Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain Cell Death Is Reduced With Cooling by 3.5°C to 5°C but Increased With Cooling by 8.5°C in a Piglet Asphyxia Model

Background and Purpose— In infants with moderate to severe neonatal encephalopathy, whole-body cooling at 33°C to 34°C for 72 hours is standard care with a number needed to treat to prevent a adverse outcome of 6 to 7. The precise brain temperature providing optimal neuroprotection is unknown. Methods— After a quantified global cerebral hypoxic-ischemic insult, 28 piglets aged <24 hours were randomized (each group, n=7) to (1) normothermia (38.5°C throughout) or whole-body cooling 2 to 26 hours after insult to (2) 35°C, (3) 33.5°C, or (4) 30°C. At 48 hours after hypoxia-ischemia, delayed cell death (terminal deoxynucleotidyl transferase deoxyuridine triphosphate nick end labeling and cleaved caspase 3) and microglial ramification (ionized calcium-binding adapter molecule 1) were evaluated. Results— At 48 hours after hypoxia-ischemia, substantial cerebral injury was found in the normothermia and 30°C hypothermia groups. However, with 35°C and 33.5°C cooling, a clear reduction in delayed cell death and microglial activation was observed in most brain regions (P<0.05), with no differences between 35°C and 33.5°C cooling groups. A protective pattern was observed, with U-shaped temperature dependence in delayed cell death in periventricular white matter, caudate nucleus, putamen, hippocampus, and thalamus. A microglial activation pattern was also seen, with inverted U-shaped temperature dependence in periventricular white matter, caudate nucleus, internal capsule, and hippocampus (all P<0.05). Conclusions— Cooling to 35°C (an absolute drop of 3.5°C as in therapeutic hypothermia protocols) or to 33.5°C provided protection in most brain regions after a cerebral hypoxic-ischemic insult in the newborn piglet. Although the relatively wide therapeutic range of a 3.5°C to 5°C drop in temperature reassured, overcooling (an 8.5°C drop) was clearly detrimental in some brain regions.
Recent Progress on C-RAN Centralization and Cloudification

This paper presents the latest progress on cloud RAN (C-RAN) in the areas of centralization and virtualization. A C-RAN system centralizes the baseband processing resources into a pool and virtualizes soft base-band units on demand. The major challenges for C-RAN including front-haul and virtualization are analyzed with potential solutions proposed. Extensive field trials verify the viability of various front-haul solutions, including common public radio interface compression, single fiber bidirection and wavelength-division multiplexing. In addition, C-RANs facilitation of coordinated multipoint (CoMP) implementation is demonstrated with 50%-100% uplink CoMP gain observed in field trials. Finally, a test bed is established based on general purpose platform with assisted accelerators. It is demonstrated that this test bed can support multi-RAT, i.e., Time-Division Duplexing Long Term Evolution, Frequency-Division Duplexing Long Term Evolution, and Global System for Mobile Communications efficiently and presents similar performance to traditional systems.
Adaptive Self-Paced Deep Clustering with Data Augmentation

Deep clustering gains superior performance than conventional clustering by jointly performing feature learning and cluster assignment. Although numerous deep clustering algorithms have emerged in various applications, most of them fail to learn robust cluster-oriented features which in turn hurts the final clustering performance. To solve this problem, we propose a two-stage deep clustering algorithm by incorporating data augmentation and self-paced learning. Specifically, in the first stage, we learn robust features by training an autoencoder with examples that are augmented by random shifting and rotating the given clean examples. Then, in the second stage, we encourage the learned features to be cluster-oriented by alternatively finetuning the encoder with the augmented examples and updating the cluster assignments of the clean examples. During finetuning the encoder, the target of each augmented example in the loss function is the center of the cluster to which the clean example is assigned. The targets may be computed incorrectly, and the examples with incorrect targets could mislead the encoder network. To stabilize the network training, we select most confident examples in each iteration by utilizing the adaptive self-paced learning. Extensive experiments validate that our algorithm outperforms the state of the arts on four image datasets.
A Moment Majorization principle for random matrix ensembles with applications to hardness of the noncommutative Grothendieck problem

