Miracle-dz/UniFAR-FTUs huggingface dataset:
Vortioxetine Differentially Modulates MK-801-Induced Changes in Visual Signal Detection Task Performance and Locomotor Activity

Attention impairment is a common feature of Major Depressive Disorder (MDD), and MDD-associated cognitive dysfunction may play an important role in determining functional status among this patient population. Vortioxetine is a multimodal antidepressant that may improve some aspects of cognitive function in MDD patients, and may indirectly increase glutamate neurotransmission in brain regions classically associated with attention function. Previous non-clinical research suggests that vortioxetine has limited effects on attention. This laboratory previously found that vortioxetine did not improve attention function in animals impaired by acute scopolamine administration, using the visual signal detection task (VSDT). However, vortioxetine has limited effects on acetylcholinergic neurotransmission, and thus it is possible that attention impaired by other mechanisms would be attenuated by vortioxetine. This study sought to investigate whether acute vortioxetine administration can attenuate VSDT impairments and hyperlocomotion induced by the non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist MK-801. We found that acute vortioxetine administration had no effect on VSDT performance on its own, but potentiated MK-801-induced VSDT impairments. Furthermore, vortioxetine had no effect on locomotor activity on its own, and did not alter MK-801-induced hyperlocomotion. We further investigated whether vortioxetine’s effect on MK-801 could be driven by a kinetic interaction, but found that plasma and brain exposure for vortioxetine and MK-801 were similar whether administered alone or in combination. Thus, it appears that vortioxetine selectively potentiates MK-801-induced impairments in attention without altering its effects on locomotion, and further that this interaction must be pharmacodynamic in nature. A theoretical mechanism for this interaction is discussed.
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals

We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study the problem of training non-convex models subject to these rate constraints (or any non-convex and non-differentiable constraints). In the non-convex setting, the standard approach of Lagrange multipliers may fail. Furthermore, if the constraints are non-differentiable, then one cannot optimize the Lagrangian with gradient-based methods. To solve these issues, we introduce the proxy-Lagrangian formulation. This new formulation leads to an algorithm that produces a stochastic classifier by playing a two-player non-zero-sum game solving for what we call a semi-coarse correlated equilibrium, which in turn corresponds to an approximately optimal and feasible solution to the constrained optimization problem. We then give a procedure which shrinks the randomized solution down to one that is a mixture of at most $m+1$ deterministic solutions, given $m$ constraints. This culminates in algorithms that can solve non-convex constrained optimization problems with possibly non-differentiable and non-convex constraints with theoretical guarantees. We provide extensive experimental results enforcing a wide range of policy goals including different fairness metrics, and other goals on accuracy, coverage, recall, and churn.
Cardiac anomaly detection based on time and frequency domain features using tree-based classifiers

Objective: Recent advantages in mHealth-enabled ECG recorders boosted the demand for algorithms, which are able to automatically detect cardiac anomalies with high accuracy. Approach: We present a combined method of classical signal analysis and machine learning which has been developed during the Computing in Cardiology Challenge (CinC) 2017. Almost 400 hand-crafted features have been developed to reflect the complex physiology of cardiac arrhythmias and their appearance in single-channel ECG recordings. For the scope of this article, we performed several experiments on the publicly available challenge dataset to improve the classification accuracy. We compared the performance of two tree-based algorithms—gradient boosted trees and random forests—using different parameters for learning. We assessed the influence of five different sets of training annotations on the classifiers performance. Further, we present a new web-based ECG viewer to review and correct the training labels of a signal data set. Moreover, we analysed the feature importance and evaluated the model performance when using only a subset of the features. The primary data source used in the analysis was the dataset of the CinC 2017, consisting of 8528 signals from four classes. Our best results were achieved using a gradient boosted tree model which worked significantly better than random forests. Main results: Official results of the challenge follow-up phase provided by the Challenge organizers on the full hidden test set are 90.8% (Normal), 84.1% (AF), 74.5% (Other), resulting in a mean F1-score of 83.2%, which was only 1.6% behind the challenge winner and 0.2% ahead of the next-best algorithm. Official results were rounded to two decimal places which lead to the equal-second best F1F-score of 83% with five others. Significance: The algorithm achieved the second-best score among 80 algorithms of the Challenge follow-up phase equal with five others.
A Cloud-Based Parallel Space-Saving Algorithm for Big Networking Data

