Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptomic Analysis Reveals the Mechanism of Picea crassifolia Survival for Alpine Treeline Condition

The physiological mechanisms driving treeline formation succession captured the attention of ecologists many years ago, yet they are still not fully understood. In this study, physiological parameters (soluble sugars, starch, and nitrogen) were investigated in combination with transcriptomic analysis in the treeline tree species Picea crassifolia. The study was conducted in the middle of Qilian Mountain Reserves, Gansu Province, China, within the elevation range of 2500–3300 m. The results showed that the concentrations of non-structural carbohydrates decreased with increasing elevation in the current-year needles and current-year branches, as well as in the coarse and fine roots. RNA-Seq demonstrated that 483 genes were upregulated and 681 were downregulated in the comparison of 2900 and 2500 m (2900 vs. 2500), 770 were upregulated and 1006 were downregulated in 3300 vs. 2500, and 282 were upregulated and 295 were downregulated in 3300 vs. 2900. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the differentially expressed genes were highly enriched in photosynthesis-related processes, carbon fixation and metabolism, and nitrogen metabolism. Furthermore, almost all photosynthesis-related genes were downregulated, whereas many genes involved in cuticle lipids and flavonoid biosynthesis were upregulated, contributing to the survival of P. crassifolia under the treeline condition. Thus, our study provided not only molecular evidence for carbon limitation hypothesis in treeline formation, but also a better understanding of the molecular mechanisms of treeline tree survival under adverse conditions.
Intimate functional interactions between TGS1 and the Smn complex revealed by an analysis of the Drosophila eye development

Trimethylguanosine synthase 1 (TGS1) is a conserved enzyme that mediates formation of the trimethylguanosine cap on several RNAs, including snRNAs and telomerase RNA. Previous studies have shown that TGS1 binds the Survival Motor Neuron (SMN) protein, whose deficiency causes spinal muscular atrophy (SMA). In addition, TGS1 depletion results in increased hTR levels and telomere elongation in human cells. Here, we analyzed the roles of the Drosophila orthologs of the human TGS1 and SMN genes. We show that the Drosophila TGS1 protein (dTgs1) physically interacts with all subunits of the Drosophila Smn complex (Smn, Gem2, Gem3, Gem4 and Gem5), and that a human TGS1 transgene rescues the mutant phenotype caused by dTgs1 loss. We demonstrate that both dTgs1 and Smn are required for viability of retinal progenitor cells and that downregulation of these genes leads to a reduced eye size. Importantly, overexpression of dTgs1 partially rescues the eye defects caused by Smn depletion, and vice versa. These results suggest that the Drosophila eye model can be exploited for screens aimed at the identification of genes and drugs that modify the phenotypes elicited by Tgs1 and Smn deficiency. These modifiers could help to devise new therapies for SMA and diseases caused by telomerase insufficiency.
Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

Time series forecasting is often fundamental to scientific and engineering problems and enables decision making. With ever increasing data set sizes, a trivial solution to scale up predictions is to assume independence between interacting time series. However, modeling statistical dependencies can improve accuracy and enable analysis of interaction effects. Deep learning methods are well suited for this problem, but multi-variate models often assume a simple parametric distribution and do not scale to high dimensions. In this work we model the multi-variate temporal dynamics of time series via an autoregressive deep learning model, where the data distribution is represented by a conditioned normalizing flow. This combination retains the power of autoregressive models, such as good performance in extrapolation into the future, with the flexibility of flows as a general purpose high-dimensional distribution model, while remaining computationally tractable. We show that it improves over the state-of-the-art for standard metrics on many real-world data sets with several thousand interacting time-series.
Gavial: Programming the web with multi-tier FRP

Developing web applications requires dealing with their distributed nature and the natural asynchronicity of user input and network communication. For facilitating this, different researchers have explored the combination of a multi-tier programming language and functional reactive programming. However, existing proposals take this approach only part of the way (some parts of the application remain imperative) or remain naive, with no regard for avoiding glitches across network communication, network traffic overhead, compatibility with common APIs like XMLHttpRequest etc. In this paper, we present Gavial: the first mature design and implementation of multi-tier FRP that allows constructing an entire web application as a functionally reactive program. By applying a number of new ideas, we demonstrate that multi-tier FRP can in fact deal realistically with important practical aspects of building web applications. At the same time, we retain the declarative nature of FRP, where behaviors and events have an intuitive, compositional semantics and a clear dependency structure.
Combining Parametric Land Surface Models with Machine Learning

