text:
The Material Copper
Continuous Cast Rod
Oxygen-free Copper Rod
Bars & Profiles
ISO and Policy Statements
Dirk Vandenberghe becomes Managing Director Aurubis Olen and Aurubis Beerse as of January 1, 2023
Inge Hofkens joins the Aurubis Executive Board and strengthens the focus on the recycling business
Press Release: Aurubis signs renewable power deal with green energy producer Eneco
Thierry Breton (EU Commissioner for Internal Trade) visits Aurubis Olen
Helden Voor Helden, Aurubis Olen and Thomas More join forces in special cycling project
Aurubis builds facility to recycle more nickel and copper in Belgium
After takeover, Metallo now also gets new name: Aurubis Beerse
Bobbejaanlandtrail: Ready, set, go!
Aurubis Car Free Day
Working at Aurubis
Laurens Verstraelen (29), Junior Manager IT Infrastructure
Theo Van Santvoort (51), Technical Operator at Contirod
Kevin Vranken (38), Purchaser
Els Wils (41), Warehouse Worker
Paul Verwimp (64), Team leader
Sarah Bakelants (31), Payroll Coordinator
Daan Goossens (19), Electromechanic
Michiel Coolkens (23), Technical Operator
Home Career Stories Stories Sarah Bakelants (31), Payroll Coördinator
"There is a very open culture at Aurubis and that creates a positive atmosphere."
Olen | Friday, June 3, 2022
Sarah has been working for our HR department since the summer of 2019. As Payroll Coordinator, she and her two direct colleagues are responsible for the monthly payroll processing of our 640 colleagues.
Sarah handles monthly payroll processing for Olen colleagues.
As Payroll Coordinator, Sarah is part of the larger HR team.
"I love being involved with numbers as well as people."
"On the site itself, where we cast the copper, so nice to see!"
Ice creams for everyone in the summer: Sarah appreciates those extras.
Sarah Bakelants
HR Payroll Coördinator
Works for Aurubis Olen since 2019
What exactly does your position as Payroll Coordinator entail?
Together with my two direct colleagues, I am responsible for the payroll processing of 640 colleagues. We take care of the payment of the salaries and wages of our blue-collar workers, white-collar workers and managers, without the intervention of a social secretariat. In other words, we make sure that our colleagues receive their money on time! But don't be fooled: it's not a simple press of a button. Sometimes it's a true challenge, because before you can actually do the payment, all sorts of checks have to be carried out, such as checking whether all absences and arrivals are in the system. In addition, we also take care of the meal vouchers, we keep the systems up-to-date when entering or leaving service, we are the contact point for questions from colleagues about short leaves, and so on ... so much more than just the payment itself!
You are part of the HR team. How would you describe HR?
HR is as they say "the personnel department". In the larger HR team, I work with the colleagues from training, recruitment, benefits, communication and reception. We respond well to each other and can always turn to each other. We are located in a landscape office, so we can also consult each other smoothly. Together with our supervisors, we have weekly team meetings. There is a very open culture in our company, we all address each other by our first names, managers are very accessible and that creates a positive atmosphere.
What appeals to you in your job?
I love working with numbers and with people, that combination is absolutely wonderful! And with more than 600 colleagues from different departments and with different profiles, across generations, you have plenty of variety in any case, because every story is different. That diversity fascinates me.
And of course I also love working in Olen: I live barely 1 kilometer from Aurubis, so I can get to work by bike in no time. No traffic jams or long travel times! And thanks to my flexible hours I can choose my own hours.
You take care of the payment of 640 colleagues. To what extent do you know everyone?
I know everyone by name! (laughs) No, I certainly do not know everyone. It's when I first started working here that I came to realise that so many people I know actually work here. I would run into someone and it would literally be like "Oh, do you work here too?". In principle, I see every colleague on his or her first day, but it is getting difficult to keep remembering all the faces: this year, 40 new colleagues have already joined our Aurubis Olen team. Very nice to witness this growth so closely!
What would you rather see differently?
Working at Aurubis Olen is fascinating, because it is a large company with plenty of room for new projects and ideas are launched all the time. I may be from Olen, but I was still surprised by how big the company is! And that's why I sometimes also regret that I have an office job and therefore don't get to see much of the company. I know all the names, but who does what in which department? On the site itself, that's where everything happens and where we cast and produce the copper: so nice to see!
What opportunities do you get to develop further?
I think Aurubis gives a lot of opportunities to its people. For example, there is a lot of focus on training, both internal and external. My manager actively encourages me to learn and to grasp challenges with both hands. And I do! Aurubis offers many growth chances, not only in terms of training. You are also encouraged to come up with improvements yourself and you really get the chance to work things out. A few years ago, for example, I introduced Doccle: a switch from paper payslips to an online platform. It's very handy, because thanks to this app our employees have all their pay slips in one place online. And it's good for the environment, so that makes it even better!
How would you describe Aurubis as an employer?
As an employer who really takes good care of his people. I personally find it important to commit to a company that is good to its people. At Aurubis, you get comprehensive hospitalization insurance as standard, for example. And you have the option of including your family members at an advantageous rate. There is also a group insurance, so at Aurubis they also think about your future. A nice bonus for when you retire, right? Every week fresh fruit is provided. Last week, there was a sumptuous dinner for the celebrated colleagues of the honorary marks of labor. And at the end of June, the company will organize a pop-up Aurubis Summer Bar where we can gather and enjoy with all colleagues. I'm already looking forward to it!
How do you see your career evolving within Aurubis Olen?
I see myself continuing to grow in my role as payroll coordinator. I am still learning every day and look forward to developing further. In a few years' time, my colleague Marijke will be enjoying her well-deserved retirement, so gradually I will be trained further to take over her job.
