Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey of public information about ongoing clinical trials funded by industry: evaluation of completeness and accessibility

Abstract Objective: To evaluate the completeness and accessibility of public information about US clinical trials of drugs in development. Design: Review of online registers of clinical trials. Data sources: Drugs in phase III trials were identified using three drug industry sources: PhRMA Survey, What's in the Pipeline, and the NDA Pipeline. Drug trials were then searched for on the following publicly accessible registers of clinical trials: CancerNet.gov, CenterWatch.com, ClinicalTrials.gov, and registers associated with the 37 “Comprehensive Cancer Centers” designated by the National Cancer Institute. Main outcome measure: Extent of availability of public information on phase III trials of drugs in development for treating either prostate or colon cancer. Results: Search of industry sources identified 12 drugs for prostate cancer and 20 for colon cancer undergoing phase III trials. The most comprehensive publicly available register, ClinicalTrials.gov, contained trial listings for only seven of the prostate cancer drugs and 10 of the colon cancer drugs. Trials of three prostate cancer and three colon cancer drugs were listed on only one register each. A substantial proportion of trials of prostate cancer drugs (3/12) and colon cancer drugs (8/20) were not associated with trial listings in any registers. Conclusions: Existing trials registers are unlikely to be meeting user needs since many ongoing drug trials are not listed. There is a clear need for a comprehensive clinical trials register encompassing all ongoing trials, including industry sponsored trials.
Adaptive large neighborhood search for the dynamic vehicle routing problem with electric vehicles

This paper investigates the dynamic electric vehicle routing problem with electric vehicles (DVRP-E). In the DVRP-E, customer demands may emerge stochastically throughout the operational horizon. Moreover, as electric vehicle (EV) batteries remain a key limiting factor for EVs, EVs must return to the depot before batteries run down. An adaptive large neighborhood search algorithm is proposed for its solution, and computational tests are presented to demonstrate its effectiveness and accuracy. The ALNS algorithm has proved itself an effective tool to solve the DVRP-E with up to 1000 customers according to our computational test.
Reexamining Indoor Mobile Robots from a Cognitive Perspective

The field of traditional mobile robot navigation has undergone a gradual transformation, evolving into a standardized and procedural research domain. Through a fresh cognitive perspective on this navigation process, a bridge emerges between robotic researchers and cognitive neuroscientists, thereby fostering the growth of interdisciplinary exploration. This article meticulously examines three pivotal phases of robot navigation: information acquisition, simultaneous localization and mapping (SLAM), and path planning. These phases are intricately linked to pertinent cognitive research. Furthermore, a comprehensive survey of existing biomimetic and neuromorphic algorithms is presented, highlighting their applications in augmenting robot navigation capabilities. Ultimately, this article charts a visionary course for the future of robot navigation, envisioning its potential in facilitating more intelligent and efficient navigation techniques.
SIG-SLAM: Semantic Information-Guided Real-Time SLAM for Dynamic Scenes

Visual Simultaneous Localization and Mapping (Visual-SLAM) providing the camera’s location and the map of surroundings, becomes more and more popular in the field of AR/VR, robotics, unmanned vehicle driving and so on. Traditional SLAM assumes that the scene is static, which results in the bad performance in dynamic scenes. In this paper we present SIG-SLAM, a real-time dynamic SLAM system that is built on ORB-SLAM2, adding the capability of dynamic object pixel-level semantic segmentation. SIG-SLAM can still perform well in dynamic scenes via taking the semantic information into account. Besides, by adding another independent thread for semantic segmentation, tracking thread does not need to stop to wait for semantic information any more. Finally, a novel approach to utilize the semantic information efficiently is proposed, making sure the real-time performance of our system. We evaluate our system in the public dataset TUM. The result shows that compared to other state-of-the-art algorithms, our proposed method can provide competitive accuracy while ensuring good real-time performance.
A generic drift reduction technique for orientation estimation from biomechanical angular velocity