As the network continues to evolve, completely analyzing the traffic requires immeasurable resources. In situations of processing enormous streaming data, the most significant k items (Top-k) are more interesting, and some streaming algorithms are deployed due to relatively limited memory and also limited processing time per item. Space-saving is such one of the most popular algorithms for computation of frequent and Top-k elements in data streams. In this paper, this algorithm is implemented in the cloud for analyzing big networking data, and an empirical formula of the counter number is derived for efficiently maintaining Top-k items. Meanwhile, easily understandable proof manner is presented to prove the merging ability of Space-saving algorithm, and some experiments are conducted to affirm the effectiveness of the algorithm.
Dislocation of large-diameter head metal-on-metal total hip arthroplasty and hip resurfacing arthroplasty

Introduction: Dislocation of large-diameter head (LDH) metal-on-metal (MoM) total hip arthroplasty (THA) or hip resurfacing arthroplasty (HRA) is a rare complication. This study aimed to determine the incidence and risk factors for dislocation of LDH MoM THAs and HRAs. Methods: This retrospective analysis considered 4038 cementless LDH MoM THAs and HRAs, 3207 THAs in 2912 patients and 831 HRAs in 757 patients. The end of follow-up was revision due to dislocation. Incidence of dislocation was evaluated from this study population of 4038, and study groups were formed. The study was designed as a case-control study, and a threefold stratified randomised control group was formed. Demographic data were collected and radiological analyses were performed in the study groups. Results: There were 26/3207 (0.8%) early dislocations in the THA group, and 6/831 (0.7%) in the HRA group (p = 0.9). Most LDH THA dislocations occurred in a group with head size ⩽ 38 mm (18/26) (p < 0.001). In dislocated hips, there were more dysplastic acetabula and post-traumatic hips than in the control group (p = 0.036). In the dislocation group, the mean acetabulum component anteversion angle was 19.6° (standard deviation [SD] 13.4°) and in the control group it was 23.2° (SD 10.4°) (p = 0.006); 7/32 (21.8%) of dislocated THAs needed revision surgery, and mean time to revision from the index surgery was 1.2 (SD 2.6) years. Discussion: Dislocations occurred more often in THAs of head size ⩽ 38 mm and with a smaller anteversion angle of the acetabulum component. Hip dysplasia and post-traumatic osteoarthritis were more common in patients with dislocation.
Closing-in Behavior and Parietal Lobe Deficits: Three Single Cases Exhibiting Different Manifestations of the Same Behavior

Closing-in behavior (CIB) is observed in copying tasks (graphic or gestural) when the copy is performed near or on the top of the model. This symptom has been classically considered to be a manifestation of constructional apraxia and is often associated with a visuospatial impairment. More recent work emphasizes the attentional and/or executive nature of the behavior and its association with frontal lobe dysfunction. We describe three patients in whom CIB was associated with posterior parietal deficits of different etiologies (stroke in Patient 1 and dementia in Patients 2 and 3). In copying figures, Patient 1 produced the shape with high accuracy but the rendering overlapped the model, while for Patients 2 and 3 the copies were distorted but overlapping or in close proximity to the target. In gesture imitation, Patient 2 performed the gestures toward the examiner’s space, while Patient 1 showed a peculiar form of CIB: when he was asked to place the ipsilesional arm in a position that mirrored the contralesional hand, Patient 1 moved his hand toward his contralesional hand. Patient 3 did not present gestural CIB. While CIB in Patient 1 was associated with selective deficits in executive functions and attention, additional visuospatial deficits were observed in Patients 2 and 3. The latter two patients showed a general visuoconstructional deficit. These case studies support a primary attentional account of CIB but also suggest that visuoconstructional impairments may contribute to the emergence of CIB, in some subjects. This evidence argues for different types of CIB with different cognitive and neural underpinnings. Furthermore, the data support the hypothesis of a differential involvement of fronto-parietal network in CIB.
NewGraph: Balanced Large-Scale Graph Processing on FPGAs with Low Preprocessing Overheads

Large-scale graph processing has been widely required in various domains, including social network analysis, neural network modeling, database computing, etc. Performance of large-scale graph suffers from random and unpredictable data access pattern, which leads to drastic bandwidth degradation on caches, DRAMs, and disks. The support for high bandwidth random access makes SRAMs the promising solution for graph processing. Many FPGA based large-scale graph processing systems have been proposed in previous works and taken advantage of the SRAM resources.
Statistics of SINR in 3-D Environment With Correlated Log-Normal Shadowing

Based on the stochastic geometry theory, the analysis for the statistics of signal-to-noise-and-interference ratio (SINR) is conducted in this letter, where the links between the base stations and user equipments are impaired by correlated log-normal shadowing in 3-D environment. The closed-form expressions of the statistics of signal-to-interference ratio, signal-to-noise ratio, and SINR are presented. Then, the distribution of SINR−1 is approximated by the log-Pearson type III distribution based on the moments of SINR−1. Numerical results indicate that the cell coverage increases with the increase of the correlation coefficient between communication and interference links.
Transform Domain Processing-Based Cooperative Spectrum Sharing Protocol With Adaptive Relaying

