Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomalous Hall effect in thin bismuth

Bismuth, the heaviest of all group V elements with strong spin-orbit coupling, is famously known to exhibit many interesting transport properties, and effects such as Shubnikov-de Haas and de Haas-van Alphen were first revealed in its bulk form. However, the transport properties have not yet been fully explored experimentally in thin bismuth nor in its 2D limit. In this work, bismuth flakes with average thicknesses ranging from 29 to 69 nm were mechanically exfoliated by a micro-trench technique and were used to fabricate four-point devices. Due to mixing of components, Onsager's relations were used to extract the longitudinal ($R_{xx}$) and Hall ($R_{xy}$) resistances where the latter shows a Hall anomaly that is consistent with the Anomalous Hall Effect (AHE). Our work strongly suggests that that there could be a hidden mechanism for time-reversal symmetry breaking in pure bismuth thin films.
Comparative Analysis of XGBoost and Minirocket Algortihms for Human Activity Recognition

Human Activity Recognition (HAR) has been extensively studied, with recent emphasis on the implementation of advanced Machine Learning (ML) and Deep Learning (DL) algorithms for accurate classification. This study investigates the efficacy of two ML algorithms, eXtreme Gradient Boosting (XGBoost) and MiniRocket, in the realm of HAR using data collected from smartphone sensors. The experiments are conducted on a dataset obtained from the UCI repository, comprising accelerometer and gyroscope signals captured from 30 volunteers performing various activities while wearing a smartphone. The dataset undergoes preprocessing, including noise filtering and feature extraction, before being utilized for training and testing the classifiers. Monte Carlo cross-validation is employed to evaluate the models' robustness. The findings reveal that both XGBoost and MiniRocket attain accuracy, F1 score, and AUC values as high as 0.99 in activity classification. XGBoost exhibits a slightly superior performance compared to MiniRocket. Notably, both algorithms surpass the performance of other ML and DL algorithms reported in the literature for HAR tasks. Additionally, the study compares the computational efficiency of the two algorithms, revealing XGBoost's advantage in terms of training time. Furthermore, the performance of MiniRocket, which achieves accuracy and F1 values of 0.94, and an AUC value of 0.96 using raw data and utilizing only one channel from the sensors, highlights the potential of directly leveraging unprocessed signals. It also suggests potential advantages that could be gained by utilizing sensor fusion or channel fusion techniques. Overall, this research sheds light on the effectiveness and computational characteristics of XGBoost and MiniRocket in HAR tasks, providing insights for future studies in activity recognition using smartphone sensor data.
The First Place Solution of WSDM Cup 2024: Leveraging Large Language Models for Conversational Multi-Doc QA

Conversational multi-doc question answering aims to answer specific questions based on the retrieved documents as well as the contextual conversations. In this paper, we introduce our winning approach for the"Conversational Multi-Doc QA"challenge in WSDM Cup 2024, which exploits the superior natural language understanding and generation capability of Large Language Models (LLMs). We first adapt LLMs to the task, then devise a hybrid training strategy to make the most of in-domain unlabeled data. Moreover, an advanced text embedding model is adopted to filter out potentially irrelevant documents and several approaches are designed and compared for the model ensemble. Equipped with all these techniques, our solution finally ranked 1st place in WSDM Cup 2024, surpassing its rivals to a large extent. The source codes have been released at https://github.com/zhangzhao219/WSDM-Cup-2024.
Entropy production from waiting-time distributions for overdamped Langevin dynamics

For a Markovian dynamics on discrete states, the logarithmic ratio of waiting-time distributions between two successive, instantaneous transitions in forward and backward direction is a measure of time-irreversibility. It thus serves as an entropy estimator, which is exact in the case of a uni-cyclic network. We adopt this framework to overdamped Langevin dynamics, where such transitions have finite duration. By introducing milestones based on the observation of a particle at at least three points, we identify an entropy estimator that becomes exact for driven motion along a one-dimensional potential.
Copula Approximate Bayesian Computation Using Distribution Random Forests

This paper introduces a novel Approximate Bayesian Computation (ABC) framework for estimating the posterior distribution and the maximum likelihood estimate (MLE) of the parameters of models defined by intractable likelihood functions. This framework can describe the possibly skewed and high dimensional posterior distribution by a novel multivariate copula-based distribution, based on univariate marginal posterior distributions which can account for skewness and be accurately estimated by Distribution Random Forests (DRF) while performing automatic summary statistics (covariates) selection, and on robustly estimated copula dependence parameters. The framework employs a novel multivariate mode estimator to perform for MLE and posterior mode estimation, and provides an optional step to perform model selection from a given set of models with posterior probabilities estimated by DRF. The posterior distribution estimation accuracy of the ABC framework is illustrated through simulation studies involving models with analytically computable posterior distributions, and involving exponential random graph and mechanistic network models which are each defined by an intractable likelihood from which it is costly to simulate large network datasets. Also, the framework is illustrated through analyses of large real-life networks of sizes ranging between 28,000 to 65.6 million nodes (between 3 million to 1.8 billion edges), including a large multilayer network with weighted directed edges.
The Design and Implementation of a High-Performance Log-Structured RAID System for ZNS SSDs