To conclude: There is a vacancy for HR Payroll Officer: what is your advice for potential candidates?
Definitely apply, because you will be joining a very nice team! (laughs) Aurubis is a good employer where you regularly get something extra and it is a fun and challenging job with lots of variety.
HR is hiring a Payroll Officer. Will you soon be Sarah's colleague?
Apply quickly and easily online. We would be happy to receive you for tests and an introductory meeting. We look forward to seeing you soon!
Kathleen Thys
Watertorenstraat 35, 2250 Olen
Send e-mail k.thys@aurubis.com
© 2023 Aurubis Olen nv


text:
Suresh Madhavan public CV
Suresh Madhavan, MBA, PhD, FAPhA
Professor, Pharmacotherapy
Dean, UNT System College of Pharmacy
EmailSuresh.Madhavanunthscedu
Sponsored Projects (2)
Access to services, quality of care, and family impact for children with autism, other developmental disabilities, and other mental health conditions
Vohra, R., Madhavan, S., Sambamoorthi, U. & St Peter, C., 1 Oct 2014, In: Autism. 18, 7, p. 815-826 12 p.
Developmental Disabilities 100%
access to services 93%
Quality of care 86%
Autistic Disorder 79%
Adequacy of FDA'S prescription to over-the-counter switch criteria in physician evaluation of proposed switches of drug products
Madhavan, S. S., 1 Jan 1994, In: Therapeutic Innovation & Regulatory Science. 28, 2, p. 579-592 14 p.
drug product 100%
United States Food and Drug Administration 71%
medication 61%
Prescriptions 60%
A discrete choice experiment to elicit patient willingness to pay for attributes of treatment-induced symptom relief in comorbid insomnia
Roy, A. N., Suresh Madhavan, S. & Lloyd, A., 2015, Managed Care, 2015, APR, p. 42-50 9 p.
experiment 46%
drug 39%
Alternative Decision Analysis Modeling in the Economic Evaluation of Tumor Necrosis Factor Inhibitors for Rheumatoid Arthritis
Kamal, K. M., Miller, L. A., Kavookjian, J. & Madhavan, S., Aug 2006, In: Seminars in Arthritis and Rheumatism. 36, 1, p. 50-60 11 p.
Tumor Necrosis Factor Inhibitors 100%
Decision Support Techniques 97%
Economic Models 38%
A Multidimensional Analysis of Physicians' Perceptions of Rx-to-OTC Switched Drug Products
Madhavan, S. & Gore, P., 1 Jan 1994, In: Journal of Pharmaceutical Marketing & Management. 8, 3, p. 3-28 26 p.
Drugs 100%
Physicians 85%
Prescribing 27%
Prescription 21%
Safety 20%
An explanatory model for state medicaid per capita prescription drug expenditures
Roy, S. & Madhavan, S. S., 1 Oct 2012, In: Social Work in Public Health. 27, 6, p. 537-553 17 p.
Prescription Drugs 100%
Medicaid 94%
Health Expenditures 86%
expenditures 74%
A population-based study comparing HRQoL among breast, prostate, and colorectal cancer survivors to propensity score matched controls, by cancer type, and gender
LeMasters, T., Madhavan, S., Sambamoorthi, U. & Kurian, S., Oct 2013, In: Psycho-Oncology. 22, 10, p. 2270-2282 13 p.
Cancer Survivors 100%
Propensity Score 97%
Colorectal Neoplasms 72%
Appropriateness of switches from prescription to over-the-counter drug status: Influence of demographic and practice characteristics on pharmacist decision making
Madhavan, S., 1990, In: Journal of Pharmacy Technology. 6, 6, p. 239-242 4 p.
Are obese women more likely to participate in a mobile mammography program?
Atkins, E., Madhavan, S., Lemasters, T., Vyas, A., Gainor, S. J. & Remick, S., Apr 2013, In: Journal of Community Health. 38, 2, p. 338-348 11 p.
preventive care 100%
Mammography 99%
comorbidity 85%
Preventive Medicine 80%
Assessing managed care's role in promoting preventive care
Amonkar, M. M., Madhavan, S., Rosenbluth, S. A., Odedina, F. T. & Simon, K. J., 2000, In: Journal of Community Health. 25, 3, p. 225-240 16 p.
Managed Care Programs 80%
Utilization Review 79%
managed care 79%
Assessment of health-related quality of life among primary caregivers of children with Autism Spectrum Disorders
Khanna, R., Madhavan, S. S., Smith, M. J., Patrick, J. H., Tworek, C. & Becker-Cottrill, B., 1 Sep 2011, In: Journal of Autism and Developmental Disorders. 41, 9, p. 1214-1227 14 p.
caregiver 82%
Caregivers 82%
Assessment of urologists' practice preferences for the management of benign prostatic hyperplasia in the Veterans Administration program
Fernandes, A. W., Amonkar, M. M., Madhavan, S. S. & Nseyo, U. O., 2001, In: Current Therapeutic Research - Clinical and Experimental. 62, 2, p. 129-141 13 p.
United States Department of Veterans Affairs 100%
Urologists 94%
Prostatic Hyperplasia 89%
Routine Diagnostic Tests 19%
Association between Metformin Use and Cancer Stage at Diagnosis among Elderly Medicare Beneficiaries with Preexisting Type 2 Diabetes Mellitus and Incident Prostate Cancer
Raval, A. D., Mattes, M. D., Madhavan, S., Pan, X., Wei, W. & Sambamoorthi, U., 2016, In: Journal of Diabetes Research. 2016, 2656814.
