Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time 3D reconstruction of complex scenes using single-photon lidar: when image processing meets computer graphics

Single-photon lidar has emerged as a prime candidate technology for depth imaging in extreme scenarios, such as through fog, clutter or in underwater environments. Until now, a major challenge has been the rapid reconstruction of single-photon data, to permit the real-time analysis of complex moving scenes. Here we show a new computational framework for real-time three-dimensional (3D) reconstruction of complex scenes from singlephoton data, taking full advantage of the rapid developments in single-photon avalanche diode detector array technology. By combining statistical models and highly scalable computational tools from the computer graphics community, we demonstrate 3D reconstruction of multiple-surface scenes with processing times reduced by a factor of up to 10 compared with state-of-the-art algorithms. This has enabled robust, real-time target reconstruction of complex scenes, paving the way for single-photon lidar at video rates for practical 3D imaging applications, including automotive, defence and underwater imaging.
Residual cholesteatoma: incidence and localization in canal wall down tympanoplasty with soft-wall reconstruction.

OBJECTIVE To compare the incidence and localization of residual cholesteatomas in canal wall down tympanoplasty with soft-wall reconstruction with results with the canal wall down and open tympanoplasty or canal wall up tympanoplasty. DESIGN Retrospective case-series study. SETTING Tertiary care university hospital. PATIENTS Eighty-five patients (85 ears) with fresh extensive cholesteatomas who underwent canal wall down tympanoplasty with soft-wall reconstruction as first-stage surgery and a second operation after 1 year to confirm residual cholesteatomas and perform ossiculoplasty. MAIN OUTCOME MEASURES The incidence and localization of residual cholesteatomas in the middle ear were compared between surgery using the canal wall down and open tympanoplasty and canal wall up tympanoplasty. Possible technical causes of the residua were reviewed in a retrospective videotape analysis of the first-stage operations. RESULTS Of the 85 ears operated on, 18 had residual cholesteatomas, for an overall incidence of 21%, with 1 residuum per ear. Six cholesteatomas were located in the epitympanum (33%), 3 in the sinus tympani (17%), 3 in the antrum (17%), 2 on the stapes (11%), 2 on the tympanic membrane (11%), 1 on the tympanic portion of the facial canal (6%), and 1 just under the skin of the external auditory canal (6%). The retrospective videotape analysis revealed that the main cause of residual cholesteatomas in the epitympanum and sinus tympani was incomplete removal of the matrix under an indirect surgical view because of insufficient drilling. Residual matrix in a bony defect in the middle cranial fossa or facial canal was the cause of residual cholesteatomas in the antrum or facial canal. Inappropriate keratinizing epithelium rolling during tympanic membrane or external auditory canal reconstruction was the cause of residual cholesteatomas in the tympanic membrane or external auditory canal. CONCLUSIONS The incidence of residual cholesteatomas in patients who underwent canal wall down tympanoplasty with soft-wall reconstruction was similar to that in patients who underwent surgery involving the canal wall down and open tympanoplasty or canal wall up tympanoplasty. In terms of localization, with canal wall down tympanoplasty with soft-wall reconstruction, there is the possibility of residua not only in the tympanic cavity but also in the antrum or mastoid cavity, as with the canal wall up method. Results of this study suggest that in patients with extensive cholesteatoma, canal wall down tympanoplasty with soft-wall reconstruction should be followed by a second procedure to detect any residual cholesteatomas in the tympanic cavity, antrum, or mastoid cavity.
Stability of Linear Structural Equation Models of Causal Inference

