Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-modal Conditional Attention Fusion for Dimensional Emotion Prediction

Continuous dimensional emotion prediction is a challenging task where the fusion of various modalities usually achieves state-of-the-art performance such as early fusion or late fusion. In this paper, we propose a novel multi-modal fusion strategy named conditional attention fusion, which can dynamically pay attention to different modalities at each time step. Long-short term memory recurrent neural networks (LSTM-RNN) is applied as the basic uni-modality model to capture long time dependencies. The weights assigned to different modalities are automatically decided by the current input features and recent history information rather than being fixed at any kinds of situation. Our experimental results on a benchmark dataset AVEC2015 show the effectiveness of our method which outperforms several common fusion strategies for valence prediction.
N-desmethyl-Loperamide Is Selective for P-Glycoprotein among Three ATP-Binding Cassette Transporters at the Blood-Brain Barrier

[11C]N-desmethyl-Loperamide ([11C]dLop) is used in positron emission tomography (PET) to measure the in vivo activity of efflux transporters that block the passage of drugs across the blood-brain barrier. The three most prevalent ATP-binding cassette efflux transporters at the blood-brain barrier are P-glycoprotein (P-gp), multidrug resistance protein 1 (Mrp1), and breast cancer resistance protein (BCRP). We sought to measure the selectivity of dLop among these three transporters. The selectivity of dLop at low concentrations (≤1 nM) was measured both as the accumulation of [3H]dLop in human cells that overexpress each transporter and as the uptake of [11C]dLop in brains of mice that lack genes encoding P-gp, Mrp1, or BCRP. The selectivity of dLop at high concentrations (≥20 μM) was measured as the inhibition of uptake of a fluorescent substrate and the change in cytotoxicity of drugs effluxed at each transporter. Accumulation of [3H]dLop was lowest in cells overexpressing P-gp, and the uptake of [11C]dLop was highest in brains of mice lacking P-gp. At high concentrations, dLop selectively inhibited P-gp function and also decreased the resistance of only the P-gp-expressing cells to cytotoxic agents. dLop is selective for P-gp among these three transporters, but its activity is dependent on concentration. At low concentrations (≤1 nM), dLop acts only as a substrate; at high concentrations (≥20 μM), it acts as both a substrate and an inhibitor (i.e., a competitive substrate). Because low concentrations of radiotracer are used for PET imaging, [11C]dLop acts selectively and only as a substrate for P-gp.
3D Printed Electronics

PARC has been a leading innovator in printed electronics with over a decade of experience in large area electronics, application of novel and current printing technology to industrial applications and the printing of electronic circuits and devices. We are currently applying this knowledge to the concept of printing multi-material 3 dimensional objects with electronic and/or mechanical functionality. The concept is to develop capability and provide means of manufacturing functional objects which may be difficult to manufacture or even not be manufacturable by other means. In this paper, we will outline the creation of a rapid digital manufacturing system and considerations for building such a “printer” with the ability to integrate a structural material with functional electronic materials. The printer incorporates inkjet and extrusion techniques on the same platform, along with an in-line UV-curing lamp. This platform enables a wide processing window for structural and electronic materials; i.e., it is capable of patterning inks with viscosity ranging from 1 to 25000 cP The printer has been optimized to yield smooth printed features (down to tens of microns) without implementing pressure-leveling steps.
Fabrication of photonic crystals and nanocavities

The fabrication of three-dimensional inverse woodpile photonic crystals is highly desirable because of their predicted large photonic band gap, their conceptual ease of fabrication, and their robustness to withstand deviations from the ideal geometry that are intrinsic to nanofabrication. In this thesis, several fabrication methods were investigated to obtain three-dimensional inverse woodpile photonic crystals. Preferably, the entire process is compatible with methods used in the complementary metal oxide semiconductor (CMOS) industry to allow the incorporation of the photonic crystals in silicon integrated circuits. The e�ects of fabrication induced deviations on the photonic band gap of the structures were quanti�ed. Furthermore, we have reported a new method to obtain material cavities that will act as optical cavities in opal photonic crystals. Although the photonic band gap in inverse woodpile crystals is generally robust to disorder, it is sensitive to tapering of the pores. Nonetheless, within the limits of the fabrication methods, the fabricated crystals are expected to remain strongly photonic. In addition, a di�erent crystal geometry was found that yields a slightly larger band gap. By changing the lithographic processes in our fabrication scheme, obtaining this crystal geometry is within reach.
Automatic localization of a language-independent sub-network on deep neural networks trained by multi-lingual speech

