an improvement in background rejection over a wide range of high signal efficiency ($>95\%$). These findings highlight the prospects of ANN with Residual Blocks for imaging detectors in the PID task of particle physics experiments.
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning

Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish cooperative training without exposing their raw data or model parameters. VFL has gained significant attention for its research potential and real-world applications in recent years, but still faces substantial challenges, such as in defending various kinds of data inference and backdoor attacks. Moreover, most of existing VFL projects are industry-facing and not easily used for keeping track of the current research progress. To address this need, we present an extensible and lightweight VFL framework VFLAIR (available at https://github.com/FLAIR-THU/VFLAIR), which supports VFL training with a variety of models, datasets and protocols, along with standardized modules for comprehensive evaluations of attacks and defense strategies. We also benchmark 11 attacks and 8 defenses performance under different communication and model partition settings and draw concrete insights and recommendations on the choice of defense strategies for different practical VFL deployment scenario.
One For All & All For One: Bypassing Hyperparameter Tuning with Model Averaging For Cross-Lingual Transfer

Multilingual language models enable zero-shot cross-lingual transfer (ZS-XLT): fine-tuned on sizable source-language task data, they perform the task in target languages without labeled instances. The effectiveness of ZS-XLT hinges on the linguistic proximity between languages and the amount of pretraining data for a language. Because of this, model selection based on source-language validation is unreliable: it picks model snapshots with suboptimal target-language performance. As a remedy, some work optimizes ZS-XLT by extensively tuning hyperparameters: the follow-up work then routinely struggles to replicate the original results. Other work searches over narrower hyperparameter grids, reporting substantially lower performance. In this work, we therefore propose an unsupervised evaluation protocol for ZS-XLT that decouples performance maximization from hyperparameter tuning. As a robust and more transparent alternative to extensive hyperparameter tuning, we propose to accumulatively average snapshots from different runs into a single model. We run broad ZS-XLT experiments on both higher-level semantic tasks (NLI, extractive QA) and a lower-level token classification task (NER) and find that conventional model selection based on source-language validation quickly plateaus to suboptimal ZS-XLT performance. On the other hand, our accumulative run-by-run averaging of models trained with different hyperparameters boosts ZS-XLT performance and closely correlates with"oracle"ZS-XLT, i.e., model selection based on target-language validation performance.
Soil and Plant Nitrogen Pools as Related to Plant Diversity in an Experimental Grassland

Increasing plant species richness decreases soil NO 3 - concentrations in experimental plant mixtures, but the role of particular plant functional groups has remained unclear. Most analyses have focused on particular times of the year or were restricted to NO 3 - . We tested whether plant species richness or particular plant functional groups affect the size of plant-available N pools in soil (KCl-extractable NO 3 - , dissolved inorganic N and organic N [DON] and total dissolved N [TDN] in soil solution) and N concentrations and pools in aboveground biomass. Furthermore, we assessed seasonal variations in the effects of plant species richness and plant functional groups. The experimental grassland site had 86 plots with different combinations of numbers of species (1, 2, 4, 8, 16, and 60) and numbers of functional groups (1, 2, 3, and 4, being grasses, small nonlegume herbs, tall nonlegume herbs, and legumes). In the second year after establishment, increasing species richness reduced soil NO 3 - concentrations (ANOVA, 11% of sum of squares [SS]). The presence of legumes correlated positively with soil NO 3 - concentrations (17% of SS). The presence of grasses significantly decreased soil NO 3 - concentrations (11% of SS). Seasonality had no influence on the relationships between NO 3 - concentrations and species richness. Volume-weighted mean DON and TDN concentrations in soil solution correlated negatively with species richness. Nitrogen pools in plant mixture biomass correlated positively with species diversity (14% of SS), indicating that total N uptake increased with increasing diversity. We conclude that both diversity (either in species or functional groups) and functional composition of grassland mixtures are significant controls of soil and plant N pools. Plant communities with more diverse mixtures are liable to use limiting resources such as N more effectively.
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams

Test-time adaptation (TTA) aims to address distributional shifts between training and testing data using only unlabeled test data streams for continual model adaptation. However, most TTA methods assume benign test streams, while test samples could be unexpectedly diverse in the wild. For instance, an unseen object or noise could appear in autonomous driving. This leads to a new threat to existing TTA algorithms; we found that prior TTA algorithms suffer from those noisy test samples as they blindly adapt to incoming samples. To address this problem, we present Screening-out Test-Time Adaptation (SoTTA), a novel TTA algorithm that is robust to noisy samples. The key enabler of SoTTA is two-fold: (i) input-wise robustness via high-confidence uniform-class sampling that effectively filters out the impact of noisy samples and (ii) parameter-wise robustness via entropy-sharpness minimization that improves the robustness of model parameters against large gradients from noisy samples. Our evaluation with standard TTA benchmarks with various noisy scenarios shows that our method outperforms state-of-the-art TTA methods under the presence of noisy samples and achieves comparable accuracy to those methods without noisy samples. The source code is available at https://github.com/taeckyung/SoTTA .
The Crisscross and the Cup: Two Short 3-Twist Paper Moebius Bands

We introduce the crisscross and the cup, both of which are immersed $3$-twist polygonal paper Moebius band of aspect ratio $3$. We explain why these two objects are limits of smooth embedded paper Moebius bands having knotted boundary. We conjecture that any smooth embedded paper Moebius band with knotted boundary has aspect ratio greater than $3$. The crisscross is planar but the cup is not.
Finite homological dimension of Hom and vanishing of Ext

For finitely generated modules $M$ and $N $ over a commutative Noetherian local ring $R$, we give various sufficient criteria for detecting freeness of $M$ or $N$ via vanishing of some finitely many Ext modules $\textrm{Ext}^i_R(M,N)$ and finiteness of certain homological dimension of $\textrm{Hom}_R(M,N)$. Some of our results provide partial progress towards answering a question of Ghosh-Takahashi and also generalize their main results in many ways, for instance, by reducing the number of vanishing. Certain special cases of our results allow us to address the Auslander-Reiten conjecture for modules whose (self) dual has finite projective dimension. Among other things, the notion of residually faithful modules and certain variations of it play a crucial role in our study. As some applications, we provide affirmative answers to two questions raised by Tony Se on $n$-semidualizing modules. Namely, we show that over a local ring of depth $t$, every $(t-1)$-semidualizing module of finite G-dimension is free. Moreover, we establish that for normal domains which satisfy Serre's condition $(S_3)$ and are locally Gorenstein in co-dimension two, the class of $1$-semidualizing modules form a subgroup of the divisor class group. These two groups coincide when, in addition, the ring is locally regular in co-dimension two.
End-to-end Offline Reinforcement Learning for Glycemia Control

The development of closed-loop systems for glycemia control in type I diabetes relies heavily on simulated patients. Improving the performances and adaptability of these close-loops raises the risk of over-fitting the simulator. This may have dire consequences, especially in unusual cases which were not faithfully-if at all-captured by the simulator. To address this, we propose to use offline RL agents, trained on real patient data, to perform the glycemia control. To further improve the performances, we propose an end-to-end personalization pipeline, which leverages offline-policy evaluation methods to remove altogether the need of a simulator, while still enabling an estimation of clinically relevant metrics for diabetes.
Fast and Low-Cost Approximate Multiplier for FPGAs using Dynamic Reconfiguration

Multipliers are widely-used arithmetic operators in digital signal processing and machine learning circuits. Due to their relatively high complexity, they can have high latency and be a significant source of power consumption. One strategy to alleviate these limitations is to use approximate computing. This paper thus introduces an original FPGA-based approximate multiplier specifically optimized for machine learning computations. It utilizes dynamically reconfigurable lookup table (LUT) primitives in AMD-Xilinx technology to realize the core part of the computations. The paper provides an in-depth analysis of the hardware architecture, implementation outcomes, and accuracy evaluations of the multiplier proposed in INT8 precision. Implementation results on an AMD-Xilinx Kintex Ultrascale+ FPGA demonstrate remarkable savings of 64% and 67% in LUT utilization for signed multiplication and multiply-and-accumulation configurations, respectively, when compared to the standard Xilinx multiplier core. Accuracy measurements on four popular deep learning (DL) benchmarks indicate a minimal average accuracy decrease of less than 0.29% during post-training deployment, with the maximum reduction staying less than 0.33%. The source code of this work is available on GitHub.
Generating counterfactual explanations of tumor spatial proteomes to discover effective strategies for enhancing immune infiltration