Metformin 100%
Medicare 95%
Type 2 Diabetes Mellitus 69%
Elderly 62%
Association between persistence with mammography screening and stage at diagnosis among elderly women diagnosed with breast cancer
Vyas, A., Madhavan, S. & Sambamoorthi, U., 25 Nov 2014, In: Breast Cancer Research and Treatment. 148, 3, p. 645-654 10 p.
Mammography 100%
Medicare 9%
Datasets 7%
Association between types of chronic conditions and cancer stage at diagnosis among elderly medicare beneficiaries with prostate cancer
Raval, A. D., Madhavan, S., Mattes, M. D. & Sambamoorthi, U., 1 Dec 2016, In: Population Health Management. 19, 6, p. 445-453 9 p.
chronic condition 100%
Association of Entrepreneurial Traits With Interest in Becoming a Pharmacist Provider Among Student Pharmacists
Nili, M., Shaikh, N. F., Dwibedi, N. & Madhavan, S. S., Aug 2021, In: Journal of Pharmacy Practice. 34, 4, p. 547-552 6 p.
Pharmacists 100%
Motivation 24%
Analysis of Variance 15%
Association of medication adherence with workplace productivity and health-related quality of life in patients with asthma
Joshi, A. V., Madhavan, S. S., Ambegaonkar, A., Smith, M., Scott, V. & Dedhia, H., 1 Aug 2006, In: Journal of Asthma. 43, 7, p. 521-526 6 p.
Medication Adherence 100%
Workplace 87%
Association of the severity of diabetes-related complications with stage of breast cancer at diagnosis among elderly women with pre-existing diabetes
Alenzi, E. O., Madhavan, S. S. & Tan, X., 1 Jan 2018, In: Breast Cancer Research and Treatment. 167, 1, p. 183-193 11 p.
Diabetes Complications 100%
Early Detection of Cancer 25%
Associations among cardiometabolic risk factor clustering, weight status, and cardiovascular disease in an appalachian population
Blake, K. B., Shankar, A., Madhavan, S. & Ducatman, A., Dec 2010, In: Journal of Clinical Hypertension. 12, 12, p. 964-972 9 p.
Cluster Analysis 100%
Asthma-chronic obstructive pulmonary disease overlap and cost-related medication non-adherence among older adults in the United States
Nili, M., Adelman, M., Madhavan, S. S., LeMasters, T., Dwibedi, N. & Sambamoorthi, U., 2020, (Accepted/In press) In: Journal of Asthma.
Cost of Illness 100%
Medication Adherence 93%
Chronic Obstructive Pulmonary Disease 75%
Attitudes of Pharmacists Toward Rx-to-OTC Switches and Their Effect on Pharmacists' Overall Judgment of Switch Appropriateness
Madhavan, S. & Schondelmeyer, S. W., 1 Jan 1990, In: Journal of Pharmaceutical Marketing & Management. 4, 4, p. 3-25 23 p.
Appropriateness 88%
Discriminant Analysis 22%
Factor Scores 19%
Discriminant 17%
Barriers and facilitators to pharmaceutical care in rural community practice
Venkataraman, K., Madhavan, S. S. & Bone, P., 1 Dec 1997, In: Journal of Social and Administrative Pharmacy. 14, 4, p. 208-219 12 p.
Pharmaceutical Services 100%
Psychological Practice 82%
Pharmacists 28%
Barriers and facilitators to providing common preventive screening services in managed care settings
Amonkar, M. M., Madhavan, S., Rosenbluth, S. A. & Simon, K. J., 1999, In: Journal of Community Health. 24, 3, p. 229-247 19 p.
Managed Care Programs 100%
Organizations 40%
Papanicolaou Test 34%
utilization 29%
Bisphenol A and metabolic syndrome: Results from NHANES
Teppala, S., Madhavan, S. & Shankar, A., 2012, In: International Journal of Endocrinology. 2012, 598180.
bisphenol A 100%
Nutrition Surveys 84%
Creatinine 8%
Alcohols 8%
Breast cancer screening, incidence, and mortality in West Virginia.
Khanna, R., Bhanegaonkar, A., Colsher, P., Madhavan, S. S. & Halverson, J., Oct 2009, In: The West Virginia medical journal. 105 Spec No, p. 24-32 9 p.
Early Detection of Cancer 100%
Burden of emergency and non-emergency colorectal cancer surgeries in west virginia and the USA
Shah, N. A., Halverson, J. & Madhavan, S., Mar 2013, In: Journal of Gastrointestinal Cancer. 44, 1, p. 46-53 8 p.
Colorectal Surgery 100%
Emergencies 63%
Hospital Charges 31%
Length of Stay 26%
Cancer type and risk of newly diagnosed depression among elderly medicare beneficiaries with incident breast, colorectal, and prostate cancers
Alwhaibi, M., Sambamoorthi, U., Madhavan, S., Bias, T., Kelly, K. & Walkup, J., 1 Jan 2017, In: JNCCN Journal of the National Comprehensive Cancer Network. 15, 1, p. 46-55 10 p.
Medicare 100%
Caregiver Burdens and Preventive Dental Care for Children with Autism Spectrum Disorder, Developmental Disability and/or Mental Health Conditions: National Survey of CSHCN, 2009–2010
Wiener, R. C., Vohra, R., Sambamoorthi, U. & Madhavan, S. S., 1 Dec 2016, In: Maternal and Child Health Journal. 20, 12, p. 2573-2580 8 p.
Dental Care for Children 100%
dental care 85%
developmental disorder 84%
Developmental Disabilities 78%
Child's autism severity: effect on West Virginia caregiver satisfaction with school services
Vohra, R., Madhavan, S., Khanna, R. & Becker-Cottrill, B., 2014, In: The West Virginia medical journal. 110, 5, p. 32-39 8 p.