Deep neural networks (DNNs) have been successfully applied to automatic speech recognition (ASR). However, no study has investigated the possibility of building a language-independent sub-network DNN as the basis for further training of any new language using a simple plug-in of the sub-network. In this paper, we propose a novel technique to split a DNN into language-independent and -dependent sub-networks using multi-lingual speech training data. Our basic assumption is that, in a DNN for speech processing, language-independent feature processing is done in stages that are near to the input layer, while language-dependent processing is performed in stages that are near to the output layer. Based on this assumption, we propose a technique to simultaneously optimize multiple sub-networks in a DNN trained with multi-lingual speech data. The language-dependent and -independent processing boundaries in individual sub-networks are segmented automatically. We test our technique in phoneme classification experiments. The results demonstrate that a language-independent sub-network DNN extracted by our technique can be used as a universal network for speech processing of additional new languages.
Robust Vehicle Localization Using Entropy-Weighted Particle Filter-based Data Fusion of Vertical and Road Intensity Information for a Large Scale Urban Area

This letter proposes a robust vehicle localization method based on a prior point cloud in urban area. The high resolution point cloud collected six months ago is provided from Singapore Land Authority around One-north area in Singapore, because the data are outdated there are many changed aspects of the environment such as redrawn road markings, construction areas, and changing tree shapes. In response, this paper proposes a novel fusion algorithm based on a particle filter using vertical and road intensity information for robust localization. Whereas the state-of-the-art fusion algorithm focus on optimization of the vehicle pose based on multiple measurements, the proposed method estimates a robust vehicle pose by considering the reliability of each feature from the prior map. We also propose an efficient management strategy of a grid map that includes multilayer vertical and road intensity information for real-time operation. A sensor system equipped on a vehicle consists of 32 channels of 3D Light Detection And Ranging, IMU, wheel odometry, which are used for the proposed algorithm. RTK-GPS, wheel odometry, and iterative closest point algorithm are utilized by using a graph-structure optimization method in off-line for estimating ground truth. The total data set for the demonstration is collected in a 19.9 km way in an urban area. The proposed approach has successfully performed an autonomous vehicle driving in an urban area.
Mine Ventilation Optimization Analysis and Airflow Control Based on Harmony Annealing Search

The mine ventilation optimization and airflow control are the mine ventilation system's two important aspects, good ventilation optimization design can deliver fresh air to establish good work environment and provide a scientific and reliable basis to prevent disaster. Based on the air distribution request of the mine ventilation network, carries on the optimized computation to the mine ventilation system network, fuses the dynamic perturbation and the simulated annealing to improve the harmony search algorithm, the improved harmony search algorithm enhances the convergent speed, has overcome certain limitations of the traditional ventilation network optimizing control algorithm. In the mine ventilation analysis, the improved harmony search through proper tuning these parameters was presented. The simulation results show that the method outperforms the conventional nonlinear programming approach whether from the viewpoint of the number of iterations required to find the optimum solutions or from the final solutions obtained. Mine fire is a very complex physicochemical process. Once it happens, there are lots of poisonous and harmful gas ingredients. Additionally, the fume caused by the fire will block the vision and decrease the visibility, which will impede human evacuation and fire fighting, moreover, it is easy to cause the gas and coal dust explosion. So it is very important for the evacuation of miners and mining salvation to research the air flow control during mine fire. Airflow control based on harmony search has provided a scientific and reliable basis to develop disaster prevention and processing.
Inhibition of lymphocyte proliferation stimulated by lectins and allogeneic cells by normal plasma lipoproteins

Lipoproteins, isolated by sequential flotation at densities 1.006, 1.019, 1.063, and 1.21, were examined for their ability to inhibit human lymphocytes stimulated by allogeneic cells and by lectins (phytohemagglutinin-P and concanavalin A). All the classes of normal plasma lipoproteins inhibited lymphoproliferation when peripheral blood lymphocytes were cultured in autologous, heterologous, or lipoprotein- deficient plasma (d greater than 1.21). The rank order of inhibitory potency was intermediate density lipoprotein (IDL) greater than very low density lipoproteins (VLDL) greater than low density lipoproteins (LDL) greater than high density lipoproteins (HDL), regardless of the mode of stimulation. The concentrations of IDL, VLDL, and LDL required for complete inhibition of stimulated lymphoproliferation were considerably below the levels of each of these lipoproteins normally found in human plasma. In addition, the concentration of HDL required for 50-90% inhibition was in the range of HDL levels normally found in human plasma. Moreover, at relatively higher concentrations, lipoproteins suppressed the incorporation of [3H]thymidine into DNA below the levels seen with reseting, unstimulated lymphocytes. The results suggest that circulating lymphocytes may normally be highly suppressed by the combined effects of all the endogenous lipoproteins and that the lipoproteins may play important roles in vivo in modulating lymphocyte functions and responses.
Optimal Scheduling for Fair Resource Allocation in Ad Hoc Networks With Elastic and Inelastic Traffic

