Miracle-dz/UniFAR-FTUs huggingface dataset:
SPINS: A structure priors aided inertial navigation system

We propose a navigation system combining sensor‐aided inertial navigation and prior‐map‐based localization to improve the stability and accuracy of robot localization in structure‐rich environments. Specifically, we adopt point, line, and plane features in the navigation system to enhance the feature richness in low‐texture environments and improve the localization reliability. We additionally integrate structure prior information of the environments to constrain the localization drifts and improve the accuracy. The prior information is called structure priors and parameterized as low‐dimensional relative distances/angles between different geometric primitives. The localization is formulated as a graph‐based optimization problem that contains sliding‐window‐based variables and factors, including Inertial Measurement Unit, heterogeneous features, and structure priors. A limited number of structure priors are selected based on the information gain to alleviate the computation burden. Finally, the proposed framework is extensively tested on synthetic data, public data sets, and, more importantly, on the real Unmanned Aerial Vehicle flight data obtained from both indoor and outdoor inspection tasks. The results show that the proposed scheme can effectively improve the accuracy and robustness of localization for autonomous robots in civilian applications.
Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment

This paper proposes a deep learning approach to the adaptive and sequential beamforming design problem for the initial access phase in a mmWave environment with a single-path channel. For a single-user scenario where the problem is equivalent to designing the sequence of sensing beamformers to learn the angle of arrival (AoA) of the dominant path, we propose a novel deep neural network (DNN) that designs the adaptive sensing vectors sequentially based on the available information so far at the base station (BS). By recognizing that the AoA posterior distribution is a sufficient statistic for solving the initial access problem, we use the posterior distribution as the input to the proposed DNN for designing the adaptive sensing strategy. However, computing the posterior distribution can be computationally challenging when the channel fading coefficient is unknown. To address this issue, this paper proposes to use an estimate of the fading coefficient to compute an approximation of the posterior distribution. Further, this paper shows that the proposed DNN can deal with practical beamforming constraints such as the constant modulus constraint. Numerical results demonstrate that compared to the existing adaptive and non-adaptive beamforming schemes, the proposed DNN-based adaptive sensing strategy achieves a significantly better AoA acquisition performance.
Gradient-free quantum optimization on NISQ devices

Variational Quantum Eigensolvers (VQEs) have recently attracted considerable attention. Yet, in practice, they still suﬀer from the large quantum computational overhead in estimating cost function gradients for large parameter sets or resource-demanding reinforcement strategies. Here, we consider recent advances in weight-agnostic learning and propose a strategy that addresses the trade-oﬀ between ﬁnding appropriate circuit architectures and parameter tuning . We investigate the use of evolutionary algorithms which ﬁrst spawn circuit’s oﬀspring and then evaluate them via genetic competition, thus circumventing some of the previous issues common parameter based approaches. We test our methods both via simulation and on real quantum hardware and use them to solve the transverse ﬁeld Ising Hamiltonian and the Sherrington-Kirkpatrick spin model. We investigate which mutation operations most sig-niﬁcantly contribute to the optimization. The resulting insights lay out a path for further reﬁnement of gradient-free strategies. We test the algorithm’s performance both in simulation and on real quantum hardware, ﬁnding only a minor slowdown in the case of real machines, particularly if the cost function is more non-local.
Efficient randomized distributed coloring in CONGEST

Distributed vertex coloring is one of the classic problems and probably also the most widely studied problems in the area of distributed graph algorithms. We present a new randomized distributed vertex coloring algorithm for the standard CONGEST model, where the network is modeled as an n-node graph G, and where the nodes of G operate in synchronous communication rounds in which they can exchange O(logn)-bit messages over all the edges of G. For graphs with maximum degree Δ, we show that the (Δ+1)-list coloring problem (and therefore also the standard (Δ+1)-coloring problem) can be solved in O(log5logn) rounds. Previously such a result was only known for the significantly more powerful LOCAL model, where in each round, neighboring nodes can exchange messages of arbitrary size. The best previous (Δ+1)-coloring algorithm in the CONGEST model had a running time of O(logΔ + log6logn) rounds. As a function of n alone, the best previous algorithm therefore had a round complexity of O(logn), which is a bound that can also be achieved by a na'ive folklore algorithm. For large maximum degree Δ, our algorithm hence is an exponential improvement over the previous state of the art.
Learning experience for thin‐film solar modules: First Solar, Inc. case study