A hybrid machine learning and process-based-modeling (PBM) approach is proposed and evaluated at a handful of AmeriFlux sites to simulate the top-layer soil moisture state. The Hybrid-PBM (HPBM) employed here uses the Noah land-surface model integrated with Gaussian Processes. It is designed to correct the model only in climatological situations similar to the training data else it reverts to the PBM. In this way, our approach avoids bad predictions in scenarios where similar training data is not available and incorporates our physical understanding of the system. Here we assume an autoregressive model and obtain out-of-sample results with upwards of a 3-fold reduction in the RMSE using a one-year leave-one-out cross-validation at each of the selected sites. A path is outlined for using hybrid modeling to build global land-surface models with the potential to significantly outperform the current state-of-the-art.
A redshift database towards the Shapley supercluster region

We present a database and catalogue of radial velocities of galaxies towards the region of the Shapley Supercluster (SSC) based on 18 129 measured velocities for 10 702 galaxies in the approximately 300 square degree area between 12h43m00s < RA < 14h17m00s and −23° 30′00″ > Dec > − 38° 30′00″. The database contains velocity measurements that have been reported in the literature up until 2015. It also includes 5084 velocities, corresponding to 4617 galaxies, observed by us at Las Campanas Observatory and Cerro Tololo Inter-American Observatory, which had not been reported individually until now. Of the latter, 2585 correspond to galaxies with no other previously published velocity measurement before 2015. Every galaxy in the velocity database has been identified with a galaxy extracted from the SuperCOSMOS photometric catalogues. We also provide a combined average velocity catalogue for all 10 702 galaxies with measured velocities, adopting the SuperCOSMOS positions as a homogeneous base. A general magnitude cut-off at R2 = 18.0 mag was adopted (with exceptions only for some of the new reported velocities). In general terms, we confirm the overall structure of the SSC as reported in earlier papers. However, the more extensive velocity data show finer structures, which is to be discussed in a future publication.
The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence

Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.
DSCEP: An Infrastructure for Distributed Semantic Complex Event Processing

Today most applications continuously produce information under the form of streams, due to the advent of the means of collecting data. Sensors and social networks collect an immense variety and volume of data, from different real-life situations and at a considerable velocity. Increasingly, applications require processing of heterogeneous data streams from different sources together with large background knowledge. To use only the information on the data stream is not enough for many use cases. Semantic Complex Event Processing (CEP) systems have evolved from the classical rule-based CEP systems, by integrating high-level knowledge representation and RDF stream processing using both the data stream and background static knowledge. Additionally, CEP approaches lack the capability to semantically interpret and analyze data, which Semantic CEP (SCEP) attempts to address. SCEP has several limitations; one of them is related to their high processing time. This paper provides a conceptual model and an implementation of an infrastructure for distributed SCEP, where each SCEP operator can process part of the data and send it to other SCEP operators in order to achieves some answer. We show that by splitting the RDF stream processing and the background knowledge using the concept of SCEP operators, it's possible to considerably reduce processing time.
Docking Haptics: Dynamic Combinations of Grounded and Worn Devices

Grounded haptic devices can provide a variety of forces but have limited working volumes. Wearable haptic devices operate over a large volume but are relatively restricted in the types of stimuli they can generate. We propose the concept of docking haptics, in which different types of haptic devices are dynamically docked at run time. This creates a hybrid system, where the potential feedback depends on the user’s location. We show a prototype docking haptic workspace, combining a grounded six degree-of-freedom force feedback arm with a hand exoskeleton. We are able to create the sensation of weight on the hand when it is within reach of the grounded device, but away from the grounded device, hand-referenced force feedback is still available.
Cosmic-Ray Anisotropies in Right Ascension Measured by the Pierre Auger Observatory

We present measurements of the large-scale cosmic-ray (CR) anisotropies in R.A. , using data collected by the surface detector array of the Pierre Auger Observatory over more than 14 yr. We determine the equatorial dipole component, , through a Fourier analysis in R.A. that includes weights for each event so as to account for the main detector-induced systematic effects. For the energies at which the trigger efficiency of the array is small, the “east–west” method is employed. Besides using the data from the array with detectors separated by 1500 m, we also include data from the smaller but denser subarray of detectors with 750 m separation, which allows us to extend the analysis down to ∼0.03 EeV. The most significant equatorial dipole amplitude obtained is that in the cumulative bin above 8 EeV, %, which is inconsistent with isotropy at the 6σ level. In the bins below 8 EeV, we obtain 99% CL upper bounds on d⊥ at the level of 1%–3%. At energies below 1 EeV, even though the amplitudes are not significant, the phases determined in most of the bins are not far from the R.A. of the Galactic center, at αGC = −94°, suggesting a predominantly Galactic origin for anisotropies at these energies. The reconstructed dipole phases in the energy bins above 4 EeV point instead to R.A. that are almost opposite to the Galactic center one, indicative of an extragalactic CR origin.
Frequency-based Search-control in Dyna

Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency. In particular, Dyna is an elegant model-based architecture integrating learning and planning that provides huge flexibility of using a model. One of the most important components in Dyna is called search-control, which refers to the process of generating state or state-action pairs from which we query the model to acquire simulated experiences. Search-control is critical in improving learning efficiency. In this work, we propose a simple and novel search-control strategy by searching high frequency regions of the value function. Our main intuition is built on Shannon sampling theorem from signal processing, which indicates that a high frequency signal requires more samples to reconstruct. We empirically show that a high frequency function is more difficult to approximate. This suggests a search-control strategy: we should use states from high frequency regions of the value function to query the model to acquire more samples. We develop a simple strategy to locally measure the frequency of a function by gradient and hessian norms, and provide theoretical justification for this approach. We then apply our strategy to search-control in Dyna, and conduct experiments to show its property and effectiveness on benchmark domains.
Cleaning Denial Constraint Violations through Relaxation

Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a priori knowledge of the inconsistencies and the query workload, thereby requiring effort on understanding and cleaning the data that is unnecessary for the analysis. We propose an approach that performs probabilistic repair of denial constraint violations on-demand, driven by the exploratory analysis that users perform. We introduce Daisy, a system that seamlessly integrates data cleaning into the analysis by relaxing query results. Daisy executes analytical query-workloads over dirty data by weaving cleaning operators into the query plan. Our evaluation shows that Daisy adapts to the workload and outperforms traditional offline cleaning on both synthetic and real-world workloads.
Consistency-Aware Multi-Channel Speech Enhancement Using Deep Neural Networks

This paper proposes a deep neural network (DNN)–based multichannel speech enhancement system in which a DNN is trained to maximize the quality of the enhanced time-domain signal. DNN-based multi-channel speech enhancement is often conducted in the time-frequency (T-F) domain because spatial filtering can be efficiently implemented in the T-F domain. In such a case, ordinary objective functions are computed on the estimated T-F mask or spectrogram. However, the estimated spectrogram is often inconsistent, and its amplitude and phase may change when the spectrogram is converted back to the time-domain. That is, the objective function does not evaluate the enhanced time-domain signal properly. To address this problem, we propose to use an objective function defined on the reconstructed time-domain signal. Specifically, speech enhancement is conducted by multi-channel Wiener filtering in the T-F domain, and its result is converted back to the time-domain. We propose two objective functions computed on the reconstructed signal where the first one is defined in the time-domain, and the other one is defined in the T-F domain. Our experiment demonstrates the effectiveness of the proposed system comparing to T-F masking and mask-based beamforming.
The Added Value of Coordinating Inverter Control

Coordinated photovoltaic inverter control with centralized coordination of curtailment can increase the amount of energy sent from low-voltage (LV) distribution networks to the grid while respecting voltage constraints. First, this article quantifies the improvement of such an approach relative to autonomous droop control, in terms of PV curtailment and line losses in balanced networks. It then extends the coordinated inverter control to unbalanced distribution networks. Finally, it formulates a control algorithm for different objectives such as the fairer distribution of PV curtailment and rewarding PV customers for utilizing the excess power locally. The coordinated inverter control algorithm is tested on the 114-node and 906-bus LV European test feeders with cable sizes between 50 mm2 and 240 mm2 and validated with reference to OpenDSS. The results demonstrate that coordinated inverter control is superior when applied to high impedance LV networks and LV networks constrained by the distribution transformer capacity limits compared to autonomous inverters. On the 95 mm2 overhead line, it yields a 2% increase on average in the utilized PV output with up to 5% increase for some PV locations at higher penetration levels. Up to a 20% increase in PV hosting capacity was observed for location scenarios with PV system clustering.
RL agents Implicitly Learning Human Preferences