Autistic Disorder 100%
Language Therapy 17%
Speech Therapy 16%
Cluster analysis of state Medicaid prescription drug benefit programs based on potential determinants of per capita drug expenditure
Roy, S. & Madhavan, S. S., Mar 2009, In: Research in Social and Administrative Pharmacy. 5, 1, p. 51-62 12 p.
Prescription Drug 100%
Prescription Drugs 69%
Cluster Analysis 53%
Comorbidity prevalence, healthcare utilization, and expenditures of Medicaid enrolled adults with autism spectrum disorders
Vohra, R., Madhavan, S. & Sambamoorthi, U., 1 Nov 2017, In: Autism. 21, 8, p. 995-1009 15 p.
comorbidity 100%
Autism Spectrum Disorder 84%
Comparing adherence to and persistence with antipsychotic therapy among patients with bipolar disorder
Hassan, M., Madhavan, S. S., Kalsekar, I. D., Makela, E. H., Rajagopalan, K., Islam, S., Kavookjian, J. & Miller, L. A., 1 Nov 2007, In: Annals of Pharmacotherapy. 41, 11, p. 1812-1818 7 p.
Bipolar Disorder 100%
Antipsychotic Agents 94%
Risperidone 67%
Quetiapine Fumarate 36%
Olanzapine 34%
Compliance rates and predictors of cancer screening recommendations among Appalachian women
Amonkar, M. M. & Madhavan, S., 1 Nov 2002, In: Journal of Health Care for the Poor and Underserved. 13, 4, p. 443-460 18 p.
Appalachian Region 100%
preventive care 89%
Uterine Cervical Neoplasms 57%
Consumer involvement in nonprescription medicine purchase decisions
Gore, P., Madhavan, S., McClung, G. & Riley, D., 1 Jan 1994, In: Journal of Health Care Marketing. 14, 2, p. 16-23 8 p.
Nonprescription Drugs 100%
Consumer Involvement 93%
Community Participation 92%
Purchase Decision 73%
Consumers' preference and willingness to pay for pharmacist counselling for non‐prescription medicines
Gore, P. R. & Madhavan, S. S., 1 Jan 1994, In: Journal of Clinical Pharmacy and Therapeutics. 19, 1, p. 17-25 9 p.
Consumer Behavior 100%
Counseling 59%
Credibility of the sources of information for non-prescription medicines
Gore, P. & Madhavan, S. S., 1 Dec 1993, In: Journal of Social and Administrative Pharmacy. 10, 3, p. 109-122 14 p.
Semantic Differential 21%
Depression in patients with type 2 diabetes: Impact on adherence to oral hypoglycemic agents
Kalsekar, I. D., Madhavan, S. S., Amonkar, M. M., Makela, E. H., Scott, V. G., Douglas, S. M. & Elswick, B. L. M., 1 Apr 2006, In: Annals of Pharmacotherapy. 40, 4, p. 605-611 7 p.
Hypoglycemic Agents 100%
Depression treatment among elderly medicare beneficiaries with incident cases of cancer and newly diagnosed depression
Alwhaibi, M., Madhavan, S., Bias, T., Kelly, K., Walkup, J. & Sambamoorthi, U., 1 May 2017, In: Psychiatric Services. 68, 5, p. 482-489 8 p.
Cancer Survivors 37%
Depression treatment and healthcare expenditures among elderly Medicare beneficiaries with newly diagnosed depression and incident breast, colorectal, or prostate cancer
Alwhaibi, M., Sambamoorthi, U., Madhavan, S. & Walkup, J. T., Dec 2017, In: Psycho-Oncology. 26, 12, p. 2215-2223 9 p.
Determinants of adult influenza and pneumonia immunization rates
Kamal, K. M., Madhavan, S. S. & Amonkar, M. M., 2003, In: Journal of the American Pharmacists Association. 43, 3, p. 403-411 9 p.
Human Influenza 100%
Immunization 91%
White 91%
Pneumonia 87%
Determining the entrepreneurial and intrapreneurial intentions of student pharmacists in iran
Fashami, F. M., Nili, M., Farahani, A. V., Shaikh, N., Dwibedi, N. & Suresh Madhavan, S., 2021, In: American journal of pharmaceutical education. 85, 2, p. 113-122 10 p., 8080.
pharmacist 100%
Iran 87%
Intention 83%
achievement motivation 63%
Differences in hospital length-of-stay, charges, and mortality in congestive heart failure patients.
Joshi, A. V., D'Souza, A. O. & Madhavan, S. S., 2004, In: Congestive heart failure (Greenwich, Conn.). 10, 2, p. 76-84 9 p.
Length of Stay 100%
Hospitalization 41%
Differences in medicare expenditures between appalachian and nationally representative cohorts of elderly women with breast cancer: An application of decomposition technique
Vyas, A., Madhavan, S. S. & Sambamoorthi, U., 1 May 2017, In: JNCCN Journal of the National Comprehensive Cancer Network. 15, 5, p. 578-587 10 p.
Disparities in Lung Cancer Care and Outcomes among Elderly in a Medically Underserved State Population - A Cancer Registry-Linked Database Study
Nadpara, P. A., Madhavan, S. S. & Tworek, C., 1 Apr 2016, In: Population Health Management. 19, 2, p. 109-119 11 p.
Medically Underserved Area 100%
Lung Neoplasms 49%
Registries 49%
Disparities in the Initial Local Treatment of Older Women with Early-Stage Breast Cancer: A Population-Based Study
Lemasters, T. J., Madhavan, S. S., Sambamoorthi, U. & Vyas, A. M., Jul 2017, In: Journal of Women's Health. 26, 7, p. 735-744 10 p.
