Miracle-dz/UniFAR-FTUs huggingface dataset:
A Floorplan-Aware Dynamic Inductive Noise Controller for Reliable 2D and 3D Microprocessors

Power delivery is a growing reliability concern in microprocessors as the industry moves toward feature-rich, powerhungrier designs. To battle the ever-aggravating power consumption, modern microprocessor designers or researchers propose and apply aggressive power-saving techniques in the form of clock-gating and/or power-gating in order to operate the processor within a given power envelope. However, these techniques often lead to high-frequency current variations, which can stress the power delivery system and jeopardize reliability due to inductive noise (L di dt ) in the power supply network. In addition, with the advent of 3D stacked IC technology that facilitates the design of processors with much higher module density, the design of a low impedance powerdelivery network can be a daunting challenge. To counteract these issues, modern microprocessors are designed to operate under the worst-case current assumption by deploying adequate decoupling capacitance. With the lowering of supply voltages and increased leakage power and current consumption, designing a processor for the worst case is becoming less appealing. In this paper, we propose a new dynamic inductive-noise controlling mechanism at the microarchitectural level that will limit the on-die current demand within predefined bounds, regardless of the native power and current characteristics of running applications. By dynamically monitoring the access patterns of microarchitectural modules, our mechanism can effectively limit simultaneous switching activity of closeby modules, thereby leveling voltage ringing at local powerpins. Compared to prior art, our di/dt controller is the first that takes the processor’s floorplan as well as its power-pin distribution into account to provide a finer-grained control with minimal performance degradation. Based on the evaluation results using 2D and 3D floorplans, we show that our techniques can significantly improve inductive noise induced by current demand variation and reduce the average current variability by up to 7 times with an average performance overhead of 4.0% (2D floorplan) and 3.8% (3D floorplan).
Network2Canvas: network visualization on a canvas with enrichment analysis

MOTIVATION Networks are vital to computational systems biology research, but visualizing them is a challenge. For networks larger than ∼100 nodes and ∼200 links, ball-and-stick diagrams fail to convey much information. To address this, we developed Network2Canvas (N2C), a web application that provides an alternative way to view networks. N2C visualizes networks by placing nodes on a square toroidal canvas. The network nodes are clustered on the canvas using simulated annealing to maximize local connections where a node's brightness is made proportional to its local fitness. The interactive canvas is implemented in HyperText Markup Language (HTML)5 with the JavaScript library Data-Driven Documents (D3). We applied N2C to visualize 30 canvases made from human and mouse gene-set libraries and 6 canvases made from the Food and Drug Administration (FDA)-approved drug-set libraries. Given lists of genes or drugs, enriched terms are highlighted on the canvases, and their degree of clustering is computed. Because N2C produces visual patterns of enriched terms on canvases, a trained eye can detect signatures instantly. In summary, N2C provides a new flexible method to visualize large networks and can be used to perform and visualize gene-set and drug-set enrichment analyses. AVAILABILITY N2C is freely available at http://www.maayanlab. net/N2C and is open source. CONTACT avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
A multimodal probabilistic model for gesture--based control of sound synthesis

In this paper, we propose a multimodal approach to create the mapping between gesture and sound in interactive music systems. Specifically, we propose to use a multimodal HMM to conjointly model the gesture and sound parameters. Our approach is compatible with a learning method that allows users to define the gesture--sound relationships interactively. We describe an implementation of this method for the control of physical modeling sound synthesis. Our model is promising to capture expressive gesture variations while guaranteeing a consistent relationship between gesture and sound.
Exploring the use of acoustic embeddings in neural machine translation

Neural Machine Translation (NMT) has recently demonstrated improved performance over statistical machine translation and relies on an encoder-decoder framework for translating text from source to target. The structure of NMT makes it amenable to add auxiliary features, which can provide complementary information to that present in the source text. In this paper, auxiliary features derived from accompanying audio, are investigated for NMT and are compared and combined with text-derived features. These acoustic embeddings can help resolve ambiguity in the translation, thus improving the output. The following features are experimented with: Latent Dirichlet Allocation (LDA) topic vectors and GMM subspace i-vectors derived from audio. These are contrasted against: skip-gram/Word2Vec features and LDA features derived from text. The results are encouraging and show that acoustic information does help with NMT, leading to an overall 3.3% relative improvement in BLEU scores.
Semi-Supervised Video Segmentation Using Tree Structured Graphical Models