In this paper, we propose a two-time slot cooperative spectrum sharing protocol employing transform domain processing technology, in which primary users and secondary users adopt sinusoidal signals and chirp signals, respectively. Moreover, an adaptive relaying mechanism is used in our protocol to improve the flexibility of cooperation. Then, a Newton iteration method based sub-optimal power allocation algorithm is presented to reduce the complexity of exhaustive method while guaranteeing the outage probability constraint of the primary system. After that, a secondary user selection algorithm which focuses on the statistical channel conditions is proposed to further improve the performance of spectrum sharing. Simulation results show that compared with the existing protocol, outage probability, achievable data rate, and average energy efficiency are significantly improved for the secondary system in our proposed protocol. Moreover, lower outage probability, higher achievable data rate, and average energy efficiency of the secondary system can be realized with an increase in the number of secondary users.
The Promotion of a Bright Future and the Prevention of a Dark Future: Time Anchored Incitements in News Articles and Facebook’s Status Updates

Background: Research suggests that humans have the tendency to increase the valence of events when these are imagined to happen in the future, but to decrease the valence when the same events are imagined to happen in the past. This line of research, however, has mostly been conducted by asking participants to value imagined, yet probable, events. Our aim was to re-examine this time-valence asymmetry using real-life data: a Reuter’s news and a Facebook status updates corpus. Method: We organized the Reuter news (120,000,000 words) and the Facebook status updates data (41,056,346 words) into contexts grouped in chronological order (i.e., past, present, and future) using verbs and years as time markers. These contexts were used to estimate the valence of each article and status update, respectively, in relation to the time markers using natural language processing tools (i.e., the Latent Semantic Analysis algorithm). Results: Our results using verbs, in both text corpus, showed that valence for the future was significantly higher compared to the past (future > past). Similarly, in the Reuter year condition, valence increased approximately linear from 1994 to 1999 for texts written 1996–1997. In the Facebook year condition, the valence of the future was also significantly higher than past valence. Conclusion: Generally, the analyses of the Reuters data indicated that the past is devaluated relative to both the present and the future, while the analyses of the Facebook data indicated that both the past and the present are devaluated against the future. On this basis, we suggest that people strive to communicate the promotion of a bright future and the prevention of a dark future, which in turn leads to a temporal-valence asymmetrical phenomenon (valence = past < present < future). “I have a dream that one day this nation will rise up and live out the true meaning of its creed: “We hold these truths to be self-evident, that all men are created equal. ” I have a dream that one day on the red hills of Georgia, the sons of former slaves and the sons of former slave owners will be able to sit down together at the table of brotherhood. I have a dream that one day even the state of Mississippi, a state sweltering with the heat of injustice, sweltering with the heat of oppression, will be transformed into an oasis of freedom and justice. I have a dream that my four little children will one day live in a nation where they will not be judged by the color of their skin but by the content of their character. I have a dream today! ” Martin Luther King, Jr., 28th of August 1963, at the Lincoln Memorial, Washington, DC, United States
Diagnostic value of imaging examinations in patients with primary hepatocellular carcinoma

Primary hepatocellular carcinoma (PHC) includes hepatocellular carcinoma, intrahepatic cholangiocarcinoma and other pathological types and is characterized by rapid progression. Most of the clinical diagnoses are made at late stage or when distant metastasis occurs, increasing the difficulty of treatment and resulting in a poor prognosis. Therefore, the early diagnosis of PHC plays an important role in timely treatment and the improvement of prognosis. The gold standard for the diagnosis of primary liver cancer is liver biopsy, but it has limitations as an invasive examination. Presently, imaging has become the first choice for the diagnosis of liver cancer. We here summarize the new methods and techniques of imaging in diagnosis and evaluation of primary liver cancer in recent years, including ultrasonography, computed tomography perfusion imaging, diffusion-weighted imaging technology-voxel incoherent motion, diffusion tensor imaging, iterative decomposition of water and fat with echo asymmetry and least squares estimation-iron quantification, dynamic enhanced magnetic resonance imaging and hepatocyte-specific contrast medium imaging. Imaging diagnosis can not only evaluate the degree of differentiation, blood supply and perfusion, and invasiveness of the lesion, but also predict the prognosis, evaluate liver function, and provide references for clinical diagnosis and treatment.
The new social work