Segmental Mastectomy 100%
Radiotherapy 38%
Mastectomy 36%
Do appalachian women attending a mobile mammography program differ from those visiting a stationary mammography facility?
Vyas, A., Madhavan, S., Kelly, K., Metzger, A., Schreiman, J. & Remick, S., Aug 2013, In: Journal of Community Health. 38, 4, p. 698-706 9 p.
health insurance coverage 15%
Access to Health Care 14%
medical care 11%
Economic Burden of Asthma-Chronic Obstructive Pulmonary Disease Overlap among Older Adults in the United States
Nili, M., Dwibedi, N., Adelman, M., LeMasters, T., Madhavan, S. S. & Sambamoorthi, U., 2021, In: COPD: Journal of Chronic Obstructive Pulmonary Disease. 18, 3, p. 357-366 10 p.
Economic Burden of Treatment-Resistant Depression among Adults with Chronic Non-Cancer Pain Conditions and Major Depressive Disorder in the US
Shah, D., Allen, L., Zheng, W., Madhavan, S. S., Wei, W., LeMasters, T. J. & Sambamoorthi, U., Jun 2021, In: PharmacoEconomics. 39, 6, p. 639-651 13 p.
Treatment-Resistant Depressive Disorder 100%
Major Depressive Disorder 77%
Emergency Department Use Among Adults with Autism Spectrum Disorders (ASD)
Vohra, R., Madhavan, S. & Sambamoorthi, U., 1 Apr 2016, In: Journal of Autism and Developmental Disorders. 46, 4, p. 1441-1454 14 p.
Hospital Emergency Service 77%
Mental Disorders 9%
Estimation of a valuation function for a diabetes mellitus-specific preference-based measure of health: The diabetes utility index®
Sundaram, M., Smith, M. J., Revicki, D. A., Miller, L. A., Madhavan, S. & Hobbs, G., 2010, In: PharmacoEconomics. 28, 3, p. 201-216 16 p.
chronic illness 100%
Interviews 18%

redpajama_set_name: RedPajamaCommonCrawl


Virtually anything of any importance that we see in the MSM cannot be believed or trusted. "9/11″ HAD to have been an 'Inside Job", because the complexity and scale of the devastation could not have been SO successful without serious co-ordinated planning. The 'Barons' are 'farming' the 'Serfs', and our rulers are getting away with blue bloody murder. Ain't Freedom and Democracy Grand???


With a varied choice of festivities, concerts and other performances we would like to invite you to visit our palaces and castles. You are very welcome!
Unfortunately we can offer you these disclosures in German only.


Need Entertainment in the Wadhurst area of England. Garston Entertainment also supply Corporate Entertainment, Live Bands and Live Music for Corporate Events Wadhurst, Soul Bands Wadhurst. Garston Entertainment are a Live Entertainment Booking Agency and can supply Live Entertainment Hire for Corporate Events, Parties, Functions, Weddings and Venues in for Wadhurst England.


Standing on the sidewalk with your three-year-old, she pulls away from you and starts running toward a busy street. To her, it is a game, but to you, it is a life-threatening situation. You grab her just as she heads into the street and deliver a swift smack on the bum with an admonition, "No running in the street!" Startled, she stops. Have you just committed a crime?
People disagree on whether spanking is good or bad. Many European countries have banned spanking outright. The United Nations Convention on the Rights of the Child has outlawed all physical discipline, even within families. However, the vast majority of American parents spank their young children at some point, and most Americans believe that spanking is a legitimate form of discipline. Although less common than it used to be, spanking is pervasive in our society. However, anyone who spanks a child should know that sometimes spanking has criminal consequences.
While all states currently permit spanking at home, some states have outlawed spanking is schools and daycares, and most states discourage or outlaw spanking in foster homes and institutions for children.
For more information, see Criminal Consequences of Spanking in Schools.
Spanking, also called corporal punishment, is a discipline method in which a person inflicts pain on a child without inflicting injury and with the intent to modify the child's behavior. Forms of corporal punishment include hitting a child's bottom, slapping, grabbing, shoving, or hitting a child with a belt or paddle.
For example, spanking a six-year-old child twice on the bottom, where the spanking causes no injury and only fleeting discomfort, would probably be considered lawful under Georgia's and California's laws, as well as in other states. However, striking the same six-year-old numerous times with a belt, causing lacerations, bruising, and pain that lasted for days, could be considered child abuse.
it's purpose is something other than correcting inappropriate or dangerous behavior.
Causing injury to your child can also result in dependency proceedings, where the state petitions the court to limit or terminate your parental rights. These cases typically arise when a person makes a complaint to Child Protective Services (CPS). Many people who work with children, such as teachers and doctors, are required to report any suspected child abuse. CPS then launches an investigation to determine if the child has been abused or is in danger. If so, the state may file a petition to have the child declared a dependent of the court and provide continued monitoring and services to the family, or in very serious cases, remove the child from the home.
If you suspect that a child is being abused, contact the Childhelp National Child Abuse Hotline at 1-800-422-4453.
While parents have a limited right to use force against their own children for discipline, a person who is not a child's parent or caregiver does not necessarily have any right to use force against a child. What could be considered lawful spanking if done to your own child might be considered battery if done to your child's playmate or a neighbor child.
a propensity for violence towards others.
other factors related to discipline, including its frequency and severity, are more important than whether spanking is used.
A conviction for assault, battery, or child abuse can have serious consequences. Depending on the circumstances, a conviction can result in time in prison or jail, a fine, probation, and counseling.