We prove a moment majorization principle for matrix-valued functions with domain $\{-1,1\}^{m}$, $m\in\mathbb{N}$. The principle is an inequality between higher-order moments of a non-commutative multilinear polynomial with different random matrix ensemble inputs, where each variable has small influence and the variables are instantiated independently. This technical result can be interpreted as a noncommutative generalization of one of the two inequalities of the seminal invariance principle of Mossel, O'Donnell and Oleszkiewicz. Our main application is sharp Unique Games hardness for two versions of the noncommutative Grothendieck inequality. This generalizes a result of Raghavendra and Steurer who established hardness of approximation for the commutative Grothendieck inequality. A similar application was proven recently by Bri\"{e}t, Regev and Saket using different techniques.
Court Judgment Decision Support System Based on Medical Text Mining

Medical damage is a common problem faced by hospitals around the world and is widely watched by countries and the World Health Organization. As the number of medical damage dispute lawsuit cases rapidly grows, many countries in the world face the problem how to improve the efficiency of the judicial system under the premise of guaranteeing the quality of the trial. Therefore, in addition to reforming the system, the decision support system will effectively improve judicial decisions. This paper takes medical damage judgment documents in China as example, and proposes a court judgment decision support system (CJ-DSS) based on medical text mining and the automatic classification technology. The system can predict the trail results of the new lawsuit documents according to the previous cases verdict rejected and non-rejected. Combined with the cases, the study in this paper found that combined feature extraction method does improve the performance of three kinds of classifiers Support Value Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN), the degree of improved performance is different from using DF-CHI combined feature extraction method. In addition, integrated learning algorithm also improves the classification performance of the overall
05 07 03 5 v 2 2 2 O ct 2 00 5 Bell states diagonal entanglement witnesses

It has been shown that finding generic Bell states diagonal entanglement witnesses (BDEW) for d 1 ⊗ d 2 ⊗ . ⊗ d n systems exactly reduces to a linear programming if the feasible region be a polygon by itself and approximately obtains via linear programming if the feasible region is not a polygon. Since solving linear programming for generic case is difficult, the multi-qubits, 2 ⊗ N and 3 ⊗ 3 systems for the special case of generic BDEW for some particular choice of their parameters have been considered. In the rest of this paper we obtain the optimal non decomposable entanglement witness for 3 ⊗ 3 system for some particular choice of its parameters. By proving the optimality of the well known reduction map and combining it with the optimal and non-decomposable 3 ⊗ 3 BDEW (named critical entanglement witnesses) the family of optimal and non-decomposable 3 ⊗ 3 BDEW have also been obtained. Using the approximately critical entanglement witnesses, some 3 ⊗ 3 bound entangled states are so detected. So the well known Choi map as a particular case of the positive map in connection with this witness via Jamiolkowski isomorphism has been considered which approximately is obtained via linear programming.
Conversation Robot with the Function of Gaze Recognition

Gaze recognition for conversation robot is realized and its effectiveness is confirmed. In human conversation, in addition to speech information, visual information plays important role. Particularly, gaze direction is a very useful prompt for turn-taking. In the case that the speaker finish his utterance, for example, if he looks at the listener, then he expect the listener to speak. On the other hand, if the speaker does not look at the listener, he tries to keep his turn. Most conventional spoken dialogue systems detect the finish of user's turn only by speech recognition. These systems cannot understand the user tries to keep his turn, and they wrongly begin the utterance and block the user's remaining utterance. In this study, we implement the gaze recognition using the user's image captured by the camera mounted on the eye of the robot and apply the recognition results to decide who should speak next. For gaze recognition, we introduce the sub-image of user's eye region extracted with the active appearance model as the feature. Recognition with subspace method using this feature achieved 70% in recognition rate. Finally, the effectiveness of the gaze recognition is confirmed through the subjective experiment. The experiment is performed by the actual conversation between the conversation robot and the subject.
Learning processes in user training: the case for hermeneutics