This paper studies the problem of congestion control and scheduling in ad hoc wireless networks that have to support a mixture of best-effort and real-time traffic. Optimization and stochastic network theory have been successful in designing architectures for fair resource allocation to meet long-term throughput demands. However, to the best of our knowledge, strict packet delay deadlines were not considered in this framework previously. In this paper, we propose a model for incorporating the quality-of-service (QoS) requirements of packets with deadlines in the optimization framework. The solution to the problem results in a joint congestion control and scheduling algorithm that fairly allocates resources to meet the fairness objectives of both elastic and inelastic flows and per-packet delay requirements of inelastic flows.
On the Expected Number of Failures Detected by Subdomain Testing and Random Testing

We investigate the efficacy of subdomain testing and random testing using the expected number of failures detected (the E-measure) as a measure of effectiveness. Simple as it is, the E-measure does provide a great deal of useful information about the fault detecting capability of testing strategies. With the E-measure, we obtain new characterizations of subdomain testing, including several new conditions that determine whether subdomain testing is more or less effective than random testing. Previously, the efficacy of subdomain testing strategies has been analyzed using the probability of detecting at least one failure (the P-measure) for the special case of disjoint subdomains only. On the contrary, our analysis makes use of the E-measure and considers also the general case in which subdomains may or may not overlap. Furthermore, we discover important relations between the two different measures. From these relations, we also derive corresponding characterizations of subdomain testing in terms of the P-measure.
Information Retrieval Using Robust Natural Language Processing

We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effectiveness of traditional key-word based document retrieval. The backbone of our system is a statistical retrieval engine which performs automated indexing of documents, then search and ranking in response to user queries. This core architecture is augmented with advanced natural language processing tools which are both robust and efficient. In early experiments, the augmented system has displayed capabilities that appear to make it superior to the purely statistical base.
Saddle add-on metallization for RF-IC technology

A cost-effective add-on process module for reducing ohmic losses of radio-frequency (RF) inductors and interconnects in RF/BiCMOS and RF/CMOS technologies built on CMOS logic processes is proposed. The module is based on the local thickening of the top metal layer of the thin CMOS interconnects through copper (Cu) electroplating in selected areas. The combination of dense Cu-interconnects in the CMOS logic sections, of thick Cu top-level wiring through local Cu electroplating in the RF sections, and of aluminum (Al) capping of the bond pads provides an optimum tradeoff between packaging requirements, quality of passive components and interconnects, and cost. A special wet-etch process sequence for removal of the Cu-seed and adhesion films from the exposed top metal layer is described. A record quality factor of /spl sim/13 for a 10-nH inductor on a conventional 5-/spl Omega/-cm silicon substrate is demonstrated.
Paraphrasing Sentential Queries by Incorporating Coordinate Relationship

Although long queries are still a small part of the queries submitted to Web search engines, their usage tends to gradually increase. However, the effectiveness of the retrieval decreases with the increase of query length. Long queries are very likely to have few Web pages returned. We target at sentential queries, a type of long queries, and propose a method called sentential query paraphrasing for improving their retrieval performance, especially on recall. We are motivated by the assumption that a sentence is an indivisible whole, which means that removing terms or phrases from a sentence would lead to the missing of some information or query drift. In this paper, we paraphrase sentential queries to avoid missing information and consequently ensure the completeness of the information. Take the sentential query “apples pop a powerful pectin punch,” for example. Its meaning will be changed if one or more terms are removed, and few Web pages are returned by conventional search engines. In contrast, querying by its paraphrases, such as “apples contain a lot of pectin” or “apples are rich in pectin,” can retrieve more Web pages. The experimental results show that our method can acquire more paraphrases from the noisy Web. Besides, with the help of paraphrases, more Web pages can be retrieved, especially for those sentential queries that could not find any answers with its original expression.
ChoiceRank: Identifying Preferences from Node Traffic in Networks

Understanding how users navigate in a network is of high interest in many applications. We consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities. We cast it as a preference learning problem, and we study a model where choices follow Luce's axiom. In this case, the O(n) marginal counts of node visits are a sufficient statistic for the O(n^2) transition probabilities. We show how to make the inference problem well-posed regardless of the network's structure, and we present ChoiceRank, an iterative algorithm that scales to networks that contains billions of nodes and edges. We apply the model to two clickstream datasets and show that it successfully recovers the transition probabilities using only the network structure and marginal (node-level) traffic data. Finally, we also consider an application to mobility networks and apply the model to one year of rides on New York City's bicycle-sharing system.
Turning Small to Big: Efficient Mobile Advertisement Propagation With Local Information

We investigate how to leverage limited local information for mobile advertisement popularization, where users are motivated to propagate advertisements with rewards or credits distributed from a centralized platform. Previous solutions on social networks failed to be applicable for mobile advertisement propagation because of the mobility, which would lead to extremely high overhead and low propagation efficiency. Participants need to be selected carefully and efficiently in dealing with the highly dynamic network and uncertain contacts among users. Even worse, the propagation effects are difficult to quantify with the increased number of mobile users. In tackling these difficulties, we propose <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>MAP (<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> here means efficient mobile advertisement popularization with local information), a lightweight but effective propagation user selection scheme with local information. Two key technologies inspire us to achieve efficient and effective mobile advertisement propagation. First, we advocate propagation effects instead of influence for user selection, where mobile users with strong information dissemination ability could be selected. Second, we use local information instead of the global information to achieve near optimal performance for propagation. In our proposed scheme, the information potentials proposed by Loukas <italic>et al.</italic> are leveraged to find the influential users with local information. With extensive experimental study, we find that <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>MAP could effectively improve the mobile advertisements delivery ratio. Using the propagation instead of popularization is validated with different aspects of investigations when the budget is constrained. To evaluate the impact of mobility, we leverage the mobile trace data set for comprehensive evaluations. <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>MAP performs fairly well when more realistic experimental settings are incorporated.
Generation of L Cells in Mouse and Human Small Intestine Organoids

Upon a nutrient challenge, L cells produce glucagon-like peptide 1 (GLP-1), a powerful stimulant of insulin release. Strategies to augment endogenous GLP-1 production include promoting L-cell differentiation and increasing L-cell number. Here we present a novel in vitro platform to generate functional L cells from three-dimensional cultures of mouse and human intestinal crypts. We show that short-chain fatty acids selectively increase the number of L cells, resulting in an elevation of GLP-1 release. This is accompanied by the upregulation of transcription factors associated with the endocrine lineage of intestinal stem cell development. Thus, our platform allows us to study and modulate the development of L cells in mouse and human crypts as a potential basis for novel therapeutic strategies in patients with type 2 diabetes.
The Brera Multiscale Wavelet Detection Algorithm Applied to the Chandra Deep Field-South: Deeper and Deeper

Chandra Deep Fields represent the deepest look at the X-ray sky. We analyzed the Chandra Deep Field-South (CDF-S) with the aid of a dedicated wavelet-based algorithm. Here we present a detailed description of the procedures used to analyze this field, tested and verified by means of extensive simulations. We show that we can safely reconstruct the log N- log S source distribution of the CDF-S down to limiting fluxes of 2.4 × 10-17 and 2.1 × 10-16 ergs s-1 cm-2 in the soft (0.5-2 keV) and hard (2-10 keV) bands, respectively, fainter by a factor of ≳2 than current estimates. At these levels we can account for ≳90% of the 1-2 keV and 2-10 keV X-ray background.
Ranking biomedical literature search result based on relevance feedback using fuzzy logic and Unified Medical Language System

Online databases and search engines usually return a (long) list of hits that satisfy the user's search criteria. The returned list of hits is often too long for the user to review every hit if he/she does not know exactly what he/she wants and/or lacks time. Our focus is on biomedical literature search - a healthcare provider needs to find important articles while a patient is waiting for the provider's diagnosis or treatment decision. In this paper, we developed a fuzzy logic-based ranking approach for biomedical literature search using relevance feedback with the help of Unified Medical Language System (UMLS). UMLS is a biomedical term database that classifies and defines the biomedical language. Relevance feedback refers to an interactive process that helps to improve the retrieval efficiency via user feedback. UMLS provides meaning and semantic type methods that can be used for search result ranking, but they sometimes do not rank the search result accurately. To preliminarily evaluate our proposed approach, we created a document set containing 10 biomedical papers and 20 synthesized documents from them. We designed experiments to: 1) compare the performance of fuzzy ranking method with UMLS meaning and semantic type methods, and 2) evaluate the effectiveness of using relevance feedback in the search process. Our experiments showed that 1) the fuzzy ranking approach improved the average ranking order accuracy by 3.35% and 29.55% as compared with UMLS meaning and semantic type methods respectively, and 2) better ranking result using relevance feedback in the search process.