If you are charged with a crime as a result of spanking your child, you should talk to a local criminal defense attorney. An attorney can tell you what to expect in court, based on the charges, the law in your state, and the local judge and prosecutor. An attorney can help you understand your options and present the strongest defense in your case so that you can achieve the best possible outcome.


Bohemian Rhapsody is a formulaic band movie that struggles to get Queen's story off the ground in an original and captivating way, but the Live Aid finale almost makes the whole thing worthwhile – it's that and Rami Malek's stupendous performance that save this film.
Directed by Bryan Singer (X-Men) and starring Rami Malek as frontman Freddy Mercury, Bohemian Rhapsody chronicles the years between Queen's inception and their iconic Live Aid performance in 1985.
It's a film that tries to focus on Freddy Mercury himself, but never really feels idiosyncratic enough to warrant the 'biopic' label, nor does it cover much of the other band member's lives either. Everybody gets only minimal coverage here. We know that Brian May, John Deacon and Roger Taylor were there, and that's about it.
Even the performances seem to be a bit off and that's not necessarily the cast which includes Gwilym Lee as Brian May, Ben Hardy as Roger Taylor and Joseph Mazzello as John Deacon (yes, that's the kid from Jurassic Park), but rather the script which rarely gives these guys anything to work with. Shame because they were anything but normal in real life.
It's just so predictable. There's even a scene where hot shot EMI executive played by Mike Myers (yes, he's still around) says that combining opera with their signature rock music and thinking outside of the square was risky. Formula is good and it 'works'. How ironic because that's exactly what this film suffers from. An overabundance of formula.
Queen were such an extraordinary and indefinable band so it feels like a bit of a cop-out that this is what they came up with. Director Singer, who was fired at the tale-end of filming due to his unusual and reclusive behaviour, would rather play it safe and standard than get into the messy details, especially where Freddy is concerned.
At least Freddy's arc is bit more noticeable than the other band members. We watch him go from baggage handler at London's Heathrow airport to the flaming falsetto queen that we all know and love. There's some time spent on Freddy's personal life outside of the band, and how he was manipulated by record producers, managers and more, and there's also a lot of time spent on his complicated relationship with wife Mary Austin, played by Sing Street breakout star Lucy Boynton. It's interesting enough, but those expecting a more in-depth look at his final years, will be sorely disappointed.
Why didn't we see more of Freddy's life as a gay man? Why didn't we see more of his struggle with AIDS? As disappointing as this is, perhaps it was too sensitive a topic for family and surviving band members to see? It just seems odd that an out gay director like Singer would choose to show so little of that side of his life, compared to everything else. Was it new director Dexter Fletcher that made it more 'family friendly'? Either way, there's a lot that happened behind the scenes here, and I doubt we will ever really understand what happened.
However, it's not all like that. Seeing the band's iconic Live Aid performance in IMAX almost makes you want to get up and dance in your seat. It's a real spectacle and 100% worth seeing on the big screen, even if you're not a Queen fan. Rami Malek has an unbelievable stage and screen presence, and it's a shame that his performance is all that's really holding this film up.
Bohemian Rhapsody 'wants to break free' so bad. See what I did there? 😉 But it just doesn't. Queen fans will love it though. There's enough music in there to keep them satisfied. I'd personally go and see it just for the Live Aid scene. It was wonderful.


Future planetary exploration missions will require the landing of larger payloads more softly and precisely than recently performed. The paper considers several future missions, the landing requirements produce different thrust level needed for the landing vehicle but are similar in many other repects. To fulfil the mission needs of these planetary lander studies, preferably with a common chemical propulsion subsystem concept for all these studies to use as much commonalities as possible, a trade- off has been performed comparing the most promising concepts. These are the concept of a throttable turbo pump engine, of main and assist engines and the clustered/plug nozzle concept. There the propulsion concept of a clustered/plug nozzle is proposed. A discussion is given which shows the advantages of this concept. One of its major advantages is the scalability to different mission, spacecraft mass und thrust requirements. Once the clustered/plug nozzle concept is developed it can easily be adapted to different thrust needs. Therefore just the thruster forming the primary nozzle of the plug nozzle has to be exchanged; the conceptual layout of the propulsion subsystem remains unchanged. The wide variety of the Astrium in-house monopropellant and bi-propellant thruster portfolio supports this scalability. Additional benefits of this concept like stepwise thrust variation or the possibility to incorporate thrust vector steering make this concept even more attractive. The develop- ment risk and the costs of the proposed clustered/plug nozzle concept are expected to be significantly lower than that of a dedicated single main engine.


Nogales, Mexico. Raphael, 30, is originally from Mexico. He's waiting in front of the El Comedor aid center for deported migrants in a border town in Nogales, Sonora. He lived in the U.S. for fifteen years before being deported. His brother is a U.S. citizen but Raphael did not complete his citizenship process when he first came to United States. He has no formal education, but he has been working in different jobs throughout his life. "I got into trouble" is all Raphael says about how he ended up in immigration custody. He was detained for 1.5 months before being deported. He has a 4-year-old son and a girlfriend living in Phoenix, Arizona. According to Human Rights Watch report, an estimated 17 million people in the U.S. have at least one family member who is in the country illegally. Immigrants, once deported, have almost no way to return legally, whatever their family ties to the United States.
Raphael has lived in the US for 15 years. Raphael lived in the US for 15 years. Raphael had lived in the US for 15 years. What is the correct tense to describe a deported life? He may have returned to the US, in which case his US life proceeds to be lived. This present life punctuated by a pause, a subtraction of some months and the forcible movement of thousands of miles from New York to Arizona to Nogales, Mexico. He may be in Mexico, still or again.