While the maturing research literature on training has generated increasingly sophisticated and more comprehensive theoretical models, the actual process through which users learn to use a system remains a relatively neglected area. The extant literature that has paid attention to processes have conceptualized these as structures and examined them through variance studies. In this paper, we address this knowledge gap by advancing hermeneutics as a lens to depict the process through which users come to learn about the system. We explain the hermeneutic process, situate it in a training context and illustrate our conceptualization by interpreting a specific training program at a large organization. Based on our findings, we argue that hermeneutics is an appropriate lens to view training and outline how a training program can be designed according to hermeneutic principles.
The risk of cancer in users of statins.

PURPOSE Several preclinical studies suggested a role for 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (statins) in the treatment of cancer. The objective of this study was to compare the risk of incident cancer between users of statins and users of other cardiovascular medication. METHODS Data were used from the PHARMO database, containing drug dispensing records from community pharmacies and linked hospital discharge records for residents of eight Dutch cities. The study base included all patients with one or more prescriptions for cardiovascular drugs in the period between January 1, 1985 and December 31, 1998. Cases were identified as patients in the study base with a diagnosis of incident cancer and matched with four to six controls on sex, year of birth, geographic region, duration of follow-up, and index date. The analysis was adjusted for diabetes mellitus; prior hospitalizations; comorbidity; and use of diuretics, angiotensin-converting enzyme inhibitors, calcium-channel blockers, nonsteroidal anti-inflammatory drugs, sex hormones, and other lipid-lowering drug therapies. RESULTS In the study base, 3129 patients were identified and matched to 16976 controls. Statin use was associated with a risk reduction of cancer of 20% (adjusted odds ratio [OR], 0.80; 95% CI, 0.66 to 0.96). Our data suggest that statins are protective when used longer than 4 years (adjusted OR, 0.64; 95% CI, 0.44 to 0.93) or when more than 1350 defined daily doses are taken (adjusted OR, 0.60; 95% CI, 0.40 to 0.91). CONCLUSION This observational study suggests that statins may have a protective effect against cancer.
Real-Time Quantitative PCR (QPCR) and Reverse Transcription-QPCR for Detection and Enumeration of Total Yeasts in Wine

ABSTRACT Real-time PCR, or quantitative PCR (QPCR), has been developed to rapidly detect and quantify the total number of yeasts in wine without culturing. Universal yeast primers were designed from the variable D1/D2 domains of the 26S rRNA gene. These primers showed good specificity with all the wine yeasts tested, and they did not amplify the most representative wine species of acetic acid bacteria and lactic acid bacteria. Numerous standard curves were constructed with different strains and species grown in yeast extract-peptone-dextrose medium or incubated in wine. The small standard errors with these replicas proved that the assay is reproducible and highly robust. This technique was validated with artificially contaminated and natural wine samples. We also performed a reverse transcription-QPCR (RT-QPCR) assay from rRNA for total viable yeast quantification. This technique had a low detection limit and was more accurate than QPCR because the dead cells were not quantified. As far as we know, this is the first time that RT-QPCR has been performed to quantify viable yeasts from rRNA. RT-QPCR is a rapid and accurate technique for enumerating yeasts during industrial wine fermentation and controlling the risk of wine spoilage.
A Face Detection Method Used for Color Images

To the problem of face detection in color images, a novel method is proposed based on skin color segmentation and geometry features. Firstly, some common color models are analyzed, and a large amount of skin images are used to establish an YCbCr color model for region segmentation. Then, the morphological processing is executed on the binary image, and the facial regions filtering is conducted by adopting some geometry constraints such as Euler number, the ratio of width and height, centroid. Finally, the face region is located and labeled with a rectangle. Experimental results showed that the proposed method can equilibrate the unbalance between good accuracy and real-time performance to some extent, and is effective in face location.
A Unified Framework for Object Retrieval and Mining

