Miracle-dz/UniFAR-FTUs huggingface dataset:
Structured Sparsity Learning Based Multiuser Detection in Massive-Device Multiple Access

In this work, we study the non-time-slotted massive-device multiple access (MaDMA) problem where massive user devices transmit sporadic data to a multi-antenna base station (BS). We develop a structured sparsity learning based multiuser detection (SSL-MUD) scheme. By exploiting the structured sparsity naturally embedded in user signals, our SSL-MUD scheme is able to blindly detect the user packets without any prior knowledge of the user activity state (UAS) and the channel state information (CSI), and hence significantly reduces the transmission overhead. For the blind signal detection at the BS, we put forth the turbo bilinear generalized approximate message passing (Turbo-BiG-AMP) algorithm. Simulation results demonstrate that the Turbo-BiG- AMP algorithm significantly outperforms the existing compressed sensing based approach and achieves a performance comparable to that of the oracle linear minimum mean-square error (Oracle- LMMSE) algorithm (which assumes perfect knowledge of UAS and CSI at the BS).
Affective Analysis of Professional and Amateur Abstract Paintings Using Statistical Analysis and Art Theory

When artists express their feelings through the artworks they create, it is believed that the resulting works transform into objects with “emotions” capable of conveying the artists' mood to the audience. There is little to no dispute about this belief: Regardless of the artwork, genre, time, and origin of creation, people from different backgrounds are able to read the emotional messages. This holds true even for the most abstract paintings. Could this idea be applied to machines as well? Can machines learn what makes a work of art “emotional”? In this work, we employ a state-of-the-art recognition system to learn which statistical patterns are associated with positive and negative emotions on two different datasets that comprise professional and amateur abstract artworks. Moreover, we analyze and compare two different annotation methods in order to establish the ground truth of positive and negative emotions in abstract art. Additionally, we use computer vision techniques to quantify which parts of a painting evoke positive and negative emotions. We also demonstrate how the quantification of evidence for positive and negative emotions can be used to predict which parts of a painting people prefer to focus on. This method opens new opportunities of research on why a specific painting is perceived as emotional at global and local scales.
Cooperative cache memory (CCM) based on the performance efficiency for 3D stacked memory system

: Three-dimensional (3D) memory stacking technology is one of the most promising technologies providing massive memory bandwidth, and thereby provides an opportunity to overcome performance bottlenecks in memory systems. In this paper, we analyze the performance of well-known eﬃcient memory components such as stream buﬀer, L2 cache, and victim cache in order to exploit the massive memory bandwidth of the 3D stacked memory system. This paper also presents a method to determine the optimal memory capacity with the best performance eﬃciency (a.k.a. performance improvement eﬃ-ciency (PIE)) and proposes a cooperative cache memory (CCM), which prefetches adaptively according to the memory access pattern in order to exploit the massive memory bandwidth of a 3D stacked memory sys-tem. The proposed CCM with a 512KB L2 cache and 256KB stream buﬀer with the proposed prefetching mechanism can deliver superior performance by about 13% over a conventional 8MB L2 cache.
Developing a Theory of Reverse Logistics

Reverse logistics, a fairly new concept in logistics, has gained increasing importance as a profitable and sustainable business strategy. I describe a holistic view of reverse logistics and distill 11 insights for successful implementation of reverse logistics from the existing literature and published case studies. The strategic factors consist of strategic costs, overall quality, customer service, environmental concerns, and legislative concerns. The operational factors consist of cost-benefit analysis, transportation, warehousing, supply management, remanufacturing and recycling, and packaging. Insights about these factors together form the state-of-the-art knowledge about the keys to successful design and use of reverse-logistics systems.
Design of X-band reconfigurable frequency selective surface with high isolation

In this paper, a bandpass reconfigurable frequency selective surface (FSS) with high isolation at passband, which can reconfigure the operating frequency by using a PIN diode for the X-band, was designed. The proposed surface is composed of four-legged loaded elements, inductance stubs, and a bias grid to supply power. By controlling the electrical length of the unit cell using the forward bias and zero bias of the PIN diode located between the cross-shaped pattern and the stub, the operating frequency can be reconfigured. By controlling the stub length, the switching range of the operating frequency can be controlled. Based on the design results, two types of reconfigurable FSS were constructed, and their measured transmission losses, obtained using the rectangular waveguides WR-75 and WR-90, were compared with simulation results. The comparison showed very similar results.
TBCT: Time-Borrowing and Clock Token based error correction and its application in microprocessor