ABSTRACT Social work practice is an industry based mostly on therapist intuition and gut feelings. There are virtually no real-time, reliable data showing the performance of any patient outcomes or on therapists providing treatment services. In this paper a new system is recommended, performance-based practice. In the future, the New Social Work proposed will not operate as a system in which patients enter community facilities to sit in waiting rooms and interact with a randomly assigned therapist for one hour a few times weekly. Social service building will transform into technology centers where therapists use tools to measure and intervene with patients in real-time. The New Social Work will finally understand that people do not recover or learn how to recover in therapist’s offices—but in their own communities—with assistance from high-performance professional therapists interacting in the moment.
Semantic-Aware Online Workload Characterization and Consolidation

Analyzing behavioral patterns of workloads is critical to understanding Cloud computing environments. How-ever, until recently, allocation of resources to Virtual Machines running the workloads were static, based on user specifications. Cloud providers performed resource consolidation mostly by packing low priority, best effort workloads with regular workloads with strict QoS requirements. This paper is building on recent efforts towards dynamic, on-line resource consolidation based on workload recognition and resource usage prediction. We introduce a new methodology for online VM consolidation that is based on a combination of resource usage data and program features for accurate resource prediction of the running workloads. We show a 15% improvement in prediction accuracy versus a baseline method using resource usage alone and an average 30% saving in resources after online consolidation with around 25% less resource capacity violations using our method.
Eccentric fixation patterns, clinical findings, and reading speed in patients with bilateral toxoplasmic macular retinochoroiditis.

PURPOSE To study visual acuity, refractive errors, eccentric fixation, and reading performance in patients with toxoplasmic macular retinochoroiditis. METHODS Twenty-three participants with bilateral toxoplasmic macular retinochoroiditis and 4 with toxoplasmic macular retinocho-roiditis in their unique eye were evaluated. Participants reported their eye dominance, confirmed by the Portus and Miles test. Best corrected visual acuity, spherical equivalent refraction, magnification need, and reading speed were measured. Microperimetry (MAIA, Centervue - Padova, Italy) recorded the preferred retinal locus and fixation stability by means of the bivariate contour ellipse area. Fourteen eyes from 14 normally sighted subjects served as controls. RESULTS Mean ± SD best corrected visual acuity was better in the dominant eye than in the nondominant eye: 0.9 ± 0.2 (logMAR 0.5 to 1.4) vs. 1.2 ± 0.3 (logMAR 0.6 to 1.7) (p<0.0001, paired t-test). Spherical equivalent myopia of -4.00 or higher was present in 42% of the eyes. Microperimetry was performed in 42 eyes. Eccentric fixation was observed in all examined eyes. In 14 eyes (33%), the preferred retinal locus was placed (in the retina) superior temporal to the macular lesion, in 10 (24%) superior nasal, in 6 (14%) inferior temporal, and in 12 (28%) inferior nasal. There was no significant difference in the distribution of the preferred retinal locus position between dominant and nondominant eyes (p=0.85, Pearson test). There was no correlation between reading speed and the distance between the preferred retinal locus and the estimated original foveal position (r=-0.09; p=0.73), the bivariate contour ellipse area (r=-0.19; p=0.44), or best corrected visual acuity (r=0.024; p=0.92). CONCLUSIONS Myopia is more prevalent in patients with toxoplasmic macular retinochoroiditis. Reading speed is not dependent on preferred retinal locus position, stability, or visual acuity. Nevertheless, documentation of fixation provides new data on the impact of visual impairment in these patients and may be useful for rehabilitation efforts.
Protecting Smartphones Based on Usage Patterns

Due to the rapid developments of cloud technologies, mobile devices, especially smartphones, have become much more powerful, bringing people great convenience. People can do many things on their smartphones: checking email, updating Facebook/Snapchat/Instagram, or shopping via different apps. With all these applications, people tend to access their smartphones frequently and instantly, i.e., they want to use the phone right away. On the other hand, more and more sensitive information is now stored or accessible on smartphones such as Visa Signature or Paypal apps. Smartphones need to be protected more than ever before. How to protect smartphones while still provide convenience becomes a challenging problem. In this paper, we describe an approach aiming to solve this problem. The approach provides an authentication framework to protect smartphones. This authentication framework requires stronger authentication to accesses that are classified as suspicious, whereas still offers quick access for those authentic.
Towards Hybrid Model Persistence

Change-based persistence has the potential to support faster and more accurate model comparison, merging, as well as a range of analytics activities. However, reconstructing the state of a model by replaying its editing history every time the model needs to be queried or modified can get increasingly expensive as the model grows in size. In this work, we integrate change-based and state-based persistence mechanisms in a hybrid model persistence approach that delivers the best of both worlds. In this paper, we present the design of our hybrid model persistence approach and report on its impact on time and memory footprint for model loading, saving, and storage space usage.