Inertial measurement units are used in ambulatory human movement analysis, but their use is often subject to domain- or application-specific assumptions and methods to compensate for drift in kinematic estimations. Here, we propose and evaluate a generic drift reduction technique for orientation estimation. A second order Taylor approximation for 3D orientation estimation from sampled angular velocity is derived and evaluated on a publicly available dataset containing angular velocity data of 4 runners. The use of a second order Taylor approximation substantially reduces drift when the angular velocity has considerable contributions along all three axes. The second order Taylor approximation could therefore facilitate the use of minimal sensor setups for the study of dynamic 3D human movements.
Reward Teaching for Federated Multi-armed Bandits

Most existing federated multi-armed bandits (FMAB) designs are based on the presumption that clients will implement the new design to collaborate with the server. In reality, however, it may not be possible to modify the client protocols. Motivated by this limitation, this work focuses on clients who always maximize their individual cumulative rewards, and introduces a novel idea of reward teaching, where the server guides the clients towards global optimality through implicit local reward adjustments. Under this framework, the server faces two tightly coupled tasks of bandit learning and target teaching, whose combination is non-trivial and challenging. A novel algorithm, called Teaching-After-Learning (TAL), is proposed, which encourages and discourages clients’ explorations separately. General performance analyses of TAL on regret and cost are first established when the clients’ strategies satisfy certain requirements. To particularize the results, clients with UCB or ε-greedy strategies are then considered, where novel technical approaches are developed to analyze their warm-start behaviors. The obtained guarantees concretely demonstrate that when facing these client strategies, TAL achieves logarithmic regrets while only incurring logarithmic adjustment costs, which is order-optimal w.r. t. a natural lower bound.
Artificial Intelligence for Cybersecurity

Cybersecurity attacks are on a steep increase across industry domains.1,2 With ubiquitous connectivity and increasingly standard software stacks, basically all software is accessible and vulnerable. Yet, cybersecurity is not systematically deployed because necessary processes are demanding and need continuous attention paired with technology competences. Many software suppliers do not pay adequate attention and governance, resulting in problems such as weak communication protocols, insufficient passwords, and social engineering risks.
A Novel State Estimator Based on Algebraic Parametric Method

It’s crucial to design a robust and accurate state estimator for a given observable LTI system of which the initial conditions are unknown, especially when the system is in a noisy environment. This paper handles this problem from the non-asymptotic perspective and provides a novel design process for estimators based on the algebraic parametric method. Unlike other algebraic-parametric-method-based state estimators, the proposed estimator regards the unknown initial conditions as values to be estimated, which can also provide some theoretical support for initial value settings in other estimation methods. Moreover, the separation theorem is proven to hold, so we can construct a state feedback controller based on the designed novel estimator to achieve the control objective. Finally, numerical simulations with a damping platform model illustrate that the proposed state estimator outperforms the traditional Luenberger state observer in robustness, accuracy, and fastness.
PRIMO: A Full-Stack Processing-in-DRAM Emulation Framework for Machine Learning Workloads

Recently, the size of deep learning models has significantly increased, making the excessive memory access between the AI processor and DRAM a major bottleneck of the system. The processing-in-DRAM (DRAM-PIM) concept has emerged as a promising solution, which integrates computing logic within memory, thus saving abundant access to external memory. Although many simulators have been proposed to model and analyze the benefits of DRAM-PIM, they are often too slow to run an entire application. FPGA-based emulators have been introduced to overcome this limitation. However, none of the prior works include the full software stack from the model to DRAM-PIM hardware. This paper presents a full-stack processing-in-DRAM emulation framework named PRIMO, the first emulation framework that can model and analyze DRAM-PIM for end-to-end ML inference. PRIMO enables software developers to develop and test their customized software stacks on various ML workloads without requiring a real DRAM-PIM chip. Moreover, it allows designers to explore design space and monitor memory access patterns, facilitating software and hardware co-design for efficient DRAM-PIM architectures. To achieve these goals, we develop a real-time FPGA emulator that emulates DRAM-PIM architecture and generates experimental results such as predicted cycle information and computed output at incomparably high speeds compared to the CPU-based simulation. In addition, we propose a software stack comprising a PIM compiler that enables the execution of various ML workloads, including end-to-end inference, and a PIM driver that runs the workloads with high bandwidth utilization by leveraging virtual memory scatter-gather DMA. Finally, we demonstrate that PRIMO can successfully emulate DRAM-PIM 106.64-6093.56× faster than the CPU-based simulation framework for ML workloads ranging from small microbenchmarks to end-to-end inference of ResNets.
Fault diagnosis method of rolling bearing based on SE-DRN