We consider the numerical stability of the parameter recovery problem in Linear Structural Equation Model ($\LSEM$) of causal inference. A long line of work starting from Wright (1920) has focused on understanding which sub-classes of $\LSEM$ allow for efficient parameter recovery. Despite decades of study, this question is not yet fully resolved. The goal of this paper is complementary to this line of work; we want to understand the stability of the recovery problem in the cases when efficient recovery is possible. Numerical stability of Pearl's notion of causality was first studied in Schulman and Srivastava (2016) using the concept of condition number where they provide ill-conditioned examples. In this work, we provide a condition number analysis for the $\LSEM$. First we prove that under a sufficient condition, for a certain sub-class of $\LSEM$ that are \emph{bow-free} (Brito and Pearl (2002)), the parameter recovery is stable. We further prove that \emph{randomly} chosen input parameters for this family satisfy the condition with a substantial probability. Hence for this family, on a large subset of parameter space, recovery is numerically stable. Next we construct an example of $\LSEM$ on four vertices with \emph{unbounded} condition number. We then corroborate our theoretical findings via simulations as well as real-world experiments for a sociology application. Finally, we provide a general heuristic for estimating the condition number of any $\LSEM$ instance.
Visual Analytics of Anomalous User Behaviors: A Survey

With the pervasive use of information technologies, the increasing availability of data provides new opportunities for understanding user behaviors. Unearthing anomalies in user behavior is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. In this article, we survey state-of-the-art research work in visual analytics of anomalous user behaviors and classify them into four application domains, which are social interaction, travel, network communication, and financial transaction. We further examine the research work in each category in terms of data types, visualization techniques, and interactive analysis methods. We hope that our survey can provide systematic guidelines for researchers and practitioners to find effective solutions to their research problems in specific application domains. Finally, we discuss trends of academic interest over the past decades and suggest potential directions across visual analytics of these user behaviors for future research.
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA

We consider the problem of recovering a common latent source with independent components from multiple views. This applies to settings in which a variable is measured with multiple experimental modalities, and where the goal is to synthesize the disparate measurements into a single unified representation. We consider the case that the observed views are a nonlinear mixing of component-wise corruptions of the sources. When the views are considered separately, this reduces to nonlinear Independent Component Analysis (ICA) for which it is provably impossible to undo the mixing. We present novel identifiability proofs that this is possible when the multiple views are considered jointly, showing that the mixing can theoretically be undone using function approximators such as deep neural networks. In contrast to known identifiability results for nonlinear ICA, we prove that independent latent sources with arbitrary mixing can be recovered as long as multiple, sufficiently different noisy views are available.
Hospital Volume, Length of Stay, and Readmission Rates in High-Risk Surgery

Objective Aimed at reducing surgical deaths, several recent initiatives have attempted to establish volume-based referral strategies in high-risk surgery. Although payers are leading the most visible of these efforts, it is unknown whether volume standards will also reduce resource use. Methods We studied postoperative length of stay and 30-day readmission rate after 14 cardiovascular and cancer procedures using the 1994-1999 national Medicare database (total n = 2.5 million). We used regression techniques to examine the relationship between length of stay, 30-day readmission, and hospital volume, adjusting for age, gender, race, comorbidity score, admission acuity, and mean social security income. Results Mean postoperative length of stay ranged from 3.4 days (carotid endarterectomy) to 19.6 days (esophagectomy). There was no consistent relationship between volume and mean length of stay; it significantly increased across volume strata for 7 of the 14 procedures and significantly decreased across volume strata for the other 7. Mean length of stay at very-low-volume and very-high-volume hospitals differed by more than 1 day for 6 procedures. Of these, the mean length of stay was shorter in high-volume hospitals for 3 procedures (pancreatic resection, esophagectomy, cystectomy), but longer for other procedures (aortic and mitral valve replacement, gastrectomy). The 30-day readmission rate also varied widely by procedure, ranging from 9.9% (nephrectomy) to 22.2% (mitral valve replacement). However, volume was not related to 30-day readmission rate with any procedure. Conclusion Although hospital volume may be an important predictor of operative mortality, it is not associated with resource use as reflected by length of stay or readmission rates.
Absolute Pitch Does Not Depend on Early Musical Training

Abstract: The etiology and defining characteristics of absolute pitch (AP) have been controversial. To test the importance of musical training in the development of this skill, we developed a new paradigm for identifying AP that is independent of a subject's musical experience. We confirm the efficacy of the paradigm using classically defined AP and non‐AP musicians. We then present data from a nonmusician who nevertheless appears to possess AP. We conclude that musical training is not necessary for the development of AP.
1 Building a Knowledge Graph for the Air Traffic Management Community