This paper describes a unified framework for object mining system for videos, which combines shot segmentation, clustering, retrieval and object mining using a single set of detected local invariant regions. The local invariant regions are tracked throughout a shot and stable tracks are extracted. The conventional key frame method is replaced with these stable tracks of local regions to characterize different shots. A grouping technique is introduced to combine the stable tracks into meaningful object clusters. These clusters are used to mine similar objects. Compared to other object mining systems, our approach mines more instances of similar objects in different shots. The proposed framework is applied to full length feature films and the results are compared with state of the art methods.
An in silico approach for pre-surgical evaluation of an epileptic cortex

Clinical evidence indicates that a third of patients with epilepsy are refractory to anti-epileptic drug treatment. For some of these patients better seizure control can be achieved by surgical treatment in which the seizure focus is localised and resected while avoiding injury to crucial cortical tissues. In this paper, non-seizure (interictal) epoch of electrographic recording was used to calculate the functional synchrony between different cortical regions. This synchrony measure was then used as the connectivity parameter in a computational model of transitions to a seizure like state. The seizure focus was localised using this model and the surgical intervention procedure was simulated. It was shown that the in silico removal of a subset of seizure focus can decrease the likelihood of a seizure in the model. The in silico results were also compared with the clinical outcomes and a convincing agreement was shown for five out of six patients; sixth being a counter-example. These methods may aid in the identification of the seizure onset zone using the interictal electrographic data. Moreover, it may facilitate neurosurgeons to investigate alternative cortical tissues to operate on if the seizure focus cannot be operated.
Toa Source Node Self-positioning with Unknown Clock Skew in Wireless Sensor Networks

This paper investigates time-of-arrival (TOA) source node self-positioning with unknown clock skews in wireless sensor networks. For the source-to-anchor direction, source node clock skew does not affect the localization performance. When synchronized anchor nodes simultaneously transmit signals to a source node, the source node clock skew will degrade the localization performance. A semidefinite programming (SDP) algorithm that jointly estimates the source position and clock skew is proposed for the latter case. The proposed algorithm is better than the two kinds of existing schemes, namely, asynchronous TOA localization and TDOA localization. We also tune the algorithm to the case of anchor nodes position uncertainties. Simulation results validate the performance of the proposed algorithm.
Competition between translation initiation factor eIF5 and its mimic protein 5MP determines non-AUG initiation rate genome-wide

Abstract In the human genome, translation initiation from non-AUG codons plays an important role in various gene regulation programs. However, mechanisms regulating the non-AUG initiation rate remain poorly understood. Here, we show that the non-AUG initiation rate is nearly consistent under a fixed nucleotide context in various human and insect cells. Yet, it ranges from <1% to nearly 100% compared to AUG translation, depending on surrounding sequences, including Kozak, and possibly additional nucleotide contexts. Mechanistically, this range of non-AUG initiation is controlled in part, by the eIF5-mimic protein (5MP). 5MP represses non-AUG translation by competing with eIF5 for the Met-tRNAi-binding factor eIF2. Consistently, eIF5 increases, whereas 5MP decreases translation of NAT1/EIF4G2/DAP5, whose sole start codon is GUG. By modulating eIF5 and 5MP1 expression in combination with ribosome profiling we identified a handful of previously unknown non-AUG initiation sites, some of which serve as the exclusive start codons. If the initiation rate for these codons is low, then an AUG-initiated downstream ORF prevents the generation of shorter, AUG-initiated isoforms. We propose that the homeostasis of the non-AUG translatome is maintained through balanced expression of eIF5 and 5MP.
Birefringence of lead titanate (PbTiO3)