As a key component of rotating machinery, rolling bearing is widely used. Its health directly affects the normal operation of equipment. Therefore, the rolling bearing fault diagnosis technology has important engineering application value. This chapter proposes a rolling bearing fault diagnosis method based on Squeeze-Excitation Deep Residual Network (SE-DRN). First, the bearing vibration signal is decomposed by Variational Mode Decomposition (VMD), all Intrinsic Mode Function (IMF) obtained after decomposition are reconstructed, and the Intrinsic Mode Function (IMF) signal matrix is constructed. Then, the signal matrix is input to Squeeze-Excitation Deep Residual Network (SE-DRN) for feature extraction and recognition, the fault diagnosis model of Squeeze-Excitation Deep Residual Network (SE-DRN) is established. Finally, it is compared with other fault diagnosis models.The results show that the fault diagnosis model of Squeeze-Excitation Deep Residual Network (SE-DRN) can well diagnose five different state types of rolling bearings.
Evaluación de formulaciones atrayentes para la mosca del mango y la mosca del Mediterráneo (Diptera: Tephritidae) en un huerto de mango y efecto de la fructificación del cultivo en las capturas de las moscas.

Luque L, Gonzalez E, Casares R, Castillo A, Meneses H. 2007. Evaluation of attractant formulations for West Indian fruit fly and Mediterranean fruit fly (Diptera: Tephritidae) in a mango orchard and effect of crop fructification on fly captures. Enomotropica 22(3): 171-179. The efficacy of two attractant formulations were tested on the West Indian fruit fly, Anastrepha obliqua (Macquart) and the Mediterranean fruit fly, Ceratitis capitata (Wiedemann) as well as the effect of crop phenology on fly captures. Formulations (water plus attractants) were: Ped-Go Plus 9% and Ped-Go MegaPlus 5% in addition of a Control (water plus 2% borax). Along 14 intervals of 14 day (February-August 2004) 300 mL of attractant aqueous were placed in Nancy 2002 traps following a randomized block design. Traps were placed at the second 3rd height of ‘Haden’ mango trees. Tree phenology was recorded weekly. At the end of each interval phenology was summarized, trap captures recorded, attractants replaced and traps rotated among trees. Captures were recoorded as MTD (M=fly / trap x day). Results showed that all tested attractants were able to lure both A. obliqua and C. capitata. Statistical tests for MTD values could not detect any significant differences between formulations but the Control (Friedman, P ≤ 0.05). Ped-Go MegaPlus 5% allowed to capture larger number of both flies, in addition when solution of attractants were made, from a high concentrated protein base, Ped-Go MegaPlus yielded more suspension (1 : 20 ENTOMOTROPICA 22(3): 171-179. Diciembre/December 2007 172 L) than Ped-Go Plus. Both variables ripe fruit and fly captures showed a positive correlation whereas unripe fruit and fly captures was negative, highly significant; in addition capture increases coincided with ripe fruit abundance. Additional key words: Crop fructification, crop phenology, insect trap, Mangifera indica, Mediterranean fruit fly, protein lure, West Indian fruit fly
Collisions-free Deployment based on PDE of UAV Swarm*