Historically, most of the focus in the knowledge graph community has been on the support for web, social network, or product search applications. This paper describes some of our experience in developing a largescale applied knowledge graph for a more technical audience with more specialized information access and analysis needs – the air traffic management community. We describe ATMGRAPH, a knowledge graph created by integrating various sources of structured aviation data, provided in large part by US federal agencies. We review some of the practical challenges we faced in creating this knowledge graph.
Design of a Low-power and Small-area Approximate Multiplier using First the Approximate and then the Accurate Compression Method

Recently emerging applications, such as convolution neural networks (CNNs), which process thousands of convolutional computations, require a large amount of power. Multiplication is the key arithmetic in these applications and an approximate multiplier has the potential to reduce both power and area. In this study, first, we propose the approximate and, then, the accurate compression method for an 8-bit multiplier. The proposed compression method single handedly reduces eight rows of partial products into three and, then, thoroughly processes the rows into two. Comparison of the conventional Wallace tree multiplier demonstrates that the proposed approximate multiplier with the compression reduces power and area by 73.7% and 60.3%, respectively. In addition, compared with two existing low-power approximate multipliers, the proposed multiplier achieves higher reductions than these state-of-the-art multipliers relative to power, area, and delay, with undegraded accuracy. These improvements can be also found from the results of a popular CNN application.
Characterizing Approximate Adders and Multipliers Optimized under Different Design Constraints

Taking advantage of the error resilience in many applications as well as the perceptual limitations of humans, numerous approximate arithmetic circuits have been proposed that trade off accuracy for higher speed or lower power in emerging applications that exploit approximate computing. However, characterizing the various approximate designs for a specific application under certain performance constraints becomes a new challenge. In this paper, approximate adders and multipliers are evaluated and compared for a better understanding of their characteristics when the implementations are optimized for performance or power. Although simple truncation can effectively reduce the hardware of an arithmetic circuit, it is shown that some other designs perform better in speed, power and power-delay product. For instance, many approximate adders have a higher performance than a truncated adder. A truncated multiplier is faster but consumes a higher power than most approximate designs for achieving a similar mean error magnitude. The logarithmic multipliers are very fast and power-efficient at a lower accuracy. Approximate multipliers can also be generated by an automated process to be very efficient while ensuring a sufficiently high accuracy.
Epidermal growth factor receptor gene mutations and increased copy numbers predict gefitinib sensitivity in patients with recurrent non-small-cell lung cancer.

PURPOSE To evaluate epidermal growth factor receptor (EGFR) mutations and copy number as predictors of clinical outcome in patients with non-small-cell lung cancer (NSCLC) receiving gefitinib. PATIENTS AND METHODS Sixty-six patients with NSCLC who experienced relapse after surgery and received gefitinib were included. Direct sequencing of exons 18 to 24 of EGFR and exons 18 to 24 of ERBB2 was performed using DNA extracted from surgical specimens. Pyrosequencing and quantitative real-time polymerase chain reaction were performed to analyze the allelic pattern and copy number of EGFR. RESULTS Thirty-nine patients (59%) had EGFR mutations; 20 patients had deletional mutations in exon 19, 17 patients had missense mutations (L858R) in exon 21, and two patients had missense mutations (G719S or G719C) in exon 18. No mutations were identified in ERBB2. Response rate (82% [32 of 39 patients] v 11% [three of 27 patients]; P < .0001), time to progression (TTP; median, 12.6 v 1.7 months; P < .0001), and overall survival (median, 20.4 v 6.9 months; P = .0001) were significantly better in patients with EGFR mutations than in patients with wild-type EGFR. Increased EGFR copy numbers (> or = 3/cell) were observed in 29 patients (44%) and were significantly associated with a higher response rate (72% [21 of 29 patients] v 38% [14 of 37 patients]; P = .005) and a longer TTP (median, 9.4 v 2.6 months; P = .038). High EGFR copy numbers (> or = 6/cell) were caused by selective amplification of mutant alleles. CONCLUSION EGFR mutations and increased copy numbers were significantly associated with better clinical outcome in gefitinib-treated NSCLC patients.
Voltage-Controlled Magnetoelectric Memory Bit-cell Design With Assisted Body-bias in FD-SOI

