Miracle-dz/UniFAR-FTUs huggingface dataset:
Mosaic: Exploiting the spatial locality of process variation to reduce refresh energy in on-chip eDRAM modules

EDRAM cells require periodic refresh, which ends up consuming substantial energy for large last-level caches. In practice, it is well known that different eDRAM cells can exhibit very different charge-retention properties. Unfortunately, current systems pessimistically assume worst-case retention times, and end up refreshing all the cells at a conservatively-high rate. In this paper, we propose an alternative approach. We use known facts about the factors that determine the retention properties of cells to build a new model of eDRAM retention times. The model is called Mosaic. The model shows that the retention times of cells in large eDRAM modules exhibit spatial correlation. Therefore, we logically divide the eDRAM module into regions or tiles, profile the retention properties of each tile, and program their refresh requirements in small counters in the cache controller. With this architecture, also called Mosaic, we refresh each tile at a different rate. The result is a 20x reduction in the number of refreshes in large eDRAM modules - practically eliminating refresh as a source of energy consumption.
Dressing the Split-Thickness Skin Graft Donor Site: A Randomized Clinical Trial

OBJECTIVE:The primary objective of this study was to compare Aquacel (ConvaTec, Skillman, New Jersey), Allevyn (Smith & Nephew, St Petersburg, Florida), and Mediskin I (Mölnlycke, Health Care AB, Gothenburg, Sweden) in the treatment of split-thickness skin graft donor sites. DESIGN:This study was performed as a prospective randomized, 3-arm, clinical study. SETTING:A clinical study performed at a hand and plastic surgery department with burn unit. PARTICIPANTS:The study included 67 adults with a total of 73 donor sites, which were on the thigh, not reharvested, and ranged between 30- and 400-cm2 area. INTERVENTIONS:Subjects were randomly assigned to treatment with Aquacel, Allevyn, or Mediskin I. MAIN OUTCOME MEASURES:The donor site was assessed on postoperative days 3, 14, and 21 for healing, infection, pain, impact on everyday life, ease of use, and cost. MAIN RESULTS:The obtained results demonstrate significantly faster re-epithelialization for patients treated with Aquacel or Mediskin I compared with Allevyn. Regarding infections, there were no significant differences between the groups. Patients wearing Aquacel experienced significantly less pain changing the dressing and less impact on everyday life than the patients wearing Allevyn. Aquacel was shown to be significantly easier for the caregiver to use than Allevyn and Mediskin I. There is a significant difference in cost of treatment between the dressings, whereas Mediskin I is the most expensive. CONCLUSION:The authors’ results support the use of Aquacel in the treatment of split-thickness skin graft donor sites. Aquacel has a low cost per unit, is user friendly, gives short healing time, and minimizes patient discomfort.
Intragastric pH with oral vs intravenous bolus plus infusion proton-pump inhibitor therapy in patients with bleeding ulcers.

BACKGROUND & AIMS Intravenous bolus plus infusion proton pump inhibitor (PPI) therapy is recommended for patients with bleeding ulcers and higher risk stigmata. If frequent oral dosing of PPIs provided similar antisecretory effect, this might be preferred based on ease and cost. METHODS Patients presenting with overt bleeding due to ulcers had intragastric pH probes placed after endoscopy and baseline pH recorded. They were randomly assigned to intravenous lansoprazole (90-mg bolus followed by 9-mg/h infusion) or oral lansoprazole (120-mg bolus followed by 30 mg every 3 hours). pH was recorded for 24 hours. RESULTS Intragastric pH was > 6 for 67.8% of the study period with intravenous PPI (n = 32) and 64.8% with oral PPI (n = 34): difference, 3.0%; 95% confidence interval (CI): -9.2% to 15.2%. Intragastric pH was > 6 for > 60% of the study period in 22 (68.8%) patients receiving intravenous and 22 (64.7%) patients receiving oral PPI: difference, 4.0%; 95% CI: -18.7% to 26.8%. At 1 hour, mean pHs for intravenous vs oral were 5.3 +/- 0.4 vs 3.3 +/- 0.4, respectively (difference, 2.0; 95% CI: 0.8-3.1; P = .001). At > or = 1.5 hours, 95% CIs of the differences for all hourly mean pHs included zero. Mean pH rose above 6 after 2-3 hours of intravenous PPI and 3-4 hours of oral PPI. CONCLUSIONS Frequent oral PPI may be able to replace the currently recommended intravenous bolus plus infusion PPI therapy in patients with bleeding ulcers, although the possibility that intravenous PPIs are superior cannot be definitively excluded given our relatively wide confidence intervals. Intravenous PPI provides more rapid increase in pH, reaching mean pH of 6 approximately 1 hour sooner than oral PPI.
Information-guided randomization for wireless physical layer secure transmission

Security capacity improvement is always the implementation goal of physical layer security, which can be achieved by deliberate randomization based on differentia and uniqueness of wireless channels through signal processing method. An information-guided randomization secure transmission scheme is proposed combined with a spatial modulation system in which information can be carried by receiving antennas index but actually by channel differentia. Randomization is generated along with channel modulation procedure realized by corresponding preprocessing to transmitting signals. We provide three approaches for designing preprocessing weights under different constraint conditions. The former two can only be used when the number of receiving antennas is less than transmitting and have a tradeoff with varying degrees between receiving performance and security. The third one then relaxes the limits via generalized spatial modulation and can be used for arbitrary antennas configuration but with a receiving performance compromise. Lastly, results of performance analysis and numerical evaluation show that the proposed secure scheme is effective to prohibit the eavesdropper from intercepting information.
Therapieeffekte und Responseraten bei unipolar depressiven Patienten einer verhaltenstherapeutischen Hochschulambulanz

Zusammenfassung. Theoretischer Hintergrund: Obwohl sich die Behandlung depressiver Storungen in randomisiert-kontrollierten Studien (RCTs) als sehr effektiv erwiesen hat, ist wenig uber den Transfer solcher Studienergebnisse auf naturalistische Behandlungssettings bekannt. Fragestellung: Wie hoch sind Therapieeffekte und Responseraten bei depressiven Patienten im Routinesetting einer verhaltenstherapeutischen Hochschulambulanz? Methode: 229 Patienten mit Major Depression oder dysthymer Storung wurden bei Therapieanmeldung, -beginn und -ende sowie katamnestisch untersucht. Ergebnisse: Pra-Post-Analysen zeigten einen Ruckgang der Symptomatik mit Effektstarken von 1,03 (Intention-to-Treat) und 1,22 (Completer), die auch katamnestisch stabil blieben, im Vergleich zu publizierten RCTs aber geringer ausfielen. Responseraten lagen bei 40–60%. In der Wartebedingung zeigte sich keine Veranderung (Between-Group-Effektstarke: 1,14). Schlussfolgerungen: Verhaltenstherapie bei Depressionen erweist sich auch in natural.
Genetic Improvement for Code Obfuscation

Genetic improvement (GI) is a relatively new area of software engineering and thus the extent of its applicability is yet to be explored. Although a growing interest in GI in recent years started with the work on automatic bug fixing, the area flourished when results on optimisation of non-functional software properties, such as efficiency and energy consumption, were published. Further success of GI in transplanting functionality from one program to another leads to a question: what other software engineering areas can benefit from the use of genetic improvement techniques? We propose to utilise GI for code obfuscation.
University Life of Non-Traditional Students: Engagement Styles and Impact on Attrition

In the light of the literature on student engagement, social integration, and adult learning, an empirical study (n = 228 students) explored the relationship between first-year academic experience and non-traditional students' retention. Results show that engagement, social integration, and meaningfulness of learning experience have different effects on non-traditional and traditional students' retention.
Energy efficiency analysis of secondary networks in cognitive radio systems

In this paper, we evaluate a novel contention-aware channel selection algorithm that focuses on energy efficiency improvement of a secondary network (SN) in a scenario where other non-cooperating SNs are also using the primary resources. We present a detailed energy efficiency analysis and we study how the time between two consecutive sensing periods affects the energy efficiency. Our analysis proves that a categorization of the idle channels based on their contention level and the selection of the less contended ones can result in up to 70% gain in energy efficiency. The model is further evaluated through simulations.
Nonlinear variable frequency control of high power switched-capacitor converter

Switched-capacitor (SC) converters are a type of variable structure systems. In the past, most of the SC converters were initially introduced for the low power applications and monolithic integration. Recently, SC converters have been used in high power area with the open-loop control and some other simple linear voltage feedback controls. However, the simplicity of such approaches sacrifice performance with the slow dynamic response, narrow operation region and large voltage ripple. Some new nonlinear control methods have been proposed, however, such control methods are only useful for applications involving low power SC converter. This paper proposed a new nonlinear constant on time variable frequency control (COTVF) based on the capacitor ampere-second balance principle. The proposed variable frequency control method successfully addresses the regulation issue in high power SC converters. The control concept is straightforward yet with excellent performance. In combination with the high power 2X two-switch boosting switched-capacitor (TBSC) converter, simulation results demonstrate that the proposed control method highly improve the dynamic response speed with good line and load regulation in wide operation range.
Predicting the Plant Root-Associated Ecological Niche of 21 Pseudomonas Species Using Machine Learning and Metabolic Modeling

Plants rarely occur in isolated systems. Bacteria can inhabit either the endosphere, the region inside the plant root, or the rhizosphere, the soil region just outside the plant root. Our goal is to understand if using genomic data and media dependent metabolic model information is better for training machine learning of predicting bacterial ecological niche than media independent models or pure genome based species trees. We considered three machine learning techniques: support vector machine, non-negative matrix factorization, and artificial neural networks. In all three machine-learning approaches, the media-based metabolic models and flux balance analyses were more effective at predicting bacterial niche than the genome or PRMT models. Support Vector Machine trained on a minimal media base with Mannose, Proline and Valine was most predictive of all models and media types with an f-score of 0.8 for rhizosphere and 0.97 for endosphere. Thus we can conclude that media-based metabolic modeling provides a holistic view of the metabolome, allowing machine learning algorithms to highlight the differences between and categorize endosphere and rhizosphere bacteria. There was no single media type that best highlighted differences between endosphere and rhizosphere bacteria metabolism and therefore no single enzyme, reaction, or compound that defined whether a bacteria's origin was of the endosphere or rhizosphere.
On Agnostic Learning of Parities, Monomials, and Halfspaces

We study the learnability of several fundamental concept classes in the agnostic learning framework of [D. Haussler, Inform. and Comput. , 100 (1992), pp. 78-150] and [M. Kearns, R. Schapire, and L. Sellie, Machine Learning, 17 (1994), pp. 115-141]. We show that under the uniform distribution, agnostically learning parities reduce to learning parities with random classification noise, commonly referred to as the noisy parity problem. Together with the parity learning algorithm of [A. Blum, A. Kalai, and H. Wasserman, J. ACM, 50 (2003), pp. 506-519], this gives the first nontrivial algorithm for agnostic learning of parities. We use similar techniques to reduce learning of two other fundamental concept classes under the uniform distribution to learning of noisy parities. Namely, we show that learning of disjunctive normal form (DNF) expressions reduces to learning noisy parities of just logarithmic number of variables, and learning of $k$-juntas reduces to learning noisy parities of $k$ variables. We give essentially optimal hardness results for agnostic learning of monomials over $\{0,1\}^n$ and halfspaces over $\mathbb{Q}^n$. We show that for any constant $\epsilon$ finding a monomial (halfspace) that agrees with an unknown function on $1/2+\epsilon$ fraction of the examples is NP-hard even when there exists a monomial (halfspace) that agrees with the unknown function on $1-\epsilon$ fraction of the examples. This resolves an open question due to Blum and significantly improves on a number of previous hardness results for these problems. We extend these results to $\epsilon=2^{-\log^{1-\lambda}n}$ ($\epsilon=2^{-\sqrt{\log n}}$ in the case of halfspaces) for any constant $\lambda>0$ under stronger complexity assumptions.
Structural and functional basis for starch binding in the SnRK1 subunits AKINβ2 and AKINβγ

Specialized carbohydrate-binding domains, the Starch-Binding Domain (SBD) and the Glycogen Binding Domain (GBD), are motifs of approximately 100 amino acids directly or indirectly associated with starch or glycogen metabolism. Members of the regulatory β subunit of the heterotrimeric complex AMPK/SNF1/SnRK1 contain an SBD or GBD. In Arabidopsis thaliana, the β regulatory subunit AKINβ2 and a γ-type subunit, AKINβγ, also have an SBD. In this work, we compared the SBD of AKINβ2 and AKINβγ with the GBD present in rat AMPKβ1 and demonstrated that they conserved the same overall topology. The majority of the amino acids identified in the protein-carbohydrate interactions in the rat AMPKβ1 are conserved in the two plant proteins. In AKINβγ, there is an insertion of three amino acids that creates a loop adjacent to one of the conserved tryptophan residues. Functionally, the SBD from AKINβγ and AKINβ2 could bind starch, but there was an important difference in the association when an amylose/amylopectin (A/A) mixture was used. The physiological relevance of binding to starch was clear for AKINβγ, because immunolocalization experiments identified this protein inside the chloroplast. SnRK1 activity was not affected by the addition of A/A to the reaction mixture. However, addition of starch inhibited the activity 85%. Furthermore, proteins associated with A/A and starch in an in vitro-binding assay accounted for 10–20% of total SnRK1 kinase activity. Interestingly, the identification of the SnRK1 subunits associated to the protein-carbohydrate complex indicated that only the catalytic subunits, AKIN10 and AKIN11, and the regulatory subunit AKINβγ were present. These results suggest that a dimer formed between either catalytic subunit and AKINβγ could be associated with the A/A mixture in its active form but the same subunits are inactivated when binding to starch.
Towards reduction of the training and search running time complexities for non-rigid object segmentation

The problem of non-rigid object segmentation is formulated in a two-stage approach in Machine Learning based methodologies. In the first stage, the automatic initialization problem is solved by the estimation of a rigid shape of the object. In the second stage, the non-rigid segmentation is performed. The rational behind this strategy, is that the rigid detection can be performed at lower dimensional space than the original contour space. In this paper, we explore this idea and propose the use of manifolds to reduce even more the dimensionality of the rigid transformation space (first stage) of current state-of-the-art top-down segmentation methodologies. Also, we propose the use of deep belief networks to allow for a training process capable to produce robust appearance models. Experiments in lips segmentation from frontal face images are conducted to testify the performance of the proposed algorithm.
Examining Semantic Priming in a Delayed Naming Task

Semantic priming refers to the finding that a word response is facilitated if it is preceded by a related word compared to when it is preceded by an unrelated word. Research has shown that semantic priming effects still occur under task conditions which would permit ample time for the completion of word recognition processes prior to response production. This study sought to examine the impact of a related prime-target context on word production. Participants completed an immediate word naming task and a delayed word naming task. There was a semantic priming effect for the immediate naming task. For the delayed naming task semantic priming was only evident at the 500 ms cue delay. This suggests a limited time frame in which a semantic context is able to facilitate word production.
Moving object tracking via one-dimensional optical flow using queue

Although optical flow is useful for tracking of a moving object, estimation by the gradient method is ill-posed problem. In order to avoid ill-posedness, we have proposed a tracking method via one dimensional optical flow (1D flow), which is calculated on the straight line (calculation axis). The calculation axes are spanned several direction based on the estimated motion. This method votes with the number of positive 1D flow and updates the calculation axes depending on the voting result. We update these axes every five frames to achieve stable estimation of motion. Hence, it is impossible to track a object which moves fast because this object moves to the outside of the region of interest. If the calculation axes are updated every frame to track a object which moves fast, then this method tends to estimate the wrong direction due to vibration of the camera or partial occlusion. We propose a tracking method via 1D flow using queue. We prepare queues of which the length is five for each direction. The number of positive flow is enqueued. Direction which has maximum value is regarded as the moving direction. We can achieve stable tracking even if a object moves fast, because the calculation axes are updated every frame and estimation of the moving direction is based on the information of the past five frames. In experiment for real image sequences, the proposed method can track a object which moves fast.
Jersey number detection in sports video for athlete identification

Athlete identification is important for sport video content analysis since users often care about the video clips with their preferred athletes. In this paper, we propose a method for athlete identification by combing the segmentation, tracking and recognition procedures into a coarse-to-fine scheme for jersey number (digital characters on sport shirt) detection. Firstly, image segmentation is employed to separate the jersey number regions with its background. And size/pipe-like attributes of digital characters are used to filter out candidates. Then, a K-NN (K nearest neighbor) classifier is employed to classify a candidate into a digit in "0-9" or negative. In the recognition procedure, we use the Zernike moment features, which are invariant to rotation and scale for digital shape recognition. Synthetic training samples with different fonts are used to represent the pattern of digital characters with non-rigid deformation. Once a character candidate is detected, a SSD (smallest square distance)-based tracking procedure is started. The recognition procedure is performed every several frames in the tracking process. After tracking tens of frames, the overall recognition results are combined to determine if a candidate is a true jersey number or not by a voting procedure. Experiments on several types of sports video shows encouraging result.
From data to graphs to words - but where are the models?

The pioneers of statistics focused on parametric estimation and summary to communicate statistical findings. The tradition of basing inference on parametric fits is a central mode in statistics education, but in statistics applications, computer- based graphical summary is playing an increasingly important role. A parallel development has been the spread of statistics education to almost all disciplines, and thus the need to communicate statistical results to non-specialists has become more acute. These influences of more graphics and a wider distribution require adaptation in our statistics courses. This paper provides examples of, and arguments for, the use of simulation and graphical display, and the role of these techniques in enhancing the verbalization of analytical results. The immediate goal of the paper is to persuade those who design curricula for early statistics courses to provide a serious introduction to graphical data analysis, at the expense of some traditional parametric inference. The ultimate goal is to enable more students to communicate statistical findings effectively.
Sensorless Maximum Power Point Tracking in multi-type wind energy conversion systems

This paper develops a framework of Maximum Power Point Tracking (MPPT) of Doubly-Fed Induction Generator (DFIG) based and Permanent Magnet Synchronous Generator (PMSG) based wind energy systems. The principles of MPPT in various wind generators are firstly investigated and feedback control schemes to realize MPPT with/wihtout wind speed measurements are presented. Stability analysis of the wind energy conversion system with sensorless MPPT is investigated. Case studies of various wind energy system configurations are given to demonstrate the MPPT control schemes.