Miracle-dz/UniFAR-FTUs huggingface dataset:
The Observational Equivalence of Natural and Unnatural Rate Theories of Macroeconomics

The usual proof that Friedman's simple k-percent growth rule for the money supply is suboptimal comes from mechanically manipulating a reduced-form equation. Those manipulations, in general, show that pursuing a rule with feedback from current economic conditions to the money supply is better than following Friedman's advice. To be valid, the proof requires that, as written in one particular way, the reduced-form equation will remain unaltered when the monetary authority departs from the old "rule" used during the estimation period and follows a new one. Here I point out that there are always alternative ways of writing the reduced form, one being observationally equivalent with the other, so that each is equally valid in the estimation period. If one assumes that the first form is invariant when the policy rule is changed, the proof of the superiority of rules with feedback over Friedman's rule goes through. But if one assumes that it is the reduced form as written in the second way that remains unchanged, the proof that Friedman is wrong does not obtain-instead, the implication is that Friedman's rule does as well as any other deterministic feedback rule and better than a stochastic rule. Therefore, estimates of reduced forms alone will not permit one to settle the difference between Friedman and advocates of rules with feedback. Given any set of reduced-form estimates, there is an invariance assumption that will permit a member of either camp to make his point. In effect, then, this paper poses the question: Does the view that Friedman's k-percent feedback rule is as good as any other deterministic feedback rule place any restrictions on reduced forms? The answer is no. This is distressing since, for a given sampling interval and estimation period, the reduced-form estimates summarize everything that the data can ever tell
Nutrient and water use by Bt. cotton (Gossypium hirsutum) under drip fertigation

Field experiment was conducted for three years from 20082010 on medium clay soil at Rahuri to find out the effect of drip fertigation on growth, yield, water use and nutrient dynamics for Bt Cotton {Gossypium hirsutum (L.)}. The experiment was laid out in a randomized block design with eight treatments replicated three times. Pooled data of three years indicated that drip resulted into 21.5 to 23% increase in yield with 53.7% water saving, whereas drip with fertigation resulted into 43 to 66% increase in seed cotton yield with equal amount of water sav- ing as compared to conventional method. The maximum seed cotton yield (4.53 t/ha) was recorded when 125% fertigation was given. However, it was on par with 100% and 75% fertigation treatments. The seed cotton yield (3.91 t/ha) obtained under 75% fertigation was on par with 100% conventional fertilizer applied through soil (3.46 t/ ha) indicated 25% fertilizer saving under fertigation. The mobility of nutrients was observed more in drip fertigation than conventional method. N and K moved upto 30 cm, while P moved 15 cm laterally and vertically. The uptake of nutrients was also relatively more in fertigation treatments than rest of the other treatments. Maximum water use efficiency (1.42 t/hacm) was obtained in treatment, where 125% water soluble fertilizers were applied through drip. The 75% drip fertigation as per schedule B was found best practice to obtain better yield and improve nutri- ent and water use of Bt. cotton.
Immune checkpoint blockade as a potential therapeutic target: surveying CNS malignancies.