Since lead titanate was first grown in single-crystal form in the 1930s, several independent reports of its linear birefringence have been published. Whilst there is overall qualitative agreement on its behaviour with temperature, the absolute values show some degree of variation. At 298 K they range from −0.009 to −0.013, a relative spread of 40%, whilst at 673 K the discrepancy is even greater. This paper reports unexpected results obtained from independent measurements of a selection of crystals that have been grown recently, in which the birefringence is some four times greater than hitherto believed. This has important consequences for the use of birefringence measurement as a means of studying phase transitions in crystals. Intrinsic values of birefringence for lead titanate are measured for the first time.
Accidental and Nonaccidental Femur Fractures in Children

A retrospective review of 207 patients younger than 6 years of age who sustained nonpathologic diaphyseal femur fractures was done, which emphasized the characteristics of accidental versus nonaccidental injury. There were 214 fractures in 123 boys and 83 girls (the gender of one patient was unknown). The average age of the patients was 2.73 years. Mechanisms of injury were pedestrian struck by a car (62 patients), falls (92 patients), and motor vehicle accidents (10 patients). Nineteen patients did not have a history of trauma. Seventy-six cases were investigated for child abuse. The results of 13 investigations were positive. Overall, the morphologic features of the fractures were transverse (38%), spiral (27%), and oblique (17%). In the investigated group, 27% of the fractures were transverse, 39% were spiral, and 15% were oblique. In those cases with positive results of the investigation, 36% of the fractures were transverse, 36% were spiral, and 7% were oblique. Although transverse fractures are most common in accidental and nonaccidental injuries, many practitioners think spiral fractures are pathognomonic of abuse. The current data show that although spiral fractures were less common than transverse fractures overall, and no more common in the cohort of patients in whom the results of the child abuse investigations were positive, they were overrepresented in the cohort that was investigated. This suggests that spiral fractures are viewed as particularly suspicious, which may lead to missed cases of nonaccidental injury in children with transverse fractures.
GasCL: A vertex-centric graph model for GPUs

There are increasing research efforts of using GPUs for graph processing. Most prior work on accelerating GPGPU graph algorithms has been focused on algorithm and device-specific optimizations. There is little research on studying high-level programming models and associate run-time systems for graph processing on GPUs, which will be useful to solve diverse real-world problems flexibly. This paper presents a preliminary implementation of a graph framework, GasCL, supporting the well-known “think-like-a-vertex” programming model. The system is built on top of OpenCL and portable across diverse accelerators. We describe our design and use two applications as case studies. The initial performance result shows an average of 2.5× speedup on a GPU compared with a CPU.
Learning Task-Specific Object Recognition and Scene Understanding

In this paper, we present an approach to object recognition and scene understanding which integrates low-level image processing and high-level knowledge-based components. A novel machine learning system is presented which is used to acquire knowledge relating to a specific task. Learned feedback from high-level to low-level processes is introduced as a means of achieving robust task-specific segmentation. The system has been implemented and trained on a number of scenarios with differing tasks from which results are presented and discussed.
Adaptive Redundant Data Allocation Using Unused Blocks for Improving Access Efficiency in Dependable Storage Systems

This paper has presented a data allocation method with erasure correction coding to design dependable storage system. This adaptive data allocation can reduce the number of access to unavailable storage elements, moreover it also improve the reliability of the system. We proposed variable information word length method which can reduce the number of blocking accesses, and two ideas are presented to reduce the allocation map size and update cost.
Optimal Mutation Rates in Genetic Search

The optimization of a single bit string by means of iterated mutation and selection of the best a Genetic Algorithm is dis cussed with respect to three simple tness functions The counting ones problem a standard binary encoded integer and a Gray coded integer optimization problem A mu tation rate schedule that is optimal with re spect to the success probability of mutation is presented for each of the objective functions and it turns out that the standard binary code can hamper the search process even in case of unimodal objective functions While normally a mutation rate of l where l de notes the bit string length is recommend able our results indicate that a variation of the mutation rate is useful in cases where the tness function is a multimodal pseudo boolean function where multimodality may be caused by the objective function as well as the encoding mechanism