Raphael lived in the US for fifteen years before being deported to Nogales, Mexico. Fifteen years of working, loving, fathering is closed and made past. The closure of past tense places Raphael's US life beyond return; he has been dispossessed of this time and that life.
Raphael had lived in the US for 15 years—this tense demands events to follow. Raphael had lived in the US for 15 years before detention, deportation, and return to his hometown. In this tense, Raphael's past is an active antecedent for events following, for the present and a future he seeks to reclaim.
Nogales, Arizona. A landscape view of the border wall from the Nogales, Arizona side.
Elizabeth Povinelli argues that tense, the grammar of time, organizes the relationship between the time of narration and the act of narrating. It establishes a break, or a continuity. Tense pushes some pasts into nonmaterial memory and maintains other pasts as infrastructure for the present and future. For Povinelli, tense is socially divided, and the ability to experience and enunciate time in particular ways is unevenly distributed. In particular, the grammatical marking of tense relegates some ways of being to a permanent past or defers them to an unreachable future. This uneven distribution of social time influences, in turn, the distribution of social belonging, abandonment, and durability. Depending on how one does it, narrating migrant time articulates im/possibilities for mobile humans to be and not be. That is, the potentiality of living life is, in part, arranged in the grammar of time.
And right now, migrant time is at a premium. In a highly privatized US immigration detention system, migrant time is commodified in "bed days," which Immigration and Customs Enforcement is obligated to fill. Here indefinite detention, combined with the Congressional mandate to fill 34,000 beds per day, transforms migrant time into an infinitely renewable resource. In this case, outsourcing detention to private corrections companies and county governments translates the deprivation of autonomous mobility into a priced, exchangeable good. Indefinite detention guarantees its infinite availability.
Nogales, Mexico. The El Comedor aid center for deported migrants serves meals and distributes clothing and personal care items, and refers people to Mexican government services. In 2011, it served over 45,000 meals. The Mexican government agency Grupo Beta operates nearby to give first aid for deported migrants. Many people arrive in Nogales, Sonora, from the U.S. with severely blistered feet, flu symptoms and dehydration.
Making people wait is an exercise of power. Writing on Ceuta, Spain's retained migrants, Andersson argues "their immediate future had rather been vacated for them while their past had been temporarily disowned." In addition, the political economy of migration control interlinks the "time of control" and the "time of migration:" "possibilities of anticipation, interception, and deferral opened up by compression and speed have led to precisely the opposite reality for those who are targeted: a world of slowness and stasis. One mobile assemblage—that of control—feeds off and perpetuates the increasing immobility of its necessary Other." Waiting can also be an act of defiance, a refusal, an occupation. In the US and elsewhere, some refuse to sign their own deportation papers, refusing their negative asylum decision and the inevitability of return. Those who are deported sometimes return to the very places from which they were expelled, sometimes within days.
Nogales, Mexico. The San Juan Bosco shelter for deported migrants gives people a place to stay for a maximum of three nights. In the women's dormitory, women previously unknown to each other are coming together to share crossing and detention experiences and to support each other in their difficult situation. Over the past 30 years approximately 1 million people have passed through the San Juan Bosco immigrant shelter. The shelter is mostly funded through private donations.
The families and children fleeing violence in Central America leave for a number of reasons, but most of all they flee insecurity of gang violence or domestic abuse. In the words of one US-based advocate, "they figure the risk is worth it if you could die in your home the next day anyway." In a diffuse, individualized and disaggregated way, people who seek family, refuge, or work in the United States perform their own risk analysis by weighing the visceral value of being with family and the price of coyotes against the opportunities to work off their debt as well as the risk of reprisal against family members who remain. These kinds of risks change the ways in which people relate to present and the future. It changes the monetary price people are willing to pay someone to help cross a militarized border, and it elevates people's tolerance of uncertainty during these journeys.
The migrant risk calculus is about claiming a liveable life, attempting to claim time that may or may not become a future. Detention-as-deterrence policies attempt to alter this calculus, to interrupt intimate relationships and life-making, to create a sense of inevitability of forced return. Deterrence policies appropriate migrant time as both collective spectacle and individual suffering, communicated through apprehension statistics and cautionary tales shared along informal networks of transnational migrant care.
People deported to Nogales can make use of day and night shelters, but time in each of them is limited. Shuttle buses drive people to day shelters in the morning, where they received two meals per day. The day shelter photographed here has an eight day limit. Post-deportation and nominally free after weeks in detention, people stated that they felt like "living dead," as if they lived in "no time" and "out of place." Even though they were freed from the confinement of detention, they were bereft of belongings, money, and energy. Many had been deported after tired journeys northward. They complained of swollen feet, and suffered after-effects of traveling in the desert. Their faces showed deep exhaustion. Most spoke of plans to cross again, and some planned to return to their home villages. For people like Raphael, returning home meant crossing again.
Nogales, Mexico. First morning after the deportation. After small breakfast (cinnamon milk with cookies) women are waiting for transportation to El Comedor. During the day they receive money from their families and loved ones and do little shopping for things like underwear and personal care items. Not all women stay over the second night. (From right to left) Amelia, Luisa, Susana, and Marta say that they will cross again the next night. Blisters on the feet, the skin on upper thighs rubbed to raw flesh, the wounds caused by desert plants are still fresh and make the re-crossing of the desert painful and uncomfortable. It is impossible to say for sure whether they crossed again or not. However, I never saw them again in San Juan Bosco shelter nor El Comedor.