Recently, large efforts are made to achieve high throughput for aggressive voltage/frequency scaling in Error Detection and Correction system. In this paper, a zero-cycle penalty Timing-Borrowing and Clock Token (TBCT) based error correction method is proposed. A new error masking flip-flop is designed to enable time-borrowing and a clock token system is provided to pay back the borrowed time by locally shifting the clock phase of vulnerable flip-flops on the cascaded pipeline. The TBCT error correction method and error masking flip-flop are implemented into a 3-stage industrial Chinese microprocessor in a 40 nm CMOS process. Final experiment results show the proposed method achieves 1%–6% higher throughput than clock gating method and 9%–41% higher throughput than instruction replay.
Probabilistic Community and Role Model for Social Networks

Numerous models have been proposed for modeling social networks to explore their structure or to address application problems, such as community detection and behavior prediction. However, the results are still far from satisfactory. One of the biggest challenges is how to capture all the information of a social network in a unified manner, such as links, communities, user attributes, roles and behaviors. In this paper, we propose a unified probabilistic framework, the Community Role Model (CRM), to model a social network. CRM incorporates all the information of nodes and edges that form a social network. We propose methods based on Gibbs sampling and an EM algorithm to estimate the model's parameters and fit our model to real social networks. Real data experiments show that CRM can be used not only to represent a social network, but also to handle various application problems with better performance than a baseline model, without any modification to the model, showing its great advantages.
Special Section: Closed-Loop Supply Chains: Practice and Potential: Integrating Closed-Loop Supply Chains and Spare-Parts Management at IBM

IBM is among the pioneers recognizing the benefits of closed-loop supply chains that integrate product returns into business operations. We worked on a project exploiting product returns as a source of spare parts. Key decisions concern what recovery opportunities to use, the channel design, and coordinating alternative supply sources. Our analytic inventory-control model and a simulation model showed that procurement-cost savings largely outweigh reverse logistics costs and that information management is essential. These findings provide a basis for significantly expanding the usage of the novel parts supply source, thereby cutting procurement costs.
Three-dimension scheduling under multi-cycle interconnect communications

The three-dimension scheduling is defined as the simultaneous application of clock selection and operation scheduling. Previous three-dimension scheduling approach does not consider the interconnect delay. However, with the advent of nanometer era, the interconnect delay may take multiple clock cycles. In this paper, we use convex programming to formulate the three-dimension scheduling problem under multi-cycle interconnect communications. Benchmark data consistently show that our approach achieves the minimum latency within an acceptable run time.
Brief training of HIV medical providers increases their frequency of delivering prevention counselling to patients at risk of transmitting HIV to others

Objective: The aim of this study was to examine whether brief training of human immunodeficiency virus (HIV) medical providers increased the frequency with which they routinely delivered prevention counselling to patients, and whether patient characteristics were associated with receipt of that counselling. Design: Longitudinal. Setting: Seven HIV clinics in the United States. Method: Medical providers received training to deliver counselling to their HIV-positive patients. The evaluation was conducted with a longitudinal cohort (N = 729) of patients at baseline (before providers received training) and at six and 12 months after the intervention was initiated. Participants reported on receipt of routine counselling (at more than half of their primary care visits) about safer sex, disclosing HIV infection to sex partners, and alcohol/drug use. Logistic regression and generalized estimating equations (GEE) methods assessed changes in receipt of counselling over time. Results: The percentage of participants reporting routine safer-sex counselling increased significantly across time (b = 0.12, standard error [SE] = 0.04; p = 0.004), as did receipt of routine counselling about disclosing their HIV infection to sex partners (b = 0.21, SE = 0.05; p < 0.001). The trend was not significant for counselling about alcohol or drug use. Sub-group analysis indicated that the upward trend in routine safer-sex counselling was significant for men who have sex with men (MSM), for participants who had more than one sexual partner in the past three months, and for those who had a CD4 count of > 500cells/mm3. Conclusion: Brief training increased the frequency with which HIV medical providers routinely delivered prevention counselling to their patients at risk of transmitting HIV to others.
A fully integrated dual-band CMOS LNA for IEEE802.16a

A fully-integrated, dual-band, high gain, low noise amplifier designed for 802.16a WMAN standard is presented. The targeted frequency bands include un-licensed band UNII 5GHz and licensed band Int'l 10GHz. Our LNA design adopts wide band input matching scheme with adjustable load to achieve an area-efficient dual-band low noise amplifier. The LNA has also achieved the gain and noise figure of 16.1dB and 3.6dB at 5GHz, and 15.7dB and 7.6dB at 10GHz. Additionally, the input reflection coefficient is -7dB and -16.5dB, and the IIP3 is 1.8dBm and 1.1dBm at 5GHz and 10GHz, respectively. Utilizing the 0. 18µm CMOS process, the LNA dissipates 14.4mW using a 1.8V supply voltage.
Compute-efficient eye state detection: algorithm, dataset and evaluations