This paper proposes a PDE continuum model for the UAV swarm where all UAVs are deployed to the target position subject to collisions-free. A parabolic system is established based on the artificial potential functions among UAVs by using the continuous approximation method. The asymptotically stability of this parabolic system is rigorously proved with the help of the eigenvalue of closed-loop such that the state converges to the equilibrium point. Simulation results show the effectiveness of the proposed method.
On the Pole Location of Reduced-order Model in Balanced Truncation Model Order Reduction

In this paper, we discuss model order reduction of linear time invariant systems based on balanced truncation technique. We introduce $\mathcal{D}$-stability theory, which is used to describe the pole region of the system. The transient response and steady-state response of a linear time invariant system are closely related to the position of its poles, therefore, if the reduced order model maintains the pole region of the original system, it can accurately approximate the input-output behavior of the original system within a time-limited interval. According to the $\mathcal{D}$-stability theory, we establish the pole region of system and we give a conjecture regarding whether the balanced truncation technique can preserve the pole region of the original system. Finally, we present a numerical example to demonstrate that the conjecture is not true in general.
A Full 3D Imaging Approach for Contactless GPR

This study deals with a full 3D imaging approach for contactless GPR data. The approach belongs to the family of linear microwave tomographic procedures, it takes into account the vectorial nature of the scattering phenomenon, and it exploits a ray-based model of the signal propagation in an inhomogeneous scenario. Specifically, the kernel of the integral equation to be inverted is evaluated by exploiting the concept of the interface reflection point. The formulation of the approach and numerical results, assessing the imaging capabilities, are provided.
Ocean biogeochemical cycles and climate change

The climate appears to be changing due to the increase of carbon dioxide (COz) in the atmosphere because of human activity. The ocean carbon cycle plays a major role controlling atmospheric C02 levels. The oceans presently remove about 30% of the annual anthropogenic C02 emissions. Variability in the biogeochemical cycles could exert significant feedback effects during conditions of climate change. Given the complexity of ocean biogeochemistry, coupled biogeochemicaYocean circulation models are principal tools to assess and understand the sensitivity to climate change of the ocean carbon cycle. However, substantial improvements are required in the current suite of numerical models if we are to better understand the present ocean biogeochernical state and predict potential future responses to anthropogenic perturbations.
Towards Timing-Driven Routing: An Efficient Learning Based Geometric Approach

As the rapid increasing of the circuits complexity, it is urgent to develop efficient algorithmic techniques for EDA. In this paper, we consider the routing problem which is a key part for designing high-quality chips. In particular, we combine both the max path length and total wirelength for modeling our optimization objective, since the path delay often causes timing issue that can seriously degrade the whole routing efficiency (even if the total wirelength is small). Comparing with most of the previous works that only considering wirelength, the timing-driven routing objective is much more challenging to optimize. We propose an efficient learning-based approach together with several novel insights in geometry. For moderate-degree nets, our approach can yield a better smooth trade-off between the wirelength and max path length comparing with the state-of-the-art methods. For large-degree nets, we propose an elegant and easy-to-implement geometric data structure called “data-dependent polar quadtree” in the space; using this structure, we can successfully plug our learning-based approach into a divide & merge framework and the optimization quality over the whole instance can be well preserved.
Sedimentary environment control on shale gas reservoir:A case study of Lower Cambrian Qiongzhusi Formation in the Middle Lower Yangtze area

According to the assumption of "deposition control facies,facies control reservoir",based on field investigation,experimental test and previous research results,a study was performed to investigate the characteristics of shale reservoir controlled by the sedimentary environment of the Lower Cambrian Qiongzhusi Formation in the Middle Lower Yangtze area.The studies show that the organic-rich shale is mainly developed in the shelf facies and deep-sea retention basin facies,the thickness of deposition center is up to 100-180 m with the maximum organic carbon content of 18.7%,and the average organic carbon content is 5.07%. The major lithologies include organic-rich carboniferous shale,siliceous shale,silty shale and calcareous shale under anoxic environment.The minerals of the shale are mainly quartz(average 64.97%) and clays(average 23.66%). A large number of intergranular holes of pyrite under the anoxic environment,microporosity produced by hydrocarbon generation in organic matter,microporosity between layers of clay minerals and micro-cracks controlled by brittle mineral provide a good reservoir space for shale gas.Therefore,the studies on the development of reservoir characteristics under the control of the environment will provide a substantial basis for shale distribution,organic matter abundance,reservoir space and favorable area evaluation in shale gas exploration and development.
Neonatal Risk Modeling and Prediction