Zoned Namespace (ZNS) defines a new abstraction for host software to flexibly manage storage in flash-based SSDs as append-only zones. It also provides a Zone Append primitive to further boost the write performance of ZNS SSDs by exploiting intra-zone parallelism. However, making Zone Append effective for reliable and scalable storage, in the form of a RAID array of multiple ZNS SSDs, is non-trivial since Zone Append offloads address management to ZNS SSDs and requires hosts to dedicatedly manage RAID stripes across multiple drives. We propose ZapRAID, a high-performance log-structured RAID system for ZNS SSDs by carefully exploiting Zone Append to achieve high write parallelism and lightweight stripe management. ZapRAID adopts a group-based data layout with a coarse-grained ordering across multiple groups of stripes, such that it can use small-size metadata for stripe management on a per-group basis under Zone Append. It further adopts hybrid data management to simultaneously achieve intra-zone and inter-zone parallelism through a careful combination of both Zone Append and Zone Write primitives. We evaluate ZapRAID using microbenchmarks, trace-driven experiments, and real-application experiments. Our evaluation results show that ZapRAID achieves high write throughput and maintains high performance in normal reads, degraded reads, crash recovery, and full-drive recovery.
Cause and Effect: Can Large Language Models Truly Understand Causality?

With the rise of Large Language Models(LLMs), it has become crucial to understand their capabilities and limitations in deciphering and explaining the complex web of causal relationships that language entails. Current methods use either explicit or implicit causal reasoning, yet there is a strong need for a unified approach combining both to tackle a wide array of causal relationships more effectively. This research proposes a novel architecture called Context Aware Reasoning Enhancement with Counterfactual Analysis(CARE CA) framework to enhance causal reasoning and explainability. The proposed framework incorporates an explicit causal detection module with ConceptNet and counterfactual statements, as well as implicit causal detection through LLMs. Our framework goes one step further with a layer of counterfactual explanations to accentuate LLMs understanding of causality. The knowledge from ConceptNet enhances the performance of multiple causal reasoning tasks such as causal discovery, causal identification and counterfactual reasoning. The counterfactual sentences add explicit knowledge of the not caused by scenarios. By combining these powerful modules, our model aims to provide a deeper understanding of causal relationships, enabling enhanced interpretability. Evaluation of benchmark datasets shows improved performance across all metrics, such as accuracy, precision, recall, and F1 scores. We also introduce CausalNet, a new dataset accompanied by our code, to facilitate further research in this domain.
GhCPK28 negatively regulates the immune response by phosphorylating GhTIFY3b

The soil-borne fungus Verticillium dahliae (V. dahliae) seriously inhibits cotton (Gossypium hirsutum) growth and productivity. The immune system of cotton against this pathogen is largely unknown. Here we investigated the involvement of Ca2+-dependent protein kinases (CPKs) in this immunity. One CPK coding gene GhCPK28 had reduced expression after infection by V. dahliae in cotton. Knocking down of the GhCPK28 by virus induced gene silencing led to enhanced resistance to V. dahliae which is accompanied by a higher ROS accumulation and systemic acquired response. GhCPK28 was found to phosphorylate a TIFY family protein GhTIFY3b and reduce its stability. Silencing GhTIFY3b increased the susceptibility of cotton to V. dahliae. Together, this study indicate that GhCPK28 is a negative regulator of defense against V. dahliae infection and the GhTIFY3b might be a target protein of its activity. It sheds light on the immune system against V. dahliae and provides candidate genes for improving resistance against Verticillium wilt in cotton.
KSR1 regulates small-cell lung carcinoma tumor initiation and therapy resistance