We present a novel patch-based probabilistic graphical model for semi-supervised video segmentation. At the heart of our model is a temporal tree structure that links patches in adjacent frames through the video sequence. This permits exact inference of pixel labels without resorting to traditional short time window-based video processing or instantaneous decision making. The input to our algorithm is labeled key frame(s) of a video sequence and the output is pixel-wise labels along with their confidences. We propose an efficient inference scheme that performs exact inference over the temporal tree, and optionally a per frame label smoothing step using loopy BP, to estimate pixel-wise labels and their posteriors. These posteriors are used to learn pixel unaries by training a Random Decision Forest in a semi-supervised manner. These unaries are used in a second iteration of label inference to improve the segmentation quality. We demonstrate the efficacy of our proposed algorithm using several qualitative and quantitative tests on both foreground/background and multiclass video segmentation problems using publicly available and our own datasets.
How Constraining is Capital Mobility? The Partisan Hypothesis in an Open Economy

A growing literature argues that international financial integration has eliminated the possibility for distinct partisan macroeconomic strategies. I test this hypothesis by reformulating the partisan hypothesis in an open-economy context and conducting pooled timeseries analysis of budget balances, real interest rates, and capital controls for fourteen OECD countries between 1970 and 1994. The analysis provides little evidence that financial integration has eliminated distinct partisan macroeconomic policies. Under fixed exchange rates leftist governments run larger deficits than rightist governments and use capital controls to reduce interest rate premia. Under floating exchange rates leftist governments pursue looser monetary policies than rightist governments. While partisan distinctions do weaken in the 1990s in countries with fixed exchange rates, this is attributed to the recession of the early 1990s and to important institutional changes in the European Union. International financial integration, therefore, does not prevent governments from pursuing distinct partisan macroeconomic policies.
Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition

Many of the state-of-the-art algorithms for gesture recognition are based on Conditional Random Fields (CRFs). Successful approaches, such as the Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose values are mapped to the values of the labels. In this paper we propose a novel methodology to set the latent values according to the gesture complexity. We use an heuristic that iterates through the samples associated with each label value, estimating their complexity. We then use it to assign the latent values to the label values. We evaluate our method on the task of recognizing human gestures from video streams. The experiments were performed in binary datasets, generated by grouping different labels. Our results demonstrate that our approach outperforms the arbitrary one in many cases, increasing the accuracy by up to 10%.
Stability analysis for set-based control within the singularity-robust multiple task-priority inverse kinematics framework

Inverse kinematics algorithms are commonly used in robotic systems to accomplish desired behavior, and several methods exist to ensure the achievement of several tasks simultaneously. The multiple task-priority inverse kinematics framework allows tasks to be considered in a prioritized order by projecting task velocities through the nullspaces of higher priority tasks. This paper extends this framework to handle set-based tasks, i.e. tasks with a range of valid values, in addition to equality tasks, which have a specific desired value. Examples of such tasks are joint limit and obstacle avoidance. The proposed method is proven to ensure asymptotic convergence of the equality task errors and the satisfaction of all high-priority set-based tasks. Simulations results confirm the effectiveness of the proposed approach.
Physical performance is associated with markers of vascular inflammation in patients with coronary heart disease

Background Coronary heart disease as well as major cardiovascular risk factors are associated with elevated levels of proinflammatory markers. There is, however, limited information about how changes in lifestyle improving the cardiovascular risk profile influence these levels. The aim of the study was to evaluate whether changes in lifestyle measures with special attention to physical activity, were associated with the levels of such markers. Design Coronary heart disease patients (n = 197) were randomized to either a comprehensive lifestyle intervention programme comprising regular physical activity, low fat diet and smoking cessation, or usual care with routine follow-up in the outpatient clinic for 6 months. An exercise test and fasting blood samples analysed for soluble cell adhesion molecules, C reactive protein and pro-inflammatory cytokines were evaluated before and at the end of the study. Results Improved diet, physical performance and reduction in smoking were obtained in the intervention programme when compared with usual care patients, but no significant group differences in levels of inflammatory markers were observed. In the total population, however, physical performance significantly and inversely predicted levels of soluble cell adhesion molecule 1, (P>0.001), C-reactive protein (P>0.001) and interleukin-6 (P=0.01) at 6 months. Smokers had elevated levels of soluble cell adhesion molecule 1 when compared with non-smokers (P=0.011). Conclusions We demonstrated that physical performance is inversely correlated with levels of pro-inflammatory markers in coronary heart disease patients, possibly retarding the process of atherosclerosis. No effect on inflammatory markers was obtained with a 6-month lifestyle intervention programme when compared with patients who received usual care follow-up. Eur J Cardiovasc Prev Rehabil 13:356-362 © 2006 The European Society of Cardiology
A 75μW real-time scalable network controller and a 25μW ExG sensor IC for compact sleep-monitoring applications

Recently, a wearable body-sensor network realized continuous sleep monitoring by ExG (EEG, EMG, EOG, and ECG) extraction from a sleeper's face [1]. At least 14 sensors were placed on the face, and were managed by a network controller for sleep monitoring. However, the system in [2] was too bulky and heavy (127×63.5×28mm3, 210g) to wear during sleep, and consumed high power (>100mW). Another system used a CMOS IC sensor for smaller power consumption (5 to 10mW/sensor), but it was still inconvenient to wear due to its cumbersome size (49×19×6mm3) [3]. This paper presents a 5g sub-500VW compact sleep-monitoring system based on a low-power network controller and ExG sensors, and Planar Fashionable Circuit Board (P-FCB) technology [4]. The entire sleep monitor operates on a 1.5V 10mAh single tiny coin battery (Φ=5.8mm, T=2.1mm, 200mg) stacked on the controller chip. The power dissipation of 14 sensors and a controller is below 425μW, which is <1.5% of previous works [2–3]. Its low power consumption is possible by the Linked-List Manager (LLM)-based protocol such as Adaptive Dual-Mode Control (ADMC) and Continuous Data Transmission (CDT), and low-swing clock and data transceivers.
Training Conditional Random Fields Using Virtual Evidence Boosting

While conditional random fields (CRFs) have been applied successfully in a variety of domains, their training remains a challenging task. In this paper, we introduce a novel training method for CRFs, called virtual evidence boosting, which simultaneously performs feature selection and parameter estimation. To achieve this, we extend standard boosting to handle virtual evidence, where an observation can be specified as a distribution rather than a single number. This extension allows us to develop a unified framework for learning both local and compatibility features in CRFs. In experiments on synthetic data as well as real activity classification problems, our new training algorithm outperforms other training approaches including maximum likelihood, maximum pseudo-likelihood, and the most recent boosted random fields.
Optimizing root system architecture in biofuel crops for sustainable energy production and soil carbon sequestration

Root system architecture (RSA) describes the dynamic spatial configuration of different types and ages of roots in a plant, which allows adaptation to different environments. Modifications in RSA enhance agronomic traits in crops and have been implicated in soil organic carbon content. Together, these fundamental properties of RSA contribute to the net carbon balance and overall sustainability of biofuels. In this article, we will review recent data supporting carbon sequestration by biofuel crops, highlight current progress in studying RSA, and discuss future opportunities for optimizing RSA for biofuel production and soil carbon sequestration.
Determination of Oxygen in Cast Iron

Synopsis: Recently, the determination of oxygen in cast iron has been required and it is important to determine not only the total oxygen but the oxygen as each oxide. The non-metallic inclusion method such as the chlorine method, the electrolytic method, the hot nitric acid method etc . is the usual method for determining each oxide in cast iron but this method is tedious and not reliable. The authors studied on the fractional vacuum-fusion method and it was found that by using a tin-bath, FeO could be extracted at 1050°C, MnO at 1150°C, SiO2 at 1450°C and Al2O3 at 1750°C by this simple method. The same cast iron samples were analyzed bv the fractional vacuum-fusion method, the hot nitric acid method, the chlorine method and the electrolytic method. Successful results were obtained by the fractional vacuuni-fusion method in comnarison with the case of the non-metallic inclusion method.
Implementing and Evaluating the Dynamic Manet On-demand Protocol in Wireless Sensor Networks

According to MIT’s Technology Review[1], wireless sensor networks are one of the ten emerging technologies that will change the world. Undoubtedly, this fact has been widely understood by the research community and explains the interest received by the research area of sensor networks. Many issues still need to be solved, one of them is enabling software to handle multi-hop routing among nodes. The best suitable protocol may vary depending on the hardware used and on the purpose of the network. That is the reason why a wide range of proposed protocols need to be evaluated, in order to find which protocol properties are appropriate for each kind of network. This is what this master’s thesis aims at contributing to, by implementing and evaluating a particular routing protocol for mobile ad-hoc networks. The protocol considered is the Dynamic MANET Ondemand (DYMO) protocol and is evaluated in the context of sensor networks. The implementation was made in nesC on TinyOS, a programming language and an operating system specifically designed for distributed and embedded software limited by the hardware constraints of sensor networks. A simplified version of the DYMO protocol has been implemented with a more compact packet format adapted to these constraints. The evaluation consists of experiments performed on real hardware to determine the applicability of the DYMO protocol for a typical sensor network application. The results confirm the strong relationship between the use of the radio emitter and the power consumption. They also provide an idea of the life expectancy of such a sensor network depending on the traffic rate of the application.
Tracking the allocation of attention using human pupillary oscillations

The muscles that control the pupil are richly innervated by the autonomic nervous system. While there are central pathways that drive pupil dilations in relation to arousal, there is no anatomical evidence that cortical centers involved with visual selective attention innervate the pupil. In this study, we show that such connections must exist. Specifically, we demonstrate a novel Pupil Frequency Tagging (PFT) method, where oscillatory changes in stimulus brightness over time are mirrored by pupil constrictions and dilations. We find that the luminance–induced pupil oscillations are enhanced when covert attention is directed to the flicker stimulus and when targets are correctly detected in an attentional tracking task. These results suggest that the amplitudes of pupil responses closely follow the allocation of focal visual attention and the encoding of stimuli. PFT provides a new opportunity to study top–down visual attention itself as well as identifying the pathways and mechanisms that support this unexpected phenomenon.
Robotic Steering of Cardiac Ultrasound Imaging Catheters

corresponding tip and imaging plane motions Robotic Steering of Cardiac Ultrasound Imaging Catheters L.J. Brattain, P.M. Loschak, C.M. Tschabrunn, E. Anter, R.D. Howe Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA MIT Lincoln Laboratory, Lexington, MA, USA Harvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA brattain@seas.harvard. edu
Microcalcifications detection using multiresolution methods

The detection of clustered microcalcifications can aid a radiologist to detect early breast cancer. Microcalcifications exhibit some important characteristics, like small size and high luminosity. So, a CAD method can be useful to avoid them being over-looked. In the paper a multiresolution analysis is proposed by decomposing the image through a band-pass filter bank, so that in each sub-band image only the details at the given scale become visible. Thereafter all the images will be combined in a final one in order to obtain an image that contains all the details of interest at the scale where microcalcifications tend to appear. Once the image is obtained, it is necessary to determine which details correspond with microcalcifications. Statistical analysis of the histogram permits us to classify the susceptible zones containing microcalcifications. Applying these statistics techniques over the whole image and representing the results in a 2-D map, clustered microcalcifications regions appear clearly distinguishable.
Influence of Endurance Exercise Overloading Patterns on the Levels of Left Ventricular Catechoamines After a Bout of Lactate Threshold Test in Male Wistar Rat

Background: It is well known that exercise training has positive effect on catecholamine response to a given work load. But in this regard, the effective method of training needs to be studied. Objectives: The aim of this study was to compare the effects of 8 weeks endurance exercise with two overloading patterns on the left ventricular catecholamine levels. Materials and Methods: 29 male Wistar rats were randomly assigned to control (n = 9), daily sinusoidal overloading (n = 10) and weekly sinusoidal overloading (n = 10) groups. After the last exercise session, left ventricular blood samples were obtained immediately after lactate threshold test. Plasma concentrations of adrenaline and noradrenaline were measured by ELISA method. One way analysis of variance was used for analysis of the data. Results: Immediately after lactate threshold test, adrenaline level was significantly (P < 0.05) lower in weekly loading group than in control and daily loading groups. Adrenaline was higher in the daily loading group compared with control group but did not reach the significant level. Noradrenaline levels were not significantly (P > 0.05) different between three study groups. Conclusions: The results showed 8 weeks of endurance exercise with weekly sinusoidal overloading pattern could induce a lower adrenal medulla activity (reflection of physical and physiological improvement) than daily sinusoidal loading pattern in response to the same absolute work load.
Initial measurements of the cuban street network

Internet access in Cuba is severely constrained, due to limited availability, slow speeds, and high cost. Within this isolated environment, technology enthusiasts have constructed a disconnected but vibrant IP network that has grown organically to reach tens of thousands of households across Havana. We present the first detailed characterization of this deployment, which is known as the SNET, or Street Network. Working in collaboration with SNET operators, we describe the network's infrastructure and map its topology, and we measure bandwidth, available services, usage patterns, and user demographics. Qualitatively, we attempt to answer why the SNET exists and what benefits it has afforded its users. We go on to discuss technical challenges the network faces, including scalability, security, and organizational issues. To our knowledge, the SNET is the largest isolated community-driven network in existence, and its structure, successes, and obstacles show fascinating contrasts and similarities to those of the Internet at large.
A 2.7 V 900 MHz CMOS LNA and mixer

The demand for portable wireless communications systems increases the focus on radio frequency (RF) IC implementations. At the same time, fine-line CMOS process technologies offer potential for RF applications. This 2.7 V, 900 MHz, CMOS low-noise amplifier (LNA) and mixer employs a current reuse technique that increases amplifier transconductance for the LNA and mixer without increasing power dissipation, compared to standard topologies. The increased transconductance plays a key role in reducing noise figure (NF) and increasing gain.
Tamchyna Feature Selection for Factored Phrase-Based Machine Translation

In the presented work we investigate factored models for machine translation. We provide a thorough theoretical description of this machine translation paradigm. We describe a method for evaluating the complexity of factored models and verify its usefulness in practice. We present a software tool for automatic creation of machine translation experiments and search in the space of possible configurations. In the experimental part of the work we verify our analyses and give some insight into the potential of factored systems. We indicate some of the possible directions that lead to improvement in translation quality, however we conclude that it is not possible to explore these options in a fully automatic way.