New solar photovoltaic technologies face significant barriers to market entry. First Solar, Inc. is the first thin‐film company to succeed in meeting these challenges, with substantial information about the company's evolution available through its public listing. Learning experience in relation to prices, manufacturing costs, module efficiency and material costs are assessed over the 2003–early 2010 period. Copyright © 2010 John Wiley & Sons, Ltd.
1st Place Solution to VisDA-2020: Bias Elimination for Domain Adaptive Pedestrian Re-identification

This paper presents our proposed methods for domain adaptive pedestrian re-identification (Re-ID) task in Visual Domain Adaptation Challenge (VisDA-2020). Considering the large gap between the source domain and target domain, we focused on solving two biases that influenced the performance on domain adaptive pedestrian Re-ID and proposed a two-stage training procedure. At the first stage, a baseline model is trained with images transferred from source domain to target domain and from single camera to multiple camera styles. Then we introduced a domain adaptation framework to train the model on source data and target data simultaneously. Different pseudo label generation strategies are adopted to continuously improve the discriminative ability of the model. Finally, with multiple models ensembled and additional post processing approaches adopted, our methods achieve 76.56% mAP and 84.25% rank-1 on the test set. Codes are available at https://github.com/vimar-gu/Bias-Eliminate-DA-ReID
Normal ranges of some blood chemistry parameters in adult farmed Atlantic salmon, Salmo salar

Blood samples from healthy adult Atlantic salmon fed an optimal diet in net sea pens were collected at intervals from October to May. Haematological determinations and biochemical serum analyses were carried out on 20 fish in each of seven samples. The ranges of haemato-logical values for sample means were: haematocrit 44–49%, haemoglobin 8.9–10.4 g 100ml−1, red blood cell count 0.85–1.10 × 1012 l−1, MCV 441–553 × 10−15 1, MCH 94–106 × 10−6 g, MCHC 19.4–21.7 g 100ml−1 and leucocrit 0.43–0.96%. The ranges of enzyme activities in serum, for sample means, were: alkaline phosphatase 647–988Ul−1, aspartate aminotrans-ferase 202–351 Ul−1 and alanine aminotransferase 4–8 Ul−1. The ranges of the other parameters analyzed in serum were: total protein 41.6–56.6 gl−1, albumin 18.3–24.3 gl−1, albumin/total protein ratio 39.3–44.0%, creatinine 26–46 μmol, triglycerides 2.53–4.98 μmol and cholesterol 9.3–12.8 μmol. These values are considered to be the normal ranges in healthy fish. Variations due to seasonal changes, and the clinical significance of the selected parameters, are discussed. Data showing the reproducibility of the biochemical analyses in serum are presented.
Ultradeep Hydrodesulfurization and Adsorptive Desulfurization of Diesel Fuel on Metal-Rich Nickel Phosphides

Three materials containing Ni2P, Ni12P5, and Ni3P phases on silica gel with surface area 320 m2/g at loadings of 32−37 wt % and the crystal size of NixP phases 30, 9, and 13 nm, respectively, were prepared by a combination of impregnation and TPR methods and tested in hydrodesulfurization (HDS) and adsorptive desulfurization (ADS) of diesel fuel. There were established opposite trends in changing the DS efficiency in two processes: The HDS rate constant decreased while the ADS sulfur capacity (breakthrough at 1 ppmw) increased with increasing the Ni to P ratio in NixP from 2 to 3. The observed behavior was attributed to the specific features of the densities of states (DOS) obtained from the density functional theory calculations of total and partial DOS for Ni and P in NixP phases and revealed in XPS measurements of binding energy of Ni 2p3/2- and P 2p-electrons. This attribution was consistent with the analysis of the relative part of d-electrons of Ni participating in bonding with p-electrons of phosph.
Microbial Kinetics of a Membrane Anaerobic Reactor System

Microbial kinetics of a crossflow ultrafiltration membrane anaerobic reactor (CUMAR) system were investigated at different MLVSS ranges namely, 10–15 g l−1, 20–25 g l−1, 30–35 g l−1 and 40–50 g l−1 when treating brewery wastewater. The overall kinetic coefficients and kinetic coefficients estimated from the four steady-state runs had slight variations from each other. The system performed well, i.e. 99% COD removal and almost 100% BOD removal efficiencies in the membrane permeate (effluent) from the system during steady-state operation. Microbiological analyses showed that significant changes in the numbers of both total bacteria and dominant Methanogenic species per gram MLVSS were observed throughout the operation of the CUMAR system which may mainly have caused some variations in the microbial kinetic coefficients at different level of biomass concentration retained in the anaerobic reactor.
The magnetization of ferromagnetic binary alloys of cobalt or nickel with elements of the palladium and platinum groups