The tumor microenvironment (TME) significantly impacts cancer prognosis due to its immune composition. While therapies for altering the immune composition, including immunotherapies, have shown exciting results for treating hematological cancers, they are less effective for immunologically-cold, solid tumors. Spatial omics technologies capture the spatial organization of the TME with unprecedented molecular detail, revealing the relationship between immune cell localization and molecular signals. Here, we formulate T-cell infiltration prediction as a self-supervised machine learning problem and develop a counterfactual optimization strategy that leverages large scale spatial omics profiles of patient tumors to design tumor perturbations predicted to boost T-cell infiltration. A convolutional neural network predicts T-cell distribution based on signaling molecules in the TME provided by imaging mass cytometry. Gradient-based counterfactual generation, then, computes perturbations predicted to boost T-cell abundance. We apply our framework to melanoma, colorectal cancer liver metastases, and breast tumor data, discovering combinatorial perturbations predicted to support T-cell infiltration across tens to hundreds of patients. This work presents a paradigm for counterfactual-based prediction and design of cancer therapeutics using spatial omics data.
Towards Open-World Co-Salient Object Detection with Generative Uncertainty-aware Group Selective Exchange-Masking

The traditional definition of co-salient object detection (CoSOD) task is to segment the common salient objects in a group of relevant images. This definition is based on an assumption of group consensus consistency that is not always reasonable in the open-world setting, which results in robustness issue in the model when dealing with irrelevant images in the inputting image group under the open-word scenarios. To tackle this problem, we introduce a group selective exchange-masking (GSEM) approach for enhancing the robustness of the CoSOD model. GSEM takes two groups of images as input, each containing different types of salient objects. Based on the mixed metric we designed, GSEM selects a subset of images from each group using a novel learning-based strategy, then the selected images are exchanged. To simultaneously consider the uncertainty introduced by irrelevant images and the consensus features of the remaining relevant images in the group, we designed a latent variable generator branch and CoSOD transformer branch. The former is composed of a vector quantised-variational autoencoder to generate stochastic global variables that model uncertainty. The latter is designed to capture correlation-based local features that include group consensus. Finally, the outputs of the two branches are merged and passed to a transformer-based decoder to generate robust predictions. Taking into account that there are currently no benchmark datasets specifically designed for open-world scenarios, we constructed three open-world benchmark datasets, namely OWCoSal, OWCoSOD, and OWCoCA, based on existing datasets. By breaking the group-consistency assumption, these datasets provide effective simulations of real-world scenarios and can better evaluate the robustness and practicality of models.
Adaptive Workload Distribution for Accuracy-aware DNN Inference on Collaborative Edge Platforms

DNN inference can be accelerated by distributing the workload among a cluster of collaborative edge nodes. Heterogeneity among edge devices and accuracy-performance trade-offs of DNN models present a complex exploration space while catering to the inference performance requirements. In this work, we propose adaptive workload distribution for DNN inference, jointly considering node-level heterogeneity of edge devices, and application-specific accuracy and performance requirements. Our proposed approach combinatorially optimizes heterogeneity-aware workload partitioning and dynamic accuracy configuration of DNN models to ensure performance and accuracy guarantees. We tested our approach on an edge cluster of Odroid XU4, Raspberry Pi4, and Jetson Nano boards and achieved an average gain of 41.52% in performance and 5.2% in output accuracy as compared to state-of-the-art workload distribution strategies.
xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection

Widely used learned metrics for machine translation evaluation, such as COMET and BLEURT, estimate the quality of a translation hypothesis by providing a single sentence-level score. As such, they offer little insight into translation errors (e.g., what are the errors and what is their severity). On the other hand, generative large language models (LLMs) are amplifying the adoption of more granular strategies to evaluation, attempting to detail and categorize translation errors. In this work, we introduce xCOMET, an open-source learned metric designed to bridge the gap between these approaches. xCOMET integrates both sentence-level evaluation and error span detection capabilities, exhibiting state-of-the-art performance across all types of evaluation (sentence-level, system-level, and error span detection). Moreover, it does so while highlighting and categorizing error spans, thus enriching the quality assessment. We also provide a robustness analysis with stress tests, and show that xCOMET is largely capable of identifying localized critical errors and hallucinations.
LICO: Explainable Models with Language-Image Consistency