These feelings of timelessness and displacement emerged, perhaps ironically, in places where their time was extremely limited. The three and eight day limits of subsistence-level humanitarian aid contrast sharply with the indefinite detention in privatized detention centers. The shelters provide enough food for biological reproduction: a few nights rest, enough to collect oneself, to pick up, make a plan, and move on. There is a gap between the biological life of humanitarian aid and lived lives, daily routines, homes spaces, and work. For deported people, this gap is the non-place, no-time of post-deportation. Despite describing it as a negative space surrounding their memories and plans, people do inhabit this gap. Its spatiotemporality is dramatically different, however, from the trajectories of movement and the tempos of normal life.
The U.S. surveillance cameras near the border wall, gazing over the fence in Nogales, Arizona. The U.S. has installed new surveillance technology, and by hired more border patrol agents. The number of border patrol agents has quadrupled since mid-1990's. In fiscal year 2014, US Border Patrol apprehended 486,651 people. With over 22,700 agents, Customs and Border Patrol is the largest federal law enforcement agency in the country.
While much is made of migration control's spaces—walls, detention centers, flows, containments—it is the duration of immobility, and the enforcement of particular arrangements of potentiality and futurity that produce migration's different temporal modes. There is the time of detention, the time of deportation, the time of traveling, the time of hiding, the time of arrival, the time of daily life. As migrants ride the crest of potentiality and risk, time acquires a different sensibility, and perhaps we do not have an appropriate grammar for it. For Povinelli, however, it is precisely in these new senses of time and place that new ways of being emerge, that the unspeakable and the unknowable might become recognizable. And in doing so, new politics emerge. Or not.


text:
\section{Introduction}
In real applications, many task-oriented dialogue systems are mostly based on Natural Language Understanding (NLU) to classify or match a user query into a known category and reply with a prepared answer. If we can discover as much new intents as possible, then chat robots will be able to answer many kinds of questions and will improve the user experience. To discover the intents, we need to group different samples with similar intents together through clustering techniques, and every cluster will be treated as a new potential intent. Meanwhile, the accuracy of clustering also matters for NLU modules, because clustered samples will be used for training a classification model or building distance-matching model. If a cluster contains too much noisy samples, the downstream NLU module may not recognize user intents correctly. Therefore, a well-performing chat robot depends on not only NLU abilities, but also some preparatory works like intent clustering.

Since intent discovery is critical for chat robots nowadays, there have been lots of works proposed in this specific field or in related fields. Early works mainly focus on unsupervised clustering, in which all samples will be treated as unlabeled for clustering. The most basic method for unsupervised clustering is the combination of a encoder model and a clustering model. In Natural Language Processing (NLP) tasks, the feature-extracting encoder can be language model such as BERT\cite{devlin2018bert} and SBERT\cite{reimers2019sentence}, and the clustering model can be machine learning methods such as K-Means++\cite{arthur2006k} and HDBSCAN\cite{campello2013density}. However, such methods separate the encoding step and clustering step, which cannot optimize the representation according to the clustering loss. To solve this problem, some early works use deep-learning-based clustering methods, such as DEC\cite{xie2016unsupervised} and DCN\cite{yang2017towards}, which associate representation and unsupervised clustering as a simultaneous optimization procedure and improve the final performance. In more recent researches, contrastive learning has been introduced to further improve the representations. In DeepCluster\cite{caron2018deep} and SwAV\cite{caron2020unsupervised}, contrastive learning as well as deep-learning-based clustering, greatly improve the representations of images for downstream tasks. In SCCL\cite{zhang2021supporting}, improved DEC\cite{xie2016unsupervised} with contrastive learning, has achieved ideal clustering performance for unsupervised text clustering.

However, in common scenarios, there will be few labelled samples of limited known intents available, and quite a lot of raw corpus waiting to be classified into known or unknown intents. Take our experience for example, when we are going to build a task-oriented chat robot, we will borrow some labeled corpus from another task (which contains some intents in common across different tasks), and will try to supplement new intents continuously. Unsupervised methods cannot benefit from these labeled samples and further improve the performance, therefore recently some researches have been work on semi-supervised models to utilize the limited supervised information. CDAC+\cite{lin2020discovering} uses labeled samples for pairwise similarities to guide the clustering process. DeepAligned\cite{zhang2021discovering} trains a better encoder through classification loss on labeled samples, and then iteratively train the encoder through pseudo labels produced by K-Means, which previously has achieved state-of-the-art results. Although these methods successfully utilize known intents, we think there is still space for improvements (for example, training of DeepAligned\cite{zhang2021discovering} lacks distance constrain which is more friendly for clustering, and this method still relies on K-Means for updating pseudo labels which is not robust as deep-learning-based methods).

In summary, there are two ways for improving the clustering, the first is utilizing labeled intents for better initial text representations, the second is building deep learning model for joint optimization for both representation and clustering. To solve these two problems, we propose Deep Contrastive Semi-supervised Clustering (DCSC). DCSC bases on BERT\cite{devlin2018bert} as backbone, and it is trained through a two-stage dual-task process. In stage one, DCSC is trained on labeled samples through Cross Entropy Loss and Supervised Contrastive Loss\cite{khosla2020supervised} for distance constrain, and is trained on unlabeled samples through Contrastive Loss as well. In stage two, we build a classifier head for to produce pseudo labels, and train DCSC using Cross Entropy Loss and Supervised Contrastive Loss\cite{khosla2020supervised} on samples with either ground truth labels or pseudo labels. We conduct experiments on two public datasets, Clinc\cite{larson2019evaluation} and Banking\cite{casanueva2020efficient}. To simulate the real situation and make our experiments comparable with previous works, we keep the same experiment settings as DeepAligned\cite{zhang2021discovering}, and evaluate models using Accuracy (ACC), Adjusted Rand Index (ARI), Normalized Mutual Information (NMI). