Miracle-dz/UniFAR-FTUs huggingface dataset:
Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust 3D Object Detection

LiDAR-based 3D object detection models inevitably struggle under rainy conditions due to the degraded and noisy scanning signals. Previous research has attempted to address this by simulating the noise from rain to improve the robustness of detection models. However, significant disparities exist between simulated and actual rain-impacted data points. In this work, we propose a novel rain simulation method, termed DRET, that unifies Dynamics and Rainy Environment Theory to provide a cost-effective means of expanding the available realistic rain data for 3D detection training. Furthermore, we present a Sunny-to-Rainy Knowledge Distillation (SRKD) approach to enhance 3D detection under rainy conditions. Extensive experiments on the Waymo-Open-Dataset show that, when combined with the state-of-the-art DSVT model and other classical 3D detectors, our proposed framework demonstrates significant detection accuracy improvements, without losing efficiency. Remarkably, our framework also improves detection capabilities under sunny conditions, therefore offering a robust solution for 3D detection regardless of whether the weather is rainy or sunny.
Local Fourier uniformity of higher divisor functions on average

Let $\tau_k$ be the $k$-fold divisor function. By constructing an approximant of $\tau_k$, denoted as $\tau_k^*$, which is a normalized truncation of the $k$-fold divisor function, we prove that when $\exp\left(C\log^{1/2}X(\log\log X)^{1/2}\right)\leq H\leq X$ and $C>0$ is sufficiently large, the following estimate holds for almost all $x\in[X,2X]$: \[ \Big|\sum_{x<n\leq x+H}(\tau_k(n)-\tau_k^*(n)) e(\alpha_dn^d+\cdots+\alpha_1n)\Big|=o(H\log^{k-1}X), \] where $\alpha_1, \dots, \alpha_d\in \mathbb{R}$ are arbitrary frequencies.
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation

We introduce Bonito, an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning. Our goal is to enable zero-shot task adaptation of large language models on users' specialized, private data. We train Bonito on a new large-scale dataset with 1.65M examples created by remixing existing instruction tuning datasets into meta-templates. The meta-templates for a dataset produce training examples where the input is the unannotated text and the task attribute and the output consists of the instruction and the response. We use Bonito to generate synthetic tasks for seven datasets from specialized domains across three task types -- yes-no question answering, extractive question answering, and natural language inference -- and adapt language models. We show that Bonito significantly improves the average performance of pretrained and instruction tuned models over the de facto self supervised baseline. For example, adapting Mistral-Instruct-v2 and instruction tuned variants of Mistral and Llama2 with Bonito improves the strong zero-shot performance by 22.1 F1 points whereas the next word prediction objective undoes some of the benefits of instruction tuning and reduces the average performance by 0.8 F1 points. We conduct additional experiments with Bonito to understand the effects of the domain, the size of the training set, and the choice of alternative synthetic task generators. Overall, we show that learning with synthetic instruction tuning datasets is an effective way to adapt language models to new domains. The model, dataset, and code are available at https://github.com/BatsResearch/bonito.
On the relation between pseudocharacters and Chenevier's determinants

Consider a commutative unital ring $A$ and a unital $A$-algebra $R$. Let $d$ be a positive integer. Chenevier proved that when $(2d)! $ is invertible in $A$, the map associating to a determinant its trace is a bijection between $A$-valued $d$-dimensional determinants of $R$ and $A$-valued $d$-dimensional pseudocharacters of $R$. In this paper, we show that assuming $d! $ is invertible in $A$ is sufficient. This assumption is already made in the definition of a $d$-dimensional pseudocharacter. Our proof involves establishing a product formula for pseudocharacters, which might be of independent interest.
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks

Adversarial attacks in visual object tracking have significantly degraded the performance of advanced trackers by introducing imperceptible perturbations into images. These attack methods have garnered considerable attention from researchers in recent years. However, there is still a lack of research on designing adversarial defense methods specifically for visual object tracking. To address these issues, we propose an effective additional pre-processing network called DuaLossDef that eliminates adversarial perturbations during the tracking process. DuaLossDef is deployed ahead of the search branche or template branche of the tracker to apply defensive transformations to the input images. Moreover, it can be seamlessly integrated with other visual trackers as a plug-and-play module without requiring any parameter adjustments. We train DuaLossDef using adversarial training, specifically employing Dua-Loss to generate adversarial samples that simultaneously attack the classification and regression branches of the tracker. Extensive experiments conducted on the OTB100, LaSOT, and VOT2018 benchmarks demonstrate that DuaLossDef maintains excellent defense robustness against adversarial attack methods in both adaptive and non-adaptive attack scenarios. Moreover, when transferring the defense network to other trackers, it exhibits reliable transferability. Finally, DuaLossDef achieves a processing time of up to 5ms/frame, allowing seamless integration with existing high-speed trackers without introducing significant computational overhead. We will make our code publicly available soon.
Predicting Phase Transitions in PbTiO$_3$ using Zentropy

According to conventional X-ray measurements, PbTiO$_3$ undergoes a phase transition from a tetragonal ferroelectric phase to a cubic paraelectric phase at 763 K. However, x-ray absorption fine-structure (XAFS) measurements indicate that PbTiO$_3$ is tetragonal even after the phase transition has occurred. The difference in these results concerns the length scales accessible to each measurement technique: millimeters for X-ray and Angstroms for XAFS. For both of these measurements to be consistent, PbTiO$_3$ is macroscopically cubic but still locally tetragonal above the phase transition temperature. Despite this, most models, such as the Laundau-Ginsburg-Devonshire theory, are still unable to explain this phenomenon. Moreover, these methods involve model parameters fitted to experimental or theoretical data and do not consider other tetragonal configurations, such as domain walls, to predict the phase transition. In this study, we use our novel zentropy approach to predict the phase transition, which allows us to calculate the total entropy of a system without fitted parameters, taking only inputs from density functional theory calculations through energy-volume curves and phonon calculations. Our approach also considers the tetragonal 90{\deg} and 180{\deg} domain walls and the ferroelectric ground state in predicting the phase transition. The predicted phase transition using the metaGGA $r^2\text{SCAN}$ occurs at 830 K, showing good agreement with the experimental value of 763 K. It is the subject of future work to show that the statistical average of these tetragonal configurations at the phase transition will result in the cubic phase.
A Conditional Extension of the Park-Pham Theorem

Neglecting many motivating details for the Park-Pham theorem (previously known as the Kahn-Kalai conjecture), the result starts with a finite set $X$, a non-trivial upper set $\mathcal{F} \subseteq 2^X$, and a particular parameterized family of random variables $X_p$, then proceeds to provide an upper bound on the value $p_c(\mathcal{F})$ such that $\mathbb{P}(X_{p_c(\mathcal{F})} \in \mathcal{F}) = 1/2$. A natural question to ask is if there is an analog to the Park-Pham theorem for upper sets in finite posets other than $2^X$ and other parameterized families of random variables taking values in them. In this short note, we show that there is, with minor adjustments, in at least one circumstance. This is done by producing a conditional version of the $\epsilon$-dependent form of the Park-Pham theorem, which may be interesting in its own right.
Invariance principle and McKean-Vlasov limit for randomized load balancing in heavy traffic

We consider a load balancing model where a Poisson stream of jobs arrive at a system of many servers whose service time distribution possesses a finite second moment. A small fraction of arrivals pass through the so called power-of-choice algorithm, which assigns a job to the shortest among $\ell$, $\ell\ge 2$, randomly chosen queues, and the remaining jobs are assigned to queues chosen uniformly at random. The system is analyzed at critical load in an asymptotic regime where both the number of servers and the usual heavy traffic parameter associated with individual queue lengths grow to infinity. The first main result is a hydrodynamic limit, where the empirical measure of the diffusively normalized queue lengths is shown to converge to a path in measure space whose density is given by the unique solution of a parabolic PDE with nonlocal coefficients. Further, two forms of an invariance principle are proved, corresponding to two different assumptions on the initial distribution, where individual normalized queue lengths converge weakly to solutions of SDE. In one of these results, the limit is given by a McKean-Vlasov SDE, and propagation of chaos holds. The McKean-Vlasov limit is closely related to limit results for Brownian particles on $\mathbb{R}_+$ interacting through their rank (with a specific interaction). However, an entirely different set of tools is required, as the collection of $n$ prelimit particles does not obey a Markovian evolution on $\mathbb{R}_+^n$.
OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction

This technical report presents our solution,"occTransformer"for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023. Our method builds upon the strong baseline BEVFormer and improves its performance through several simple yet effective techniques. Firstly, we employed data augmentation to increase the diversity of the training data and improve the model's generalization ability. Secondly, we used a strong image backbone to extract more informative features from the input data. Thirdly, we incorporated a 3D unet head to better capture the spatial information of the scene. Fourthly, we added more loss functions to better optimize the model. Additionally, we used an ensemble approach with the occ model BevDet and SurroundOcc to further improve the performance. Most importantly, we integrated 3D detection model StreamPETR to enhance the model's ability to detect objects in the scene. Using these methods, our solution achieved 49.23 miou on the 3D occupancy prediction track in the autonomous driving challenge.
Looking for Complexity at Phase Boundaries in Continuous Cellular Automata

One key challenge in Artificial Life is designing systems that display an emergence of complex behaviors. Many such systems depend on a high-dimensional parameter space, only a small subset of which displays interesting dynamics. Focusing on the case of continuous systems, we introduce the 'Phase Transition Finder'(PTF) algorithm, which can be used to efficiently generate parameters lying at the border between two phases. We argue that such points are more likely to display complex behaviors, and confirm this by applying PTF to Lenia showing it can increase the frequency of interesting behaviors more than two-fold, while remaining efficient enough for large-scale searches.
Weakly Private Information Retrieval from Heterogeneously Trusted Servers

We study the problem of weakly private information retrieval (PIR) when there is heterogeneity in servers' trustfulness under the maximal leakage (Max-L) metric and mutual information (MI) metric. A user wishes to retrieve a desired message from N non-colluding servers efficiently, such that the identity of the desired message is not leaked in a significant manner; however, some servers can be more trustworthy than others. We propose a code construction for this setting and optimize the probability distribution for this construction. For the Max-L metric, it is shown that the optimal probability allocation for the proposed scheme essentially separates the delivery patterns into two parts: a completely private part that has the same download overhead as the capacity-achieving PIR code, and a non-private part that allows complete privacy leakage but has no download overhead by downloading only from the most trustful server. The optimal solution is established through a sophisticated analysis of the underlying convex optimization problem, and a reduction between the homogeneous setting and the heterogeneous setting. For the MI metric, the homogeneous case is studied first for which the code can be optimized with an explicit probability assignment, while a closed-form solution becomes intractable for the heterogeneous case. Numerical results are provided for both cases to corroborate the theoretical analysis.
Spatiotemporal dynamics of NF-κB/Dorsal inhibitor IκBα/Cactus in Drosophila blastoderm embryos

The NF-κB signaling pathway is a key regulatory network in mammals that controls many cellular processes, including immunity and inflammation. Of particular note is the relationship between NF-κB and its inhibitor IκBα, which sequesters NF-κB to the cytoplasm of cells until needed. It is also known that IκBα can enter nuclei, disrupt NF-κB binding to DNA, and shuttle it out to again sequester NF-κB in the cytoplasm. In Drosophila melanogaster, a homologous system between the proteins Dorsal (homologous to NF-κB) and Cactus (homologous to IκBα) is important in embryo development, specifically in establishment of the Dorsal nuclear concentration gradient. Previous work suggests Cactus also enters the nucleus; mathematical models of the Dorsal gradient fail to accurately predict the normal range of the gradient without nuclear Cactus. However, direct, in vivo visualization of Cactus spatiotemporal dynamics, including its localization to the nuclei, has been difficult to gather. Previously, imaging Cactus in live embryos was complicated by rapid protein turnover, preventing fluorescent protein fusions from fully maturing. To address this, we used the CRISPR/Cas9 system to tag Cactus with the recently developed “LlamaTag” (LT), a genetically encodable nanobody from llamas that dynamically binds to GFP in vivo. We then employed standard confocal imaging, as well as advanced optical techniques such as raster image correlation spectroscopy (RICS) and fluorescent recovery after photobleaching (FRAP) to investigate the spatiotemporal distribution of Cactus-LlamaTag in Drosophila embryos at the blastoderm stage. Our results demonstrate that Cactus can be found in the nuclei of early embryos, consistent with its role as a transcription factor regulator. Moreover, by using the data from FRAP and RICS, we were able to estimate biophysical parameters of Cactus dynamics in vivo, including its nuclear transport rate constants and fraction bound to GFP. These data were further used to constrain a mathematical model that allowed us to infer experimentally inaccessible biophysical parameters, such as the concentration of Cact protein and the dissociation constant of LT and GFP. Our study provides new insights into the regulation of the NF-κB pathway in early Drosophila embryos and highlights the power of advanced optical techniques for investigating complex biological dynamics.
Promoter motifs facilitate as well as impede the emergence and evolution of gene expression

Gene regulation is essential for life and controlled by regulatory DNA. Mutations can modify the activity of regulatory DNA, and also create new regulatory DNA, a process called regulatory emergence. Non-regulatory and regulatory DNA contain motifs to which transcription factors may bind. Prokaryotic gene expression requires a stretch of regulatory DNA called a promoter, which contains two promoter motifs called -10 and -35 boxes. However, these motifs may occur in both promoters and non-regulatory DNA in multiple copies. It is possible that these motifs influence both the evolution and emergence of regulatory DNA, but this has never been thoroughly tested. To understand whether and how promoter motifs influence promoter emergence and evolution, we start from 25 DNA sequences enriched with -10 and -35 box motifs (promoter islands), mutagenize the sequences, and measure gene expression driven by 240’000 mutants. We find that the probability that mutations create an active promoter varies more than 200-fold, but is not correlated with the number of promoter motifs. In most mutants (∼67%), the weakening, strengthening, or emergence of a promoter is caused by mutations that modify already existing motifs. Notably, creating new motifs can either increase or decrease promoter activity. Our work demonstrates how promoter motifs influence promoter emergence and evolution. It has implications for predicting and understanding regulatory evolution, de-novo genes, and phenotypic evolution.
Directed biomechanical compressive forces enhance fusion efficiency in model placental trophoblast cultures

The syncytiotrophoblast is a multinucleated structure that arises from fusion of mononucleated cytotrophoblasts, to sheath the placental villi and regulate transport across the maternal-fetal interface. Here, we ask whether the dynamic mechanical forces that must arise during villous development might influence fusion, and explore this question using in vitro choriocarcinoma trophoblast models. We demonstrate that mechanical stress patterns arise around sites of localized fusion in cell monolayers, in patterns that match computational predictions of villous morphogenesis. We then externally apply these mechanical stress patterns to cell monolayers and demonstrate that equibiaxial compressive stresses (but not uniaxial or equibiaxial tensile stresses) enhance expression of the syndecan-1 marker of fusion. These findings suggest that the mechanical stresses that contribute towards sculpting the placental villi may also impact fusion in the developing tissue. We then extend this concept towards 3D cultures and demonstrate that fusion can be enhanced by applying low isometric compressive stresses to spheroid models, even in the absence of an inducing agent. These results indicate that mechanical stimulation is a potent activator of cellular fusion, suggesting novel avenues to improve experimental reproductive modelling, placental tissue engineering, and understanding disorders of pregnancy development.
Chlorpromazine inhibits EAG1 channels by altering the coupling between the PAS, CNBH and pore domains

EAG1 depolarization-activated potassium selective channels are important targets for treatment of cancer and neurological disorders. EAG1 channels are formed by a tetrameric subunit assembly with each subunit containing an N-terminal Per-Arnt-Sim (PAS) domain and C-terminal cyclic nucleotide-binding homology (CNBH) domain. The PAS and CNBH domains from adjacent subunits interact and form an intracellular tetrameric ring that regulates the EAG1 channel gating, including the movement of the voltage sensor domain (VSD) from closed to open states. Small molecule ligands can inhibit EAG1 channels by binding to their PAS domains. However, the allosteric pathways of this inhibition are not known. Here we show that chlorpromazine, a PAS domain small molecule binder, alters interactions between the PAS and CNBH domains and decreases the coupling between the intracellular tetrameric ring and the pore of the channel, while having little effect on the coupling between the PAS and VSD domains. In addition, chlorpromazine binding to the PAS domain did not alter Cole-Moore shift characteristic of EAG1 channels, further indicating that chlorpromazine has no effect on VSD movement from the deep closed to opened states. Our study provides a framework for understanding global pathways of EAG1 channel regulation by small molecule PAS domain binders.
Modeling gene-environment interactions in Parkinson’s Disease: Helicobacter pylori infection of Pink1−/− mice induces CD8 T cell-dependent motor and cognitive dysfunction

Parkinson’s disease (PD) is a chronic neurodegenerative disorder characterized by progressive loss of motor function. Diagnosis occurs late: after motor symptom development downstream of the irreparable loss of a large proportion of the dopaminergic neurons in the substantia nigra of the brain. Understanding PD pathophysiology in its pre-motor prodromal phase is needed for earlier diagnosis and intervention. Genetic risk factors, environmental triggers, and dysregulated immunity have all been implicated in PD development. Here, we demonstrate in a mouse model deficient in the PD-associated gene Pink, that infection with the human PD-associated gastric bacterium Helicobacter pylori leads to development of motor and cognitive signs resembling prodromal features of PD. This was also associated with proliferation and activation of primary mitochondria-reactive CD8 T cells and infiltration of CD8 T cells into the brain. Development of the motor and cognitive phenotypes in the infected Pink1−/− mice was abrogated when CD8 T cells were depleted prior to infection. We anticipate that this new model, which integrates genetic PD susceptibility, a PD-relevant environmental trigger, and specific immune changes that are required for symptom development, will be a valuable tool for increasing our understanding of this complex disease.
The Determinants of Plant Variety Protection Applications in China

Abstract China passed its Plant Variety Protection Act (PVPA) in 1997 which allowed us to do some initial testing of this proposition. We used a unique dataset on the applications of the new Plant Variety Protection (PVP), morphological characteristics, and institutional sources of all the important varieties of rice in three provinces of China to estimate the determinants of plant variety protection applications in China. The evidence suggests that both the government and private research programs are responding to economic and policy incentives and investing in plant variety protection as economists would expect profit maximizing firms to invest. The discussion of the evolution of the seed industry suggested that the combination of the new seed law in 2000 and the new plant variety protection regulation had changed the structure of the seed industry and provided an important incentive to invest in PVPs both by public research institutes and commercial firms. Finally, there is some preliminary evidence that private firms have smaller incentives to develop new varieties than to purchase the new varieties.
Synchronization of Complex Dynamical Networks via Event-Triggered Pinning Impulses

This article studies the synchronization problem of complex dynamical networks. The impulsive control method is considered with a novel event-triggered pinning algorithm. Sufficient conditions on the network topology are obtained to ensure network synchronization. It is shown that synchronization can be realized with a careful selection of the pinning nodes. Furthermore, an adaptive coupling strength is incorporated into the network to allow network synchronization with an arbitrary selection of the pinning nodes. An example of a network with node dynamics described by the Chen system is studied to demonstrate the theoretical results.