Electronic fetal monitoring (EFM) is designed for the early detection of fetal risks and the prevention of serious neurological impairment but suffers from high false positive rates. The Fetal Reserve Index (FRI) is an expert-based system that combines EFM with maternal, obstetrical, and fetal risk factors and displays superior performance in risk detection than EFM alone. Towards translating the FRI into an automated risk prediction system that can make recommendations to clinicians in real-time, we here develop machine learning classifiers that calculate feature importance based on historical data from labor cases and predict the risk of developing neurological impairment. We train random forest and multilayer perceptron (MLP) models to classify abnormal and normal delivery cases and to assess the model performance using a dataset of 1462 labor cases. The random forest classifier achieves a macro average f-1 score of 0.82 with an abnormal case recall of 0.59. Alternatively, MLP classifiers provide higher abnormal case recall at a cost of lower accuracy and macro average f-1 score. Future work will aim to optimize weightings and trade-offs of statistical performance to achieve further improvements for clinical practice.
8. COMPOSITION MAPS OF SURFACE SEDIMENTS OF THE EASTERN TROPICAL PACIFIC OCEAN1

The eastern tropical Pacific Ocean is a region having high primary productivity gradients associated with upwelling at coastal margins, equatorial divergence, and advection of nutrients by currents (see "Introduction" chapter, this volume). These gradients produce compositional changes in the surface sediments of the region. Understanding these modern sedimentation patterns is a vital step toward interpreting past environments and establishing how these differed from those in the modern world. The eastern equatorial Pacific region is topographically subdivided into the Guatemala Basin on the Cocos Plate, the Panama Basin (which straddles the Cocos and Nazca plates), and the Peru and B Vauer basins on the Nazca Plate (Fig. 1). The East Pacific Rise separates these eastern Pacific Ocean basins from the Pacific Basin proper. Sill depths generally are deep, about 3 km in most cases, thus they are only partly separated from the main mass of Pacific Ocean deep water. The primary latitudinal patterns of productivity and sedimentation are caused by zonal banding of the currents and by equatorial divergence. Nutrient-poor waters from the western Pacific Ocean are advected eastward into the eastern tropical Pacific region at about 5°N, while the South Equatorial Current transports nutrient-rich waters from the margin of South America. Along the equator, productivity associated with the equatorial divergence provides another zonal pattern (Koblentz-Mishke et al., 1970). Superimposed upon this latitudinal pattern are (1) high productivity associated with coastal upwelling and (2) nutrient cycling near the continental margins. This pattern is readily apparent in the depth to the brown-green color change in the uppermost seafloor sediments in the region (Fig. 2). Brown surface sediments are indicative of oxidizing sediment conditions, where Fe(III) is the stable oxidation state of Fe. As the flux of organic carbon to the sediments increases, the sediments become green at depth, where the Fe is reduced to Fe(II). The depth to this boundary is a good semiquantitative measure of both organic carbon flux and sedimentary redox intensity. In the eastern tropical Pacific Ocean, a shallow Fe(III)/Fe(II) boundary can be found along coastal margins and in the equatorial region. Around the Galapagos Islands, there is a complex structure to this pattern where island-induced upwelling occurs and where the equatorial undercurrent splits and occasionally surfaces (Pak and Zaneveld, 1973; Leetma, 1982). Sediments under the nutrient-poor North Equatorial Countercurrent form a tongue of brown sediment that extends eastward at 5°N. This brown-green color change is immediately discernible by macroscopic core description. Other patterns of sediment composition also exist, but can be studied only by chemical analysis of the sediments.