In the real world, RL agents should be rewarded for fulfilling human preferences. We show that RL agents implicitly learn the preferences of humans in their environment. Training a classifier to predict if a simulated human's preferences are fulfilled based on the activations of a RL agent's neural network gets . 93 AUC. Training a classifier on the raw environment state gets only . 8 AUC. Training the classifier off of the RL agent's activations also does much better than training off of activations from an autoencoder. The human preference classifier can be used as the reward function of an RL agent to make RL agent more beneficial for humans.
Zero-Resource Cross-Domain Named Entity Recognition

Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains. However, collecting data for low-resource target domains is not only expensive but also time-consuming. Hence, we propose a cross-domain NER model that does not use any external resources. We first introduce a Multi-Task Learning (MTL) by adding a new objective function to detect whether tokens are named entities or not. We then introduce a framework called Mixture of Entity Experts (MoEE) to improve the robustness for zero-resource domain adaptation. Finally, experimental results show that our model outperforms strong unsupervised cross-domain sequence labeling models, and the performance of our model is close to that of the state-of-the-art model which leverages extensive resources.
Successive minima and asymptotic slopes in Arakelov Geometry

Let X be a normal and geometrically integral projective variety over a global field K and let D be an adelic Cartier divisor on X. We prove a conjecture of Chen, showing that the essential minimum ζess(D) of D equals its asymptotic maximal slope under mild positivity assumptions. As an application, we see that ζess(D) can be read on the Okounkov body of the underlying divisor D via the Boucksom–Chen concave transform. This gives a new interpretation of Zhang’s inequalities on successive minima and a criterion for equality generalizing to arbitrary projective varieties a result of Burgos Gil, Philippon and Sombra concerning toric metrized divisors on toric varieties. When applied to a projective space X = P K , our main result has several applications to the study of successive minima of hermitian vector spaces. We obtain an absolute transference theorem with a linear upper bound, answering a question raised by Gaudron. We also give new comparisons between successive slopes and absolute minima, extending results of Gaudron and Rémond. Beijing International Center for Mathematical Research, Peking University 5 Yi He Yuan Road, Beijing 100871, China francois.ballay@bicmr.pku. edu.cn http://bicmr.pku. edu.cn/~ballay
Layer-wise Pruning and Auto-tuning of Layer-wise Learning Rates in Fine-tuning of Deep Networks

Existing fine-tuning methods use a single learning rate over all layers. In this paper, first, we discuss that trends of layer-wise weight variations by fine-tuning using a single learning rate do not match the well-known notion that lower-level layers extract general features and higher-level layers extract specific features. Based on our discussion, we propose an algorithm that improves fine-tuning performance and reduces network complexity through layer-wise pruning and auto-tuning of layer-wise learning rates. Through in-depth experiments on image retrieval (CUB-200-2011, Stanford online products, and Inshop) and fine-grained classification (Stanford cars, Aircraft) datasets, the effectiveness of the proposed algorithm is verified.
How to cluster nearest unique nodes from different classes using JJCluster in Wisp application?

The work of finding the best place according to user preference is tedious task. It needs manual research and lot of intuitive processes to find the best location according to some earlier knowledge about the place. It is mainly about accessing publicly available spatial data, applying a simple algorithm to summarise the data according to given preferences, and visualising the result on a map. We introduced JJCluster to eliminate the rigorous way of researching about a place and visualizing the location in real time. This algorithm successfully finds the heart of a city when used in Wisp application. The main purpose of designing Wisp is to find the best location according to a set of preferences in a map. This has interesting application in finding the perfect location for a trip to unknown place which is nearest to a set of preferences. We also discussed the various optimization algorithms that are pioneer of today’s dynamic programming and the need for visualization to find patterns when the data is cluttered. Yet, this general cluster algorithm can be used in other areas where we can explore every possible preference to maximize its utility.
Phase Reconstruction Based On Recurrent Phase Unwrapping With Deep Neural Networks

Phase reconstruction, which estimates phase from a given amplitude spectrogram, is an active research field in acoustical signal processing with many applications including audio synthesis. To take advantage of rich knowledge from data, several studies presented deep neural network (DNN)–based phase reconstruction methods. However, the training of a DNN for phase reconstruction is not an easy task because phase is sensitive to the shift of a waveform. To overcome this problem, we propose a DNN-based two-stage phase reconstruction method. In the proposed method, DNNs estimate phase derivatives instead of phase itself, which allows us to avoid the sensitivity problem. Then, phase is recursively estimated based on the estimated derivatives, which is named recurrent phase unwrapping (RPU). The experimental results confirm that the proposed method outperformed the direct phase estimation by a DNN.