Eye state can be used as an important cue to monitor the wellness of a patient. In this paper, we propose a computationally efficient eye state detection technique in the context of patient monitoring. The proposed method uses weighted accumulations of intensity and gradients, along with a color thresholding on a reduced set of pixels to extract the various features of the eye, which in turn are used for inferring the eye state. Additionally, we present a dataset of 2500 images that was created for evaluating the proposed technique. The method was shown to effectively differentiate open, closed and half-closed eye states with an accuracy of 91.3% when evaluated on the dataset. The computational cost of the proposed technique is evaluated and is shown to achieve about 67% savings with respect to the state of art.
Non-stationary sound source localization based on zero crossings with the detection of onset intervals

This paper presents a sound source localization algorithm based on zero crossings in acoustic reverberant environments. From sudden increases of acoustic energy in a non-stationary signal, we identify intervals which dominantly include signal components through direct paths from a source to sensors because they may provide reliable localization cues corresponding to the source direction despite the reverberation. Experimental results show that the presented algorithm which is based on zero crossings with the detection of direct components efficiently accomplishes sound source localization in reverberant environments.
Discovery of kinase spectrum selective macrocycle (16E)-14-methyl-20-oxa-5,7,14,26-tetraazatetracyclo[19.3.1.1(2,6).1(8,12)]heptacosa-1(25),2(26),3,5,8(27),9,11,16,21,23-decaene (SB1317/TG02), a potent inhibitor of cyclin dependent kinases (CDKs), Janus kinase 2 (JAK2), and fms-like tyrosine kinase-

Herein, we describe the design, synthesis, and SAR of a series of unique small molecule macrocycles that show spectrum selective kinase inhibition of CDKs, JAK2, and FLT3. The most promising leads were assessed in vitro for their inhibition of cancer cell proliferation, solubility, CYP450 inhibition, and microsomal stability. This screening cascade revealed 26 h as a preferred compound with target IC(50) of 13, 73, and 56 nM for CDK2, JAK2 and FLT3, respectively. Pharmacokinetic (PK) studies of 26 h in preclinical species showed good oral exposures. Oral efficacy was observed in colon (HCT-116) and lymphoma (Ramos) xenograft studies, in line with the observed PK/PD correlation. 26h (SB1317/TG02) was progressed into development in 2010 and is currently undergoing phase 1 clinical trials in advanced leukemias and multiple myeloma.
The Association Between the Disclosure and the Realization of Information Security Risk Factors

Firms often disclose information security risk factors in public filings such as 10-K reports. The internal information associated with disclosures may be positive or negative. In this paper, we evaluate how the nature of the disclosed security risk factors, believed to represent the firm's internal information regarding information security, is associated with future breach announcements reported in the media. For this purpose, we build a decision tree model, which classifies the occurrence of future security breaches based on the textual contents of the disclosed security risk factors. The model is able to accurately associate disclosure characteristics with breach announcements about 77% of the time. We further explore the contents of the security risk factors using text-mining techniques to provide a richer interpretation of the results. The results show that the disclosed security risk factors with risk-mitigation themes are less likely to be related to future breach announcements. We also investigate how the market interprets the nature of information security risk factors in annual reports. We find that the market reaction following the security breach announcement is different depending on the nature of the preceding disclosure. Thus, our paper contributes to the literature in information security and sheds light on how market participants can better interpret security risk factors disclosed in financial reports at the time when financial reports are released.
Happy online and in real life too?: how social media interactions affect real life well-being of students in the U.S. and Germany

Research examining the impact of social media use on the well-being of digital natives has resulted in a myriad of opposing outcomes indicating both positive and negative effects. In this paper we examine whether there is a boundary between online and offline interpersonal sphere in the cohort of student digital natives and how does it differ between German student populations and U.S. From data collected in 2013 and 2014 we find that involvement in Social Networking Sites (SNS) results more in positive emotional outcomes than in negative ones. Secondly, we conclude that there is no significant impact of SNS interactions on real life activities. We explain this striking result by focusing on separation of communication activities in online and offline life contexts. We also report on differences between U.S. and German students. The results of our study show that German students tend to separate between online and offline sphere more strongly.
Combining medications to enhance depression outcomes (CO-MED): acute and long-term outcomes of a single-blind randomized study.

OBJECTIVE Two antidepressant medication combinations were compared with selective serotonin reuptake inhibitor monotherapy to determine whether either combination produced a higher remission rate in first-step acute-phase (12 weeks) and long-term (7 months) treatment. METHOD The single-blind, prospective, randomized trial enrolled 665 outpatients at six primary and nine psychiatric care sites. Participants had at least moderately severe nonpsychotic chronic and/or recurrent major depressive disorder. Escitalopram (up to 20 mg/day) plus placebo, sustained-release bupropion (up to 400 mg/day) plus escitalopram (up to 20 mg/day), or extended-release venlafaxine (up to 300 mg/day) plus mirtazapine (up to 45 mg/day) was delivered (1:1:1 ratio) by using measurement-based care. The primary outcome was remission, defined as ratings of less than 8 and less than 6 on the last two consecutive applications of the 16-item Quick Inventory of Depressive Symptomatology--Self-Report. Secondary outcomes included side effect burden, adverse events, quality of life, functioning, and attrition. RESULTS Remission and response rates and most secondary outcomes were not different among treatment groups at 12 weeks. The remission rates were 38.8% for escitalopram-placebo, 38.9% for bupropion-escitalopram, and 37.7% for venlafaxine-mirtazapine, and the response rates were 51.6%-52.4%. The mean number of worsening adverse events was higher for venlafaxine-mirtazapine (5.7) than for escitalopram-placebo (4.7). At 7 months, remission rates (41.8%-46.6%), response rates (57.4%-59.4%), and most secondary outcomes were not significantly different. CONCLUSIONS Neither medication combination outperformed monotherapy. The combination of extended-release venlafaxine plus mirtazapine may have a greater risk of adverse events.
Dimensions of I/S Planning and Design Aids: A Functional Model of CASE Technology

Information technology is playing an increasingly integral role in the competitive strategies of many organizations. As this trend continues, it is not surprising that there is growing emphasis on the ability of organizations to plan, design and implement critical information systems. A major strategy to improve the effectiveness of these processes is the use of computer-based planning and design aids. However, there is little empirical evidence that using this technology provides a significant performance impact. One factor limiting research on the impact of technology on planning and design is the manner in which this technology has been conceptualized for measuring usage behavior. This research develops a functional model of I/S planning and design support technology that distinguishes three general functional dimensions: Production Technology, Coordination Technology and Organizational Technology. An empirical analysis is used to test the robustness of the proposed model and its ability to discriminate among current design aids in a meaningful way. Implications for the use of this model in the study of I/S planning and design processes are discussed.
Men's health: How men understand the concept of health and how this understanding shapes actions

The purpose of this research was to explore how men understand the concept of health, and how this understanding shapes actions. This dissertation developed as a result of both working at a Community Health Centre, and having an awareness that in Australia, statistics indicate that men's health is steadily deteriorating. A theoretical framework implementing the concepts of health, socialisation (masculinity) and cognition was utilised to guide the research. The research involved interviewing six male participants, who were university students ranging in age from 20 to 40 years. Participants were questioned regarding their perceptions of health and health management. Interview transcripts were analysed utilising a symbolic interaction perspective, where the five major themes of family I friends, personal experience, exercise, food, and seriousness of concern emerged. The results of the study indicate that when analysing the five major themes, the concepts of health and cognition impacted upon ptl_rticipants responses to health concerns and health management. In contrast, men did not explicitly discuss masculinity as impacting upon health concerns or health management. Future research to explore how men understand the concept ofhealth and how this understanding shapes actions could focus upon age differences, gender comparisons, or individuals not studying at university.
Combining labeled and unlabeled data with co-training

We consider the problem of using a large unlabeled sample to boost performance of a learning algorit,hrn when only a small set of labeled examples is available. In particular, we consider a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views. For example, the description of a web page can be partitioned into the words occurring on that page, and the words occurring in hyperlinks t,hat point to that page. We assume that either view of the example would be sufficient for learning if we had enough labeled data, but our goal is to use both views together to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples. Specifically, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each algorithm’s predictions on new unlabeled examples are used to enlarge the training set of the other. Our goal in this paper is to provide a PAC-style analysis for this setting, and, more broadly, a PAC-style framework for the general problem of learning from both labeled and unlabeled data. We also provide empirical results on real web-page data indicating that this use of unlabeled examples can lead to significant improvement of hypotheses in practice. *This research was supported in part by the DARPA HPKB program under contract F30602-97-1-0215 and by NSF National Young investigator grant CCR-9357793. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. TO copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. COLT 98 Madison WI USA Copyright ACM 1998 l-58113-057--0/98/ 7. %5.00 92 Tom Mitchell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 mitchell+@cs.cmu. edu