Interpreting the decisions of deep learning models has been actively studied since the explosion of deep neural networks. One of the most convincing interpretation approaches is salience-based visual interpretation, such as Grad-CAM, where the generation of attention maps depends merely on categorical labels. Although existing interpretation methods can provide explainable decision clues, they often yield partial correspondence between image and saliency maps due to the limited discriminative information from one-hot labels. This paper develops a Language-Image COnsistency model for explainable image classification, termed LICO, by correlating learnable linguistic prompts with corresponding visual features in a coarse-to-fine manner. Specifically, we first establish a coarse global manifold structure alignment by minimizing the distance between the distributions of image and language features. We then achieve fine-grained saliency maps by applying optimal transport (OT) theory to assign local feature maps with class-specific prompts. Extensive experimental results on eight benchmark datasets demonstrate that the proposed LICO achieves a significant improvement in generating more explainable attention maps in conjunction with existing interpretation methods such as Grad-CAM. Remarkably, LICO improves the classification performance of existing models without introducing any computational overhead during inference. Source code is made available at https://github.com/ymLeiFDU/LICO.
Late-time data require smaller sound horizon at recombination

The Hubble constant problem is that the values of Hubble constant from the observation of cosmic microwave background assuming the LambdaCDM model disagrees with the values from direct measurements. This problem suggests some new physics beyond the LambdaCDM model. Typically there are two ways of reconciliation: one is the realization of smaller value of sound horizon at recombination, and the other is the modification of the way of expansion of the universe after recombination. In this letter we examine the latter possibility by comparing two typical phenomenological dark energy models with the distance-redshift relation provided by Pantheon catalogue of supernova observations and galaxy surveys by BOSS and eBOSS collaborations. Though these phenomenological dark energy models globally fit observations better than the LambdaCDM model, they are strongly disfavored by the distance-redshift relation as almost the same level as the LambdaCDM model defined by cosmic microwave background observations. The distance-redshift relation strongly suggests some new physics which realize smaller value of sound horizon at recombination.
Repetition In Repetition Out: Towards Understanding Neural Text Degeneration from the Data Perspective

There are a number of diverging hypotheses about the neural text degeneration problem, i.e., generating repetitive and dull loops, which makes this problem both interesting and confusing. In this work, we aim to advance our understanding by presenting a straightforward and fundamental explanation from the data perspective. Our preliminary investigation reveals a strong correlation between the degeneration issue and the presence of repetitions in training data. Subsequent experiments also demonstrate that by selectively dropping out the attention to repetitive words in training data, degeneration can be significantly minimized. Furthermore, our empirical analysis illustrates that prior works addressing the degeneration issue from various standpoints, such as the high-inflow words, the likelihood objective, and the self-reinforcement phenomenon, can be interpreted by one simple explanation. That is, penalizing the repetitions in training data is a common and fundamental factor for their effectiveness. Moreover, our experiments reveal that penalizing the repetitions in training data remains critical even when considering larger model sizes and instruction tuning.
Cold season CH4 and CO2 emission from boreal peat bogs (West Siberia): Winter fluxes and thaw activation dynamics

The conventional chamber technique was used to measure CH4 and CO2emission to the atmosphere from snow‐covered ombrotrophic bogs (57°N, 82°E, Plotnikovo, West Siberia). The average ± standard deviation values for CH4 and CO2fluxes in mid‐February were found to be equal mg m−2 d−15.0 ± 3.7 and 69±52, respectively. The contribution of cold season to annual methane fluxes varied from 3.5 to 11% depending on the calculation method and was similar to that found in Alaska and northern Minnesota. The vertical profiles of gases in snow were linear implying the applicability of the simple diffusion equation under steady state conditions. The diffusion reduction factor due to porous resistance and tortuousity of snowpack was 0.18 and 0.29 for methane and carbon dioxide, respectively. Thus snow forms only a passive cap which controls the gas concentration at the snow‐soil interface, while gas flux into the atmosphere is controlled by gas production in the soil. The fresh samples of frozen peat soil incubated under laboratory conditions at constant temperature −16°C displayed very slow, but steady respiration varied from 0.05 to 0.2 mg CO2−C d−1 dm−3depending on peat sampling depth. Although this activity was 200‐300 times lower than soil respiration in summertime, it was enough to support the observed in situ winter CO2 emission. The thaw and subsequent peat incubation at 15°C accelerated gas formation up to 2–5 mg CO2‐C and 1.2 mg CH4‐C h−1 dm−3of peat after 3–4 days of incubation followed by a decline by 1 order of magnitude and approaching a new steady state level. Although the mechanism of freeze‐thaw activation needs further clarification, it was nevertheless possible to simulate the observed activation dynamics by a mathematical model which accounts for the burst of microbial growth on nutrients released into soil from frost‐damaged cells.