Voltage-controlled magnetic anisotropy (VCMA)-magnetic tunnel junction (MTJ) is incorporated into FD-SOI CMOS technology. The design space of 1 transistor-1 MTJ (1T-1M) bit-cell is explored through varied VCMA pulse duration/amplitude and scaling down transistor dimensions. The design point with 1.1 V VCMA pulse amplitude, 0.44 ns pulse duration and W/L = 400 nm/30 nm access transistor shows the ultra low write energy in VCMA-MTJ based bit-cell. It achieves a minimum 3.18 fJ/bit switching energy with 28-nm FD-SOI process. Access transistor sizing is studied, while the ultra low power implementation may lead to MTJ switching failure. Voltage assisted techniques for failure mitigation are proposed based on body-bias generator (BBG). The BBG not only provides VCMA pulse signal to control MTJ barrier, but also generates body-bias to boost the transistor performance. In the presence of forward body-bias (FBB) and increased VCMA pulse level, the proposed strategy is effective in switching failure compensation as well as writing delay improvement.
Summary of the National Toxicology Program's report of the endocrine disruptors low-dose peer review.

At the request of the U.S. Environmental Protection Agency (U.S. EPA), the National Toxicology Program organized an independent and open peer review to evaluate the scientific evidence on low-dose effects and nonmonotonic dose-response relationships for endocrine-disrupting chemicals in mammalian species. For this peer review, "low-dose effects" referred to biologic changes that occur in the range of human exposures or at doses lower than those typically used in the standard testing paradigm of the U.S. EPA for evaluating reproductive and developmental toxicity. The demonstration that an effect is adverse was not required because in many cases the long-term health consequences of altered endocrine function during development have not been fully characterized. A unique aspect of this peer review was the willing submission of individual animal data by principal investigators of primary research groups active in this field and the independent statistical reanalyses of selected parameters prior to the peer review meeting by a subpanel of statisticians. The expert peer-review panel (the panel) also considered mechanistic data that might influence the plausibility of low-dose effects and identified study design issues or other biologic factors that might account for differences in reported outcomes among studies. The panel found that low-dose effects, as defined for this review, have been demonstrated in laboratory animals exposed to certain endocrine-active agents. In some cases where low-dose effects have been reported, the findings have not been replicated. The shape of the dose-response curves for reported effects varied with the end point and dosing regimen and were low-dose linear, threshold-appearing, or nonmonotonic. The findings of the panel indicate that the current testing paradigm used for assessments of reproductive and developmental toxicity should be revisited to see whether changes are needed regarding dose selection, animal-model selection, age when animals are evaluated, and the end points being measured following exposure to endocrine-active agents.
Visualization of the Attachment Organelle and Cytadherence Proteins of Mycoplasma pneumoniae by Immunofluorescence Microscopy

ABSTRACT A method was developed for protein localization in Mycoplasma pneumoniae by immunofluorescence microscopy. The P1 adhesin protein was revealed to be located at least at one cell pole in all adhesive cells, as has been observed by immunoelectron microscopy. Cell images were classified according to P1 localization and assigned by DNA content. Cells with a single P1 focus at one cell pole had a lower DNA content than cells with two foci, at least one of which was positioned at a cell pole. Those with one focus at each cell pole had the highest DNA content, suggesting that the nascent attachment organelle is formed next to the old one and migrates to the opposite cell pole before cell division. Double staining revealed that the accessory proteins for cytadherence—HMW1, HMW3, P30, P90, P40, and P65—colocalized with the P1 adhesin in all cells. The localization of cytadherence proteins was also examined in cytadherence-deficient mutant cells with a branched morphology. In M5 mutant cells, which lack the P90 and P40 proteins, HMW1, HMW3, P1, and P30 were focused at the cell poles of short branches, and P65 showed no signal. In M7 mutant cells, which produce a truncated P30 protein, HMW1, HMW3, P1, P90, and P40 were focused, and P65 showed no signal. In M6 mutant cells, which express no HMW1 and a truncated P30 protein, the P1 adhesin was distributed throughout the entire cell body, and no signal was detected for the other proteins. These results suggest that the cytadherence proteins are sequentially assembled to the attachment organelle with HMW1 first, HMW3, P1, P30, P90, and P40 next, and P65 last.
A Systolic SNN Inference Accelerator and its Co-optimized Software Framework