BACKGROUND Expression of programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) across glioma grades is undocumented, and their interactions with commonly expressed genetic and epigenetic alterations are undefined but nonetheless highly relevant to combinatorial treatments. METHODS Patients with CNS malignancies were profiled by Caris Life Sciences from 2009 to 2016. Immunohistochemistry findings for PD-1 on tumor-infiltrating lymphocytes (TIL) and PD-L1 on tumor cells were available for 347 cases. Next-generation sequencing, pyrosequencing, immunohistochemistry, fragment analysis, and fluorescence in situ hybridization were used to determine isocitrate dehydrogenase 1 (IDH1), phosphatase and tensin homolog (PTEN), and tumor protein 53 mutational status, O(6)-DNA methylguanine-methyltransferase promoter methylation (MGMT-Me) status, PTEN expression, plus epidermal growth factor receptor variant III and 1p/19q codeletion status. RESULTS PD-1+ TIL expression and grade IV gliomas were significantly positively correlated (odds ratio [OR]: 6.363; 95% CI: 1.263, 96.236)-especially in gliosarcomas compared with glioblastoma multiforme (P = .014). PD-L1 expression was significantly correlated with tumor grade with all PD-L1+ cases (n = 21) being associated with grade IV gliomas. PD-1+ TIL expression and PD-L1 expression were significantly correlated (OR: 5.209; 95% CI: 1.555, 20.144). Mutations of PTEN, tumor protein 53, BRAF, IDH1, and epidermal growth factor receptor or MGMT-Me did not associate with increased intratumoral expression of either PD-1+ TIL or PD-L1 in glioblastoma multiforme even before false discovery rate correction for multiple comparison. CONCLUSIONS Targeting immune checkpoints in combination with other therapeutics based on positive biomarker selection will require screening of large patient cohorts.
RILoD: Reduction of Information Loss in a WSN System in the Presence of Dumb Nodes

A sensor node behaves as “dumb,” when it continues to sense its vicinity, but fail to communicate with its neighbors due to the shrinkage in the communication range attributed to adverse environmental conditions such as rainfall, fog, and high temperature. However, the node behaves normally with the resumption of favorable environmental conditions. The nodes get isolated from the network only temporarily because of the temporal nature of adverse environmental conditions. Therefore, the dumb behavior of sensor nodes is temporary in nature. During the period of isolation, a sensor node continues its sensing, but it cannot send the sensed information to the sink, which increases the possibility of information loss in the network. Connectivity reestablishment of an isolated node with the network by activating the intermediate sleep nodes or adjusting the communication range gives the opportunity to send data to the sink. However, all the isolated nodes cannot reestablish connectivity due to the unavailability of intermediate nodes in the reduced or adjusted communication range. In this context, we propose a scheme, named RILoD, for a self-adaptive wireless sensor network, in which we use mobile mules to collect data from these isolated nodes and deliver them to the parent node, which can, in turn, forward the data to the sink. Using this technique, every isolated node can send their sensed information to the sink. Simulation results show that the proposed scheme reduces information loss by up to 32%. Furthermore, the proposed scheme has improved success probability of data delivery over the existing state of the art.
Hemorrhage Based on New Association Classification Algorithm

Subarachnoid hemorrhage (SAH) is one of the most common cerebrovascular emergencies, which can lead to serious consequences. Spontaneous subarachnoid hemorrhage accounts for about 15% of acute cerebrovascular accidents. Among them, spontaneous subarachnoid hemorrhage caused by rupture of an intracranial aneurysm or vascular malformation is more common, accounting for about 85%. +erefore, it is very important to detect the early symptoms of subarachnoid hemorrhage through reasonable means and to carry out appropriate clinical intervention and treatment. With the development of imaging technology, computed tomography angiography (CTA) is widely used in clinical practice. However, the accuracy of manual recognition of CT images is not very high, and the efficiency is low. +e emergence of data mining technology is gradually solving this problem. In this paper, we introduce and summarize the development of data mining, domestic and foreign research progress, the application status of data mining in the medical field, and the main technologies and methods of data mining. We study the application of association rule extraction technology in data mining in the medical field. +e Apriori algorithm for finding frequent item sets in association rule extraction and its series of improved algorithms are studied and finally combined the characteristics of medical CT images, an image mining method of association rules based on the gray-level cooccurrence matrix is proposed. Based on the FP-growth algorithm, the NCFP-growth algorithm based on association rules is proposed and compared with the mining effect of several other algorithms. +e proposed algorithm achieves a classification accuracy of above 90%, which is higher than the Apriori algorithm and its improved variations.
Direct-Acting Antiviral Agents for Hepatitis C Virus Infection—From Drug Discovery to Successful Implementation in Clinical Practice