The spontaneous magnetization at the absolute zero and Curie point of the face-centred cubic phase in the binary alloys of Pd, Rh, Ru, Pt, Ir and Os in cobalt and of Rh, Ir and Os in nickel have been measured as a function of composition, and older measurements on Pd, Ru and Pt in nickel have been re-assessed. The significance of the measurements in relation to various simple models is discussed.
Moduli of Admissible Pairs for Arbitrary Dimension, II: Functors and Moduli

Morphisms between the moduli functor of admissible semistable pairs and the Gieseker – Maruyama moduli functor (of semistable coherent torsion-free sheaves) with the same Hilbert polynomial on a non-singular N dimensional projective algebraic variety, are constructed. It is shown that these functors are isomorphic, and the moduli scheme for semistable admissible pairs ((S̃, L̃), Ẽ) is isomorphic to the Gieseker – Maruyama moduli scheme. The considerations involve all components of moduli functors and corresponding moduli scheme as they exist. Bibliography: 17 items.
NOTES ON THE DPRM PROPERTY FOR LISTABLE STRUCTURES

Abstract A celebrated result by Davis, Putnam, Robinson, and Matiyasevich shows that a set of integers is listable if and only if it is positive existentially definable in the language of arithmetic. We investigate analogues of this result over structures endowed with a listable presentation. When such an analogue holds, the structure is said to have the DPRM property. We prove several results addressing foundational aspects around this problem, such as uniqueness of the listable presentation, transference of the DPRM property under interpretation, and its relation with positive existential bi-interpretability. A first application of our results is the rigorous proof of (strong versions of) several folklore facts regarding transference of the DPRM property. Another application of the theory we develop is that it will allow us to link various Diophantine conjectures to the question of whether the DPRM property holds for global fields. This last topic includes a study of the number of existential quantifiers needed to define a Diophantine set.
Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA)

With their extensive coverage, marine low clouds greatly impact global climate. Presently, marine low clouds are poorly represented in global climate models, and the response of marine low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The Eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary layer clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. In addition, the ENA is periodically impacted by continental aerosols, making it an excellent location to study the cloud condensation nuclei (CCN) budget in a remote marine region periodically perturbed by anthropogenic emissions, and to investigate the impacts of long-range transport of aerosols on remote marine clouds. The Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) campaign was motivated by the need of comprehensive in-situ measurements for improving the understanding of marine boundary layer CCN budget, cloud and drizzle microphysics, and the impact of aerosol on marine low cloud and precipitation. The airborne deployments took place from June 21 to July 20, 2017 and January 15 to February 18, 2018 in the Azores. The flights were designed to maximize the synergy between in-situ airborne measurements and ongoing long-term observations at a ground site. Here we present measurements, observation strategy, meteorological conditions during the campaign, and preliminary findings. Finally, we discuss future analyses and modeling studies that improve the understanding and representation of marine boundary layer aerosols, clouds, precipitation, and the interactions among them.
Graph Convolutional Networks for traffic anomaly

Event detection has been an important task in transportation, whose task is to detect points in time when large events disrupts a large portion of the urban traffic network. Travel information {Origin-Destination} (OD) matrix data by map service vendors has large potential to give us insights to discover historic patterns and distinguish anomalies. However, to fully capture the spatial and temporal traffic patterns remains a challenge, yet serves a crucial role for effective anomaly detection. Meanwhile, existing anomaly detection methods have not well-addressed the extreme data sparsity and high-dimension challenges, which are common in OD matrix datasets. To tackle these challenges, we formulate the problem in a novel way, as detecting anomalies in a set of directed weighted graphs representing the traffic conditions at each time interval. We further propose \textit{Context augmented Graph Autoencoder} (\textbf{Con-GAE }), that leverages graph embedding and context embedding techniques to capture the spatial traffic network patterns while working around the data sparsity and high-dimensionality issue. Con-GAE adopts an autoencoder framework and detect anomalies via semi-supervised learning. Extensive experiments show that our method can achieve up can achieve a 0.1-0.4 improvements of the area under the curve (AUC) score over state-of-art anomaly detection baselines, when applied on several real-world large scale OD matrix datasets.
Syntax-Enhanced Pre-trained Model

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens.
Using Social Recognition To Address the Gender Difference in Volunteering for Low-Promotability Tasks

Research shows that women volunteer significantly more for tasks that people prefer others to complete. Such tasks carry little monetary incentives because of their very nature. We use a modified version of the volunteer's dilemma game to examine if non-monetary interventions, particularly, social recognition can be used to change the gender norms associated with such tasks. We design three treatments, where a) a volunteer receives positive social recognition, b) a non-volunteer receives negative social recognition, and c) a volunteer receives positive, but a non-volunteer receives negative social recognition. Our results indicate that competition for social recognition increases the overall likelihood that someone in a group has volunteered. Positive social recognition closes the gender gap observed in the baseline treatment, so does the combination of positive and negative social recognition. Our results, consistent with the prior literature on gender differences in competition, suggest that public recognition of volunteering can change the default gender norms in organizations and increase efficiency at the same time.
New Insights into the Structure of Equilibria for the Network Creation Game

We study the sum classic network creation game introduced by Fabrikant et al. in which $n$ players conform a network buying links at individual price $\alpha$. When studying this model we are mostly interested in \emph{Nash equilibria} (\NE) and the \emph{Price of Anarchy} (\PoA). It is conjectured that the \PoA is constant for any $\alpha$. Up until now, it has been proved constant \PoA for the range $\alpha = O(n^{1-\delta_1})$ with $\delta_1>0$ a positive constant and it has been proved constant \PoA for the range $\alpha>n(1+\delta_2)$ with $\delta_2>0$ a positive constant. Our contribution consists in proving that \NE graphs satisfy very restrictive topological properties generalising some properties proved in the literature and providing new insights that might help settling the conjecture that the \PoA is constant for the remaining range of $\alpha$: (i) We show that \emph{every} node has the majority of the other nodes of the \NE graph at the \emph{same} narrow range of distances. (ii) If instead of considering the average degree, we focus on the number of non-bridge links bought for any player, we can prove that there exists a constant $D$ such that the average degree is upper bounded by $\max(R, \frac{2n}{\alpha}+6)$ for any \NE graph of diameter larger than $D$, where $R$ is a positive constant.
Analysis of Short Dwell Time in Relation to User Interest in a News Application

Dwell time has been widely used in various fields to evaluate content quality and user engagement. Although many studies shown that content with long dwell time is good quality, contents with short dwell time have not been discussed in detail. We hypothesize that content with short dwell time is not always low quality and does not always have low user engagement, but is instead related to user interest. The purpose of this study is to clarify the meanings of short dwell time browsing in mobile news application. First, we analyze the relation of short dwell time to user interest using large scale user behavior logs from a mobile news application. This analysis was conducted on a vector space based on users click histories and then users and articles were mapped in the same space. The users with short dwell time are concentrated on a specific position in this space; thus, the length of dwell time is related to their interest. Moreover, we also analyze the characteristics of short dwell time browsing by excluding these browses from their click histories. Surprisingly, excluding short dwell time click history, it was found that short dwell time click history included some aspect of user interest in 30.87% of instances where the cluster of users changed. These findings demonstrate that short dwell time does not always indicate a low level of user engagement, but also level of user interest.
A Differential-Cascaded Paradigm for Control of Nonlinear Systems

This paper focuses on developing a new paradigm motivated by investigating the consensus problem of networked Lagrangian systems with time-varying delay and switching topologies. We present adaptive controllers with piecewise continuous or arbitrary times differentiable control torques for realizing consensus of Lagrangian systems, extending the results in the literature. This specific study motivates the formulation of a new paradigm referred to as forwardstepping, which is shown to be a systematic tool for solving various nonlinear control problems. One distinctive point associated with forwardstepping is that the order of the reference dynamics is typically specified to be equal to or higher than that of the original nonlinear system, and the reference dynamics and the nonlinear system are governed by a differential/dynamic-cascaded structure. The order invariance or increment of the specified reference dynamics with respect to the nonlinear system and their differential/dynamic-cascaded structure expands significantly the design freedom and thus facilitates the seeking of solutions to many nonlinear control problems which would otherwise often be intractable.
Improving the Generalization of End-to-End Driving through Procedural Generation

Recently there is a growing interest in the end-to-end training of autonomous driving where the entire driving pipeline from perception to control is modeled as a neural network and jointly optimized. The end-to-end driving is usually first developed and validated in simulators. However, most of the existing driving simulators only contain a fixed set of maps and a limited number of configurations. As a result the deep models are prone to overfitting training scenarios. Furthermore it is difficult to assess how well the trained models generalize to unseen scenarios. To better evaluate and improve the generalization of end-to-end driving, we introduce an open-ended and highly configurable driving simulator called PGDrive. PGDrive first defines multiple basic road blocks such as ramp, fork, and roundabout with configurable settings. Then a range of diverse maps can be assembled from those blocks with procedural generation, which are further turned into interactive environments. The experiments show that the driving agent trained by reinforcement learning on a small fixed set of maps generalizes poorly to unseen maps. We further validate that training with the increasing number of procedurally generated maps significantly improves the generalization of the agent across scenarios of different traffic densities and map structures. Code is available at: https://decisionforce.github. io/pgdrive