Small-cell lung cancer (SCLC) is designated a recalcitrant cancer due to its five-year relative survival rate of less than 7%. First line SCLC treatment has not changed in the last 40 years. The NeuroD1 subtype of SCLC (SCLC-N) commonly harbors MYC amplifications and other hallmarks of aggressive behavior. Finding novel therapeutic options that effectively eliminate residual disease observed after initial response to therapy is essential to improving SCLC patient outcome. Tumor-initiating cells (TICs) are reported as the sanctuary population within the bulk tumor responsible for therapeutic resistance and relapse. In contrast to earlier studies in which ERK activation is reported to be inhibitory to growth and tumor development, we show that KSR1 signaling is conserved in SCLC-N and that it regulates tumor initiation through ERK. Thus, KSR1 function in SCLC-N serves as a novel model for understanding the role of KSR1-dependent signaling in normal and malignant tissues. We further show that KSR1 mediates cisplatin resistance in SCLC-N cells. CRISPR/Cas9-mediated KSR1 knockout causes a dramatic increase in sensitivity to cisplatin and is coincident with a marked decrease in TICs, indicating that targeting KSR1 might be selectively toxic to cells responsible for therapeutic resistance and tumor initiation. Our data show that KSR1, a molecular scaffold for the Raf/MEK/ERK signaling cascade, is critical for tumor initiation and clonogenicity, both in vitro and in vivo in the highly aggressive, metastatic and therapy resistant NeuroD1 subtype of SCLC. These findings shed light on a key distinct protein responsible for regulation in SCLC-N tumors, and a potential subtype specific therapeutic target.
Prediction of neural activity in connectome-constrained recurrent networks

We develop a theory of connectome-constrained neural networks in which a “student” network is trained to reproduce the activity of a ground-truth “teacher”, representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here both networks have the same connectivity but they have different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that a connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory can also prioritize which neurons to record from to most efficiently infer unmeasured network activity. Our analysis shows that the solution spaces of connectome-constrained and unconstrained models are qualitatively different, and provides a framework to determine when such models yield consistent dynamics.
Controls of inherited lithospheric heterogeneity on rift linkage: Numerical and analog models of interaction between the Kenyan and Ethiopian rifts across the Turkana depression

Inherited rheological structures in the lithosphere are expected to have large impact on the architecture of continental rifts. The Turkana depression in the East African Rift connects the Main Ethiopian Rift to the north with the Kenya rift in the south. This region is characterized by a NW‐SE trending band of thinned crust inherited from a Mesozoic rifting event, which is cutting the present‐day N‐S rift trend at high angle. In striking contrast to the narrow rifts in Ethiopia and Kenya, extension in the Turkana region is accommodated in subparallel deformation domains that are laterally distributed over several hundred kilometers. We present both analog experiments and numerical models that reproduce the along‐axis transition from narrow rifting in Ethiopia and Kenya to a distributed deformation within the Turkana depression. Similarly to natural observations, our models show that the Ethiopian and Kenyan rifts bend away from each other within the Turkana region, thus forming a right‐lateral step over and avoiding a direct link to form a continuous N‐S depression. The models reveal five potential types of rift linkage across the preexisting basin: three types where rifts bend away from the inherited structure connecting via a (1) wide or (2) narrow rift or by (3) forming a rotating microplate, (4) a type where rifts bend towards it, and (5) straight rift linkage. The fact that linkage type 1 is realized in the Turkana region provides new insights on the rheological configuration of the Mesozoic rift system at the onset of the recent rift episode.
A Caenorhabditis elegans based system for high-throughput functional phenotyping of human gut microbiota

Bottoms-up approach of mono or poly colonizing microbes in germfree model is an important tool for mechanistic understanding of human gut microbiota. However, doing this in models such as germfree mouse is expensive and time consuming. To address this problem, we developed a Caenorhabditis elegans based screening system. We used a gut microbiota culture collection that represents more than 70% functional capacity of the human gut microbiome to anaerobically colonize C. elegans. We chose colonization resistance as the phenotype of the microbiome for further screening and found that half of the strains, previously identified in vitro as inhibiting Clostridioides difficile, also did so in C. elegans. When validated using germ-free mouse model, results were in concordance with that obtained from C. elegans model. Our system therefore could be used for screening large number of bacterial species to better understand host-microbiome interaction.
The energy metabolism of Cupriavidus necator in different trophic conditions

The ‘knallgas’ bacterium Cupriavidus necator is attracting interest of the biotech industry due to its extremely versatile metabolism. C. necator can use hydrogen or formic acid as an energy source, fixes CO2 via the Calvin–Benson–Bassham (CBB) cycle, and grows well on various other organic acids and sugars. Its three partite genome is notable for its size and manifold duplications of key genes (CBB cycle, hydrogenases, nitrate reductases). Comparatively little is known about which of these enzymes and their cofactors are actually utilized for growth on different energy sources. Here, we investigated the energy metabolism of C. necator H16 by growing a barcoded transposon knockout library on various substrates including hydrogen and formic acid. The fitness contribution of each gene was calculated as the degree of enrichment or depletion of its mutants. Clustering of gene fitness revealed several distinct pathways: 1) Some, but not all, Molybdenum Cofactor biosynthesis genes were essential for growth on formate and nitrate respiration. 2) Soluble formate dehydrogenase (FDH) was the dominant means of formate oxidation, not the membrane-bound FDH. 3) For hydrogenases, both soluble and membrane-bound enzymes were beneficial for lithoautotrophic growth. 4) Of the six terminal respiratory complexes in C. necator H16, only some are used and utilization depends on the energy source. 5) Deletion of hydrogenase-related genes boosted growth, and we show that the relief from associated protein cost is responsible for this phenomenon. This study evaluates the contribution of each of C. necator’s genes to fitness in industrially relevant growth regimes. Our results illustrate the genomic redundancy of this generalist bacterium, and may inspire future strategies for strain engineering. Graphical Abstract Highlights a barcoded transposon library was used to assess gene fitness for hydrogenase, formate dehydrogenase and ETC complexes the utilization of terminal respiratory complexes is substrate specific soluble formate dehydrogenase is more important than membrane-bound FDH soluble hydrogenase and membrane-bound hydrogenase are equally utilized inactivation of hydrogenase and its accessory genes leads to faster growth further experiments demonstrate that this is caused by the relief of protein burden
(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over diagonal linear networks. We prove the convergence of GD and SGD with macroscopic stepsizes in an overparametrised regression setting and characterise their solutions through an implicit regularisation problem. Our crisp characterisation leads to qualitative insights about the impact of stochasticity and stepsizes on the recovered solution. Speciﬁcally, we show that large stepsizes consistently beneﬁt SGD for sparse regression problems, while they can hinder the recovery of sparse solutions for GD. These eﬀects are magniﬁed for stepsizes in a tight window just below the divergence threshold, in the “edge of stability” regime. Our ﬁndings are supported by experimental results.
Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation

Gradual Domain Adaptation (GDA), in which the learner is provided with additional intermediate domains, has been theoretically and empirically studied in many contexts. Despite its vital role in security-critical scenarios, the adversarial robustness of the GDA model remains unexplored. In this paper, we adopt the effective gradual self-training method and replace vanilla self-training with adversarial self-training (AST). AST first predicts labels on the unlabeled data and then adversarially trains the model on the pseudo-labeled distribution. Intriguingly, we find that gradual AST improves not only adversarial accuracy but also clean accuracy on the target domain. We reveal that this is because adversarial training (AT) performs better than standard training when the pseudo-labels contain a portion of incorrect labels. Accordingly, we first present the generalization error bounds for gradual AST in a multiclass classification setting. We then use the optimal value of the Subset Sum Problem to bridge the standard error on a real distribution and the adversarial error on a pseudo-labeled distribution. The result indicates that AT may obtain a tighter bound than standard training on data with incorrect pseudo-labels. We further present an example of a conditional Gaussian distribution to provide more insights into why gradual AST can improve the clean accuracy for GDA.
Adversarial Robustness through Random Weight Sampling

Deep neural networks have been found to be vulnerable in a variety of tasks. Ad-versarial attacks can manipulate network outputs, resulting in incorrect predictions. Adversarial defense methods aim to improve the adversarial robustness of networks by countering potential attacks. In addition to traditional defense approaches, randomized defense mechanisms have recently received increasing attention from researchers. These methods introduce different types of perturbations during the inference phase to destabilize adversarial attacks. Although promising empirical re-sults have been demonstrated by these approaches, the defense performance is quite sensitive to the randomness parameters, which are always manually tuned without further analysis. On the contrary, we propose incorporating random weights into the optimization to exploit the potential of randomized defense fully. To perform better optimization of randomness parameters, we conduct a theoretical analysis of the connections between randomness parameters and gradient similarity as well as natural performance. From these two aspects, we suggest imposing theoretically-guided constraints on random weights during optimizations, as these weights play a critical role in balancing natural performance and adversarial robustness. We derive both the upper and lower bounds of random weight parameters by considering prediction bias and gradient similarity. In this study, we introduce the Constrained Trainable Random Weight (CTRW), which adds random weight parameters to the optimization and includes a constraint guided by the upper and lower bounds to achieve better trade-offs between natural and robust accuracy. We evaluate the effectiveness of CTRW on several datasets and benchmark convolutional neural networks. Our results indicate that our model achieves a robust accuracy approximately 16% to 17% higher than the baseline model under PGD-20 and 22% to 25% higher on Auto Attack.
LES-tree : A Spatio-temporal Access Method based on Snapshots and Events ∗

This work presents a new access method (LES-tree) for spatio-temporal databases that handles discrete change events over objects’ spatial attributes. The main characteristic of this structure is that, in addition to the traditional database snapshots, LEStree explicitly stores the events in log structures associated with space partitions. The definition of this new access method aims to extend capabilities of current spatio-temporal access methods to queries on events, while competing with current structures for traditional time-slice and time-interval queries. The paper describes the structure and presents favorable experimental cost analyses of the structure. ∗This work was partially funded by Millennium Nucleus Center for Web Research, grant P04-067-F, Mideplan, Chile. Gilberto Gutiérrez was also funded by research grant 073218 4/R, University of B́ıo-B́ıo, Chile