Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards the keyboard of oz: learning individual soft-keyboard models from raw optical sensor data

Typing on a touchscreen display usually lacks haptic feedback which is crucial for maintaining finger to key assignment, especially for touch typists who are not looking at their keyboard. This leads to typing being substantially more error prone on these devices. We present a soft keyboard model which we developed from typing data collected from users with diverging typing behavior. For data acquisition, we used a simulated perfect classifier we refer to as The Keyboard of Oz. In order to draw near to this classifier we used the complete sensor data of each keystroke and applied supervised machine learning techniques to learn and evaluate an individual keyboard model. The model not only accounts for individual keystroke distributions but also incorporates a classifier based on the images obtained from an optical touch sensor. The resulting highly individual classifier has remarkable classification accuracy. Additionally, we present an approach to compensate for hand drift during typing utilizing a Kalman filter. We show that this filter performs significantly better with the keyboard model which takes raw sensor data into account.
High-Throughput Screening Identifies a Bisphenol Inhibitor of SV40 Large T Antigen ATPase Activity

The authors conducted a high-throughput screening campaign for inhibitors of SV40 large T antigen ATPase activity to identify candidate antivirals that target the replication of polyomaviruses. The primary assay was adapted to 1536-well microplates and used to screen the National Institutes of Health Molecular Libraries Probe Centers Network library of 306 015 compounds. The primary screen had an Z value of ~0.68, signal/background = 3, and a high (5%) DMSO tolerance. Two counterscreens and two secondary assays were used to prioritize hits by EC50, cytotoxicity, target specificity, and off-target effects. Hits that inhibited ATPase activity by >44% in the primary screen were tested in dose–response efficacy and eukaryotic cytotoxicity assays. After evaluation of hit cytotoxicity, drug likeness, promiscuity, and target specificity, three compounds were chosen for chemical optimization. Chemical optimization identified a class of bisphenols as the most effective biochemical inhibitors. Bisphenol A inhibited SV40 large T antigen ATPase activity with an IC50 of 41 µM in the primary assay and 6.2 µM in a cytoprotection assay. This compound class is suitable as probes for biochemical investigation of large T antigen ATPase activity, but because of their cytotoxicity, further optimization is necessary for their use in studying polyomavirus replication in vivo.
Illustrating the coupled human–environment system for vulnerability analysis: Three case studies

The vulnerability framework of the Research and Assessment Systems for Sustainability Program explicitly recognizes the coupled human–environment system and accounts for interactions in the coupling affecting the system's responses to hazards and its vulnerability. This paper illustrates the usefulness of the vulnerability framework through three case studies: the tropical southern Yucatán, the arid Yaqui Valley of northwest Mexico, and the pan-Arctic. Together, these examples illustrate the role of external forces in reshaping the systems in question and their vulnerability to environmental hazards, as well as the different capacities of stakeholders, based on their access to social and biophysical capital, to respond to the changes and hazards. The framework proves useful in directing attention to the interacting parts of the coupled system and helps identify gaps in information and understanding relevant to reducing vulnerability in the systems as a whole.
Constraints on the applicability of the organic temperature proxies U K' 37 , TEX 86 and LDI in the subpolar region around Iceland

Abstract. Subpolar regions are key areas for studying natural climate variability due to their high sensitivity to rapid environmental changes, particularly through sea surface temperature (SST) variations. Here, we have tested three independent organic temperature proxies (UK'37; TEX86; and the long-chain diol index, LDI) regarding their potential applicability for SST reconstruction in the subpolar region around Iceland. UK'37, TEX86 and TEXL86 temperature estimates from suspended particulate matter showed a substantial discrepancy with instrumental data, while long-chain alkyl diols were below the detection limit at most of the stations. In the northern Iceland Basin, sedimenting particles revealed a seasonality in lipid fluxes, i.e., high fluxes of alkenones and glycerol dialkyl glycerol tetraethers (GDGTs) were measured during late spring and during summer and high fluxes of long-chain alkyl diols during late summer. The flux-weighted average temperature estimates had a significant negative (ca. 2.3 °C for UK'37) and positive (up to 5 °C for TEX86) offset with satellite-derived SSTs and temperature estimates derived from the underlying surface sediment. UK'37 temperature estimates from surface sediments around Iceland correlate well with summer mean sea surface temperatures, while TEX86-derived temperatures correspond with both annual and winter mean 0–200 m temperatures, suggesting a subsurface temperature signal. Anomalous LDI-SST values in surface sediments and low mass flux of 1,13- and 1,15-diols compared to 1,14-diols suggest that Proboscia diatoms are the major sources of long-chain alkyl diols in this area rather than eustigmatophyte algae, and therefore the LDI cannot be applied in this region.
Prediction of the grain size evolution during thermal treatments at the mesoscopic scale: a numerical framework and industrial examples

Recently, an original full field model working at the mesoscopic scale using the level set (LS) method in a finite element (FE) framework has been introduced. This approach has demonstrated its potential for the simulation of grain growth and recrystallization problems. Through the development of the DIGIMU ® software, this methodology is now considered for industrial applications. The paper presents (i) the recent developments made on the LS approach and (ii) some examples of large scale simulations in two and three dimensions considering thermal treatments applied on materials. Grain boundaries motion considering the presence or not of second phase particles (like precipitates) are investigated.
Bilingual English-Czech Valency Lexicon Linked to a Parallel Corpus

This paper presents a resource and the associated annotation process used in a project of interlinking Czech and English verbal translational equivalents based on a parallel, richly annotated dependency treebank containing also valency and semantic roles, namely the Prague Czech-English Dependency Treebank. One of the main aims of this project is to create a high-quality and relatively large empirical base which could be used both for linguistic comparative research as well as for natural language processing applications, such as machine translation or cross-language sense disambiguation. This paper describes the resulting lexicon, CzEngVallex, and the process of building it, as well some interesting observations and statistics already obtained.
The Hippo pathway acts via p53 and microRNAs to control proliferation and proapoptotic gene expression during tissue growth

Summary The Hippo pathway has a central role in coordinating tissue growth and apoptosis. Mutations that compromise Hippo pathway activity cause tissue overgrowth and have been causally linked to cancer. In Drosophila, the transcriptional coactivator Yorkie mediates Hippo pathway activity to control the expression of cyclin E and Myc to promote cell proliferation, as well as the expression of bantam miRNA and DIAP1 to inhibit cell death. Here we present evidence that the Hippo pathway acts via Yorkie and p53 to control the expression of the proapoptotic gene reaper. Yorkie further mediates reaper levels post-transcriptionally through regulation of members of the miR-2 microRNA family to prevent apoptosis. These findings provide evidence that the Hippo pathway acts via several distinct routes to limit proliferation-induced apoptosis.
The multi-bit watermarking method for speech signals in the time-frequency domain

In this paper, we introduce a new multi-bit watermarking method for speech signals in the joint time-frequency domain. For this purpose, we compute the Wigner distribution of the speech signal and embed the watermark in a subset of time-frequency cells. The problem of synthesizing the signal from the watermarked Wigner distribution is discussed and a simple algorithm to reconstruct the speech signal from its Wigner distribution is proposed. The performance of the proposed watermarking algorithm is illustrated for sample attacks.
Metabolite Fingerprinting of Eugenia jambolana Fruit Pulp Extracts using NMR, HPLC-PDA-MS, GC-MS, MALDI-TOF-MS and ESI-MS/MS Spectrometry

Eugenia jambolana, commonly known as ‘jamun’ or Indian blackberry, is an important source of bioactive compounds. All parts of the plant like stem bark, leaves, flower, fruit pulp and seeds are traditionally used for many diseases. Metabolite profiling in medicinally important plants is critical to resolve the problems associated with standardization and quality control. Metabolite profiling of the fruit pulp of Jamun was performed by NMR, HPLC, MS, GC-MS and MALDI-TOF mass spectrometry. These hyphenated techniques helped in the identification of 68 chemically-diverse metabolites of the fruit pulp. These include anthocyanins, anthocyanidins, sugars, phenolics and volatile compounds. Five extracts of fruit pulp were prepared i.e. hexane, chloroform, ethylacetate, butanol and aqueous methanolic. Twenty-five metabolites identified and quantified in the n-butanol and aqueous-methanolic extracts of ripe jamun fruit by qNMR. LC-PDA-MS and MALDI-TOF spectrometry helped in deciphering thirty-nine metabolites out of which thirteen were quantified.
Rapid diagnostic susceptibility testing of bacteria and fungi from clinical samples using silicon gratings

Bacterial and fungal infections persistently plague society and have amounted to one of the most prevalent issues in healthcare today. Thus, significant research effort is directed towards developing rapid diagnostic techniques for determination of the correct antibiotic (or antifungal) for a patient-tailored therapy. We have developed a rapid phenotypic antimicrobial susceptibility testing (AST) in which photonic 2D silicon microarrays are employed as both the optical transducer element and as a preferable solid−liquid interface for bacterial/fungal colonization. We harness the intrinsic ability of the micro-architectures to relay optical phase-shift reflectometric interference spectroscopic measurements (termed PRISM) and incorporate it into a platform for culture-free, label-free tracking of bacterial/fungal colonization, proliferation, and death. For example, bacteria proliferation within the microtopologies results in an increase in refractive index of the medium, yielding an increase in optical path difference, while cell death or bacteriostatic activity results in decreasing or unchanged values. The optical responses of bacteria, including clinical isolates and samples derived from patients at neighboring hospitals, to various concentrations of relevant antibiotics are tracked in real time, allowing for accurate determination of the minimum inhibitory concentration (MIC) values within 2-3 hours in comparison to assay times of <8 hours (using standard broth microdilution techniques or state-of-the-art clinical automated systems. This has opened the door to the observation of unique bacterial behaviors, as we can evaluate bacterial adhesion, growth, and antibiotic resistance on different micro-architectures, different surface chemistries, and even different strains. Motility, charge, and biofilm abilities have been explored for their effect of bacterial adhesion to the microstructures as we further develop our method of rapid, label-free AST for full clinical application.
Scalable network distance browsing in spatial databases

An algorithm is presented for finding the k nearest neighbors in a spatial network in a best-first manner using network distance. The algorithm is based on precomputing the shortest paths between all possible vertices in the network and then making use of an encoding that takes advantage of the fact that the shortest paths from vertex u to all of the remaining vertices can be decomposed into subsets based on the first edges on the shortest paths to them from u. Thus, in the worst case, the amount of work depends on the number of objects that are examined and the number of links on the shortest paths to them from q, rather than depending on the number of vertices in the network. The amount of storage required to keep track of the subsets is reduced by taking advantage of their spatial coherence which is captured by the aid of a shortest path quadtree. In particular, experiments on a number of large road networks as well as a theoretical analysis have shown that the storage has been reduced from O(N3) to O(N1.5) (i.e., by an order of magnitude equal to the square root). The precomputation of the shortest paths along the network essentially decouples the process of computing shortest paths along the network from that of finding the neighbors, and thereby also decouples the domain S of the query objects and that of the objects from which the neighbors are drawn from the domain V of the vertices of the spatial network. This means that as long as the spatial network is unchanged, the algorithm and underlying representation of the shortest paths in the spatial network can be used with different sets of objects.
A Novel Role of the Mammalian GSPT/eRF3 Associating with Poly(A)-binding Protein in Cap/Poly(A)-dependent Translation*

The mammalian GSPT, which consists of amino-terminal (N) and carboxyl-terminal (C) domains, functions as the eukaryotic releasing factor 3 (eRF3) by interacting with eRF1 in translation termination. This function requires only the C-domain that is homologous to the elongation factor (EF) 1α, while the N-domain interacts with polyadenylate-binding protein (PABP), which binds the poly(A) tail of mRNA and associates with the eukaryotic initiation factor (eIF) 4G. Here we describe a novel role of GSPT in translation. We first determined an amino acid sequence required for the PABP interaction in the N-domain. Inhibition of this interaction significantly attenuated translation of capped/poly(A)-tailed mRNA not only in an in vitro translation system but also in living cells. There was a PABP-dependent linkage between the termination factor complex eRF1-GSPT and the initiation factor eIF4G associating with 5′ cap through eIF4E. Although the inhibition of the GSPT-PABP interaction did not affect the de novoformation of an 80 S ribosomal initiation complex, it appears to suppress the subsequent recycle of ribosome. These results indicate that GSPT/eRF3 plays an important role in translation cycle through the interaction with PABP, in addition to mediating the termination with eRF1.
Collaborative information linking: Bridging knowledge gaps between users by linking across applications

Information exploration processes are often conducted in teams of experts, family members, or colleagues. These teams have to retrieve information from different sources, verify it, and finally compare and discuss their findings to find consensus. Today, support for these collaborative processes is limited and users often end up sharing either a single PC with one user taking control or using separate workstations, where support for tight collaboration is limited. In this paper, we present collaborative information linking which visually connects information across private and shared application windows to bridge knowledge gaps between users. We present the technical infrastructure for multi-user interaction and personalized meta-visualizations on large multi-projector displays, and demonstrate how personalized visual links connect information across existing applications modified in a minimally invasive manner. An observational experiment showed that information linking helps individuals to deal with large display space and teams to switch between individual information retrieval and joint verification and discussion.
Structural Analysis of Musical Signals for Indexing , Segmentation and Thumbnailing

A musical piece typically has a repetitive structure. Analysis of this structure will be useful for indexing the digital music repository, segmenting music at transitions, and summarizing the thumbnails of music, all of which can be used in building efficient music retrieval and recommendation systems. This paper presents an algorithm that can automatically analyze the repetitive structure of musical signals. First, the algorithm detects the repetition of each segment of fixed length in a piece using dynamic programming. Second, the algorithm summarizes this repetition information and infers the structure based on heuristic rules. The performance of the approach is evaluated visually using figures for qualitative evaluation, and by two structural similarity measures for quantitative evaluation. The structural analysis result can be used in various ways in addition to the apparent use for indexing. The boundaries of the sections based on the structural analysis result have better consistency with transitions than boundaries produced by random segmentation. We can also employ different strategies based on structural analysis for music thumbnailing. Preliminary results obtained using a corpus of Beatles’ songs demonstrate the promise of these methods.
The exploration of machine learning for abnormal prediction model of telecom business support system

Companies leverage plenty of monitoring tools collecting performance metrics of Telecom BSS to assure it is in good status. The influence of the system failure is variance, depending on the length of time to finish the system reparation. The metrics collected by monitoring tools may have the indication of the system failure, and the maintainers have chances to foresee a system failure from those metrics. However, the metrics collected by the monitoring tools are too much, and some hints may hide in combinations of multiple metrics. We leverage machine learning approaches to address this problem. We used several machine learning tools and algorithms to explore the configuration of the machine learning models to obtain the model performing the best to our dataset. We compared many algorithms like linear SVM, SVM with RBF kernel, random forest and fully connected neural network. We also introduced an anomaly detection learning technique to see if better performance can be achieved. We found SVM with RBF kernel can achieve the best performance to our dataset, and we conducted a comprehensive grid search of the hyperparameters of the RBF SVM to found the best configuration to our dataset. We achieve F-score 21 in the final explored result and the model can predict 15% of the system failure 60 minutes in advance.
Antagonistic effect of Streptomyces spp. on spore germination and mycelial growth of Fusarium oxysporum f.sp. lycopersici

Objectives of the research were to examine the antagonist effect of Streptomyces on spore germination and fungal mycelium growth of F. oxysporum f.sp. lycopersici. The isolates of Streptomyces was collected from the rhizosphere soil of tomato and chili orchards in Malang. Streptomyces isolation was done through dilution method and isolates was grown in Glucose Nitrate Agar media. The antagonist test between Streptomyces and F. oxysporum f.sp. lycopersici was tested using dual culture methods. The effect of Streptomyces antibiotic on the germination of F. oxysporum f.sp. lycopersici conidia was examined on slide glass. Result of the research gained 8 isolates of tomato and 3 isolates of chili. Five isolates of tomato and 3 isolates of chili produces antibiotics which caused swelling and bubbling up of the cell walls, both spore and fungal mycelium of F. oxysporum f.sp. lycopersici. Damages on the cell walls inhibit the spore germination of F. oxysporum f.sp. lycopersici for about 67 - 99 %. The isolates also inhibit the mycelium growth for about 47- 81%.
Beyond the Sum of Parts: Voting with Groups of Dependent Entities

The high complexity of multi-scale, category-level object detection in cluttered scenes is efficiently handled by Hough voting methods. However, the main shortcoming of the approach is that mutually dependent local observations are independently casting their votes for intrinsically global object properties such as object scale. Object hypotheses are then assumed to be a mere sum of their part votes. Popular representation schemes are, however, based on a dense sampling of semi-local image features, which are consequently mutually dependent. We take advantage of part dependencies and incorporate them into probabilistic Hough voting by deriving an objective function that connects three intimately related problems: i) grouping mutually dependent parts, ii) solving the correspondence problem conjointly for dependent parts, and iii) finding concerted object hypotheses using extended groups rather than based on local observations alone. Early commitments are avoided by not restricting parts to only a single vote for a locally best correspondence and we learn a weighting of parts during training to reflect their differing relevance for an object. Experiments successfully demonstrate the benefit of incorporating part dependencies through grouping into Hough voting. The joint optimization of groupings, correspondences, and votes not only improves the detection accuracy over standard Hough voting and a sliding window baseline, but it also reduces the computational complexity by significantly decreasing the number of candidate hypotheses.
Two-dimensional blind Bayesian deconvolution of medical ultrasound images

A new approach to 2-D blind deconvolution of ultrasonic images in a Bayesian framework is presented. The radio-frequency image data are modeled as a convolution of the point-spread function and the tissue function, with additive white noise. The deconvolution algorithm is derived from statistical assumptions about the tissue function, the point-spread function, and the noise. It is solved as an iterative optimization problem. In each iteration, additional constraints are applied as a projection operator to further stabilize the process. The proposed method is an extension of the homomorphic deconvolution, which is used here only to compute the initial estimate of the point-spread function. Homomorphic deconvolution is based on the assumption that the point-spread function and the tissue function lie in different bands of the cepstrum domain, which is not completely true. This limiting constraint is relaxed in the subsequent iterative deconvolution. The deconvolution is applied globally to the complete radiofrequency image data. Thus, only the global part of the point-spread function is considered. This approach, together with the need for only a few iterations, makes the deconvolution potentially useful for real-time applications. Tests on phantom and clinical images have shown that the deconvolution gives stable results of clearly higher spatial resolution and better defined tissue structures than in the input images and than the results of the homomorphic deconvolution alone.