Although Deep Neural Network (DNN) architectures have made some breakthroughs in computer vision tasks, they are not close to biological brain neurons. Spiking Neural Network (SNN) is highly expected to bridge the gap between artificial computing systems and bio-systems. And it also shows great potential in low power computing. This paper presents a low power hardware accelerator for SNN inference using systolic array, and a corresponding software framework for optimization. First, we give the hardware design which adopts systolic array inspired by explorations of SNN. Then we ensure correct data mapping for systolic array for the sake of computational correctness. Next, we use compression methods for decreasing both the runtime and memory footprint. Finally, we make the systolic array size-configurable to adapt to different input, so as to reduce computational overhead. We implement the accelerator on Xilinx FPGA V7 690T. The experimental results show that SNN inference on our scheme suffers little loss on accuracy (less than 0.1%) on MNIST and Fashion-MNIST, and the runtime of the time-consuming layers decreases. The total power of our scheme is 0.745 W at 100 MHz.
Design and implementation of content based page sharing method in Xen

Modern computers are not only sufficiently powerful but also inexpensive. However there are few opportunities for application programs to use its capabilities. On the other hand, the number of computers has been increasing in the business sectors and other fields. Thus there are a lot of needless modern computer utilizations. As these solutions, researchers interested with the utilization of virtual machines (VMs) have been rising. The VM system enables consolidation of several real machines. As we consolidate real machine, another problem occurs. It may run several the same operating systems (OSs) which have identical application program, component and data on a machine. In this case, real machinepsilas memory has same contents. If many machines are consolidated by VM, it is verbose. We have been developing original VM system based on open source and high-quality VM system Xen. In this paper, we improved memory management system of Xen and consider the method for decreasing verbose memory on real machine. We propose the design and implementation method of content based page sharing method in Xen.
Involvement of TREK-1 Channel in Cell Viability of H9c2 Rat Cardiomyoblasts Affected by Bupivacaine and Lipid Emulsion

Lipid emulsion (LE) therapy has been used to reduce overdose of bupivacaine (BPV)-induced cardiotoxicity. The TWIK-related potassium channel-1 (TREK-1) is inhibited by BPV and activated by polyunsaturated fatty acids, which are the main component in LE. These pharmacological properties inspired us to investigate whether the TREK-1 channel is associated with cell viability of H9c2 cardiomyoblasts affected by BPV and LE. Consistent with previous studies, BPV-induced cell death was reduced by LE treatment. The reduction in the TREK-1 expression level by BPV was alleviated by LE. The BPV cytotoxicity highly decreased in TREK-1 overexpressed cells but was the opposite in TREK-1 knocked-down cells. TREK-1 channel activators and inhibitors increased and decreased cell viability, respectively. BPV-induced depolarization of the plasma and mitochondrial membrane potential and increase in intracellular Ca2+ level were blocked by LE treatment. BPV-induced depolarization of membrane potential was reduced in TREK-1 overexpressed cells, indicating that TREK-1 channels mediate setting the resting membrane potentials as a background K+ channel in H9c2 cells. These results show that TREK-1 activity is involved in the BPV cytotoxicity and the antagonistic effect of LE in H9c2 cells and suggest that TREK-1 could be a target for action of BPV and LE.