Today, hepatitis C virus infection affects up to 1.5 million people per year and is responsible for 29 thousand deaths per year. In the 1970s, the clinical observation of unclear, transfusion-related cases of hepatitis ignited scientific curiosity, and after years of intensive, basic research, the hepatitis C virus was discovered and described as the causative agent for these cases of unclear hepatitis in 1989. Even before the description of the hepatitis C virus, clinicians had started treating infected individuals with interferon. However, intense side effects and limited antiviral efficacy have been major challenges, shaping the aim for the development of more suitable and specific treatments. Before direct-acting antiviral agents could be developed, a detailed understanding of viral properties was necessary. In the years after the discovery of the new virus, several research groups had been working on the hepatitis C virus biology and finally revealed the replication cycle. This knowledge was the basis for the later development of specific antiviral drugs referred to as direct-acting antiviral agents. In 2011, roughly 22 years after the discovery of the hepatitis C virus, the first two drugs became available and paved the way for a revolution in hepatitis C therapy. Today, the treatment of chronic hepatitis C virus infection does not rely on interferon anymore, and the treatment response rate is above 90% in most cases, including those with unsuccessful pretreatments. Regardless of the clinical and scientific success story, some challenges remain until the HCV elimination goals announced by the World Health Organization are met.
The polar-like decomposition and its applications

In this paper, we present a unique polar-like decomposition theorem for rectangular complex matrices. Applying this decomposition, we define on the set of rectangular matrices a new partial ordering called WL(weak L? wner) partial order ? an extension of the GL(generalized L? wner) partial order, and derive some basic properties of the new partial ordering.
Resonante magnetoelektrische Sensoren zurDetektion niederfrequenter Magnetfelder

Magnetoelektrische (ME-) Komposite konnen als Sensoren fur magnetische Felder genutzt werden. Das Prinzip beruht in der mechanischen Kopplung einer magnetostriktiven Phase mit einer piezoelektrischen Phase, in welcher durch Dehnungsubertragung proportional zur Magnetisierung eine elektrische Spannung generiert wird. An Epoxyd-laminierten Kompositen aus Terfenol-D-Folien und PZT mit Masen von 14 cm x 6 cm x 0,4 cm wurden magnetische Auflosungsgrenzen (LOD) von 20 pT Hz−1/2 fur 1 Hz bis 10 Hz gezeigt (Zhai et al. APL 2006). Damit bieten ME- Sensoren das Potential biomagnetische Signale vom Herzen oder Gehirn zu detektieren. Fur eine medizinisch sinnvolle raumliche Auflosung der Felder ist es erforderlich, die Grose der Sensoren bei gleichbleibendem LOD zu verringern. In der Dissertation wird dieses Ziel durch die Nutzung der Dunnfilmtechnologie und der Mikrosystemtechnologie verfolgt. Ein poly-Si basierter Prozess auf Waferebene wird fur die Herstellung der ME-Dunnfilm-Sensoren entwickelt. Die mechanische Kopplung erfolgt uber eine Biegebalkenstruktur mit Breite 1 mm bzw. 2 mm, Lange 3 mm und Dicke 50 µm bzw. 25 µm. Als aktive Schichten werden 2 µm AlN bzw. PZT, und FeCoSiB-Multilagen integriert. Die Einflusse der Geometrievariation, der Reduktion der Luftdampfung, der Elektrodenform und des magnetischen Zustandes auf die Sensitivitat, das Rauschen und auf das LOD wird diskutiert. Mit den Sensoren werden in Resonanz bei ca. 7,5 kHz unter Vakuumbedingungen weltweit hochste Sensitivitaten von 60 kV/T (30 kV cm−1 Oe−1) erzielt. Durch thermisch- mechanisches Rauschen ist das LOD in Resonanz dabei auf ca. 2 pT Hz−1/2 begrenzt. Durch Nutzung der ∆E-Eigenschaften kann uber eine Frequenzmodulationstechnik auch bei niedrigen Frequenzen (5 Hz - 30 Hz) ein gutes LOD von bis zu 100 pT Hz−1/2 erreicht werden. Die Ergebnisse zeigen hierbei, dass eine weitere Verbesserungen der magnetischen Phase und Erhohung der Gute das Potential bieten, die Auflosungsgrenze weiter zu verbessern.
Dynamic region-of-interest acquisition and face tracking for intelligent surveillance system

Recently, surveillance systems gain more attraction than simple CCTV systems, especially for complicated security environment. The major purpose of the proposed system is to monitor and track intruders. More specifically, accurate identification of each intruder is more important than simply recording what they are doing. Most existing surveillance systems simply keep recording the fixed viewing area, and some others adopt the tracking technique for wider coverage. Although panning and tilting the camera can extend the viewing area, only a few automatic zoom control techniques for acquiring the optimum ROI has been proposed. This paper describes a system for tracking multiple faces from input video sequences using facial convex hull-based facial segmentation and robust hausdorff distance. The proposed algorithm adapts skin color reference map in the YCbCr color space and hair color reference map in the RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide experimental result to demonstrate the performance of the proposed tracking algorithm, which efficiently tracks rotating, and zooming faces as well as multiple faces in video sequences obtained from at CCD camera.
An Efficient Adaptive Attention Neural Network for Social Recommendation

Traditional recommendation algorithms based on collaborative filtering suffer from a data sparsity problem. The emergence of online social network has enriched the user’s information, realizing a new way to solve recommendation tasks. Social-aware recommendation algorithms can effectively alleviate the data sparsity problem and improve the performance of recommendation systems. Despite the success of these algorithms, they have some common limitations. Most algorithms assume that social networks are homogeneous, with similar preferences among connected users. However, users may only share similar preferences in some aspects. Besides, different friends affect the user’s preference in different levels. And this influence of friends on users’ preference should be adaptive. Even close friends may have different influences in different decision-making processes. For example, a user may trust a friend in “travel” but distrust this friend in “music” because this friend had more travel experiences. Motivated by the above limitations, we designed a neural network model called adaptive attention neural network for social recommendation (ANSR) to study the interaction between a user and his or her social friends as well as infer the complex influence of the user’s social relationships on the user’s preferences. By utilizing the co-attention mechanism, we can not only extract the user’s special attention to certain aspects of their friends but also determine the adaptive influences of different friends on the user. When the user interacts with different items, different attention weights will be assigned to the user and his or her friends, respectively. In addition, we also utilize network embedding to learn more efficient features of each user and incorporate these features into the ANSR to enhance the recommendation results. Moreover, we also conduct extensive experiments on four different real-world datasets and demonstrate that our proposed method performs better on all datasets compared with the state-of-the-art baseline methods.
The silicon-vacancy centre in diamond for quantum information processing

Atomic defects in solids offer access to atom-like quantum properties without complex trapping methods while displaying a rich physics due to interactions with their solidstate environment. Such properties have made them an advantageous building block for quantum information processing, in particular to construct a quantum network, where information would be encoded in spins and transferred between nodes via photons. Among defects in solids, the negatively charged silicon-vacancy centre in diamond (SiV−) has attracted attention for its very promising optical properties for such a network. In this thesis, we investigate the spin properties of the silicon-vacancy centre as a potential spin-photon interface. First, we use resonant excitation of an SiV− centre in an external magnetic field to selectively address different electronic states and analyse the resulting fluorescence. We find evidence of selection rules in the optical transitions revealing that the centre possesses an electronic spin S = 1/2. Making use of the dependence of such selection rules on the applied magnetic field orientation, we resonantly drive two optical transitions forming a Λ-scheme. In the double resonance condition, we achieve coherent population trapping, whereby the SiV− is pumped into a dark state corresponding to a superposition of the two addressed ground states of opposite spin. This technique allows us to evaluate the coherence time of the dark state and hence of the spin, while demonstrating the possibility of all-optical control of the spin when a Λ-scheme is available. We then use resonant optical pulses to initialise and read out the spin state of a single SiV−. By tuning a microwave pulse into resonance between two ground states of opposite spin, we demonstrate optically detected magnetic resonance. Subsequently, by varying the duration of a resonant microwave pulse, we achieve coherent control of a single SiV− electronic spin. Through Ramsey interferometry, we measure a spin dephasing time of 115±9 ns. We then investigate interactions of the SiV− with its environment. We analyse the hyperfine interaction of the SiV− spin with the nuclear spin of 29Si, with a view to taking advantage of the long-lived nuclear spin in the future. We show that single-phonon-
Controlled Transition Density Based Power Constrained Scan-BIST with Reduced Test Time

In recent years, circuit size has increased due to scaling down of technology. Controlling power dissipation in these large circuits during test sessions is one of the major concerns in VLSI testing. In general power dissipation of a system in test mode is higher than the normal mode. This extra power can cause problems such as instantaneous power surge that causes circuit damage, formation of hot spots, difficulty in performance verification and reduction of system lifetime and product yield. The reason behind the high power dissipation during test is because unlike normal mode operation of the system correlation between consecutive test patterns does not exist in test mode. This is particularly true in case of Built-In-Self-Test (BIST) and scan -Based BIST, two popular DFT methodologies. To increase the correlation between consecutive vectors during testing, several techniques have been proposed for creating low transition density in the pattern sets and thus control the power dissipation. However, this in turn increases the test application time as the test has to run for longer test sessions to reach sufficient fault coverage. Increase in test time is undesirable as testing cost of a chip is directly related to the time it takes to test the chip. This research aims to provide a common way to deal with both the problems by optimizing test lengths for power constraint scan BIST circuits and reduce required test application time. We first show that test application time can be reduced in a power constrained test set up based on the transition density in the vector sets for the designs which contained multiple scan chains and the power dissipation can be controlled through adapting the scan clock dynamically ensuring that the test process does not cross the power budget. Having a method of speeding up test time for applying test in a power constrained set up; we next analyze how reduction in transition density in vectors affects the fault coverage of a circuit
Impact of psychological trauma on the development of psychotic symptoms: relationship with psychosis proneness

Background The reported link between psychological trauma and onset of psychosis remains controversial. Aims To examine associations between self-reported psychological trauma and psychotic symptoms as a function of prior evidence of vulnerability to psychosis (psychosis proneness). Method At baseline, 2524 adolescents aged 14–24 years provided self-reports on psychological trauma and psychosis proneness, and at follow-up (on average 42 months later) participants were interviewed for presence of psychotic symptoms. Results Self-reported trauma was associated with psychotic symptoms, in particular at more severe levels (adjusted OR 1.89, 95% CI 1.16–3.08) and following trauma associated with intense fear, helplessness or horror. The risk difference between those with and without self-reported trauma at baseline was 7% in the group with baseline psychosis proneness, but only 1.8% in those without (adjusted test for difference between these two effect sizes: χ2=4.6, P=0.032). Conclusions Exposure to psychological trauma may increase the risk of psychotic symptoms in people vulnerable to psychosis.
Interstellar scattering effects on the detection of narrow-band signals

The detection and decoding of narrow-band radio signals are investigated after propagation through the turbulent, ionized interstellar medium. For most lines of sight through the Galaxy, spectral broadening due to scattering below about 0.1 Hz at 1 GHz occurs. Spectral broadening is therefore unimportant for the detection of hypothesized signals from extraterrestrial intelligence. Intensity scintillations, however, are of considerable importance. They both help and hinder detection: signals too weak to be detected without the scattering medium may be modulated above the detection threshold while, conversely, signals above threshold can be modulated below. In strong scattering (distances above about 100 pc at 1 GHz), multiple observations of a given target comprise a strategy that is superior to single observations even when the total time per target is held fixed. Decoding information carrying signals may encounter difficulties due to intensity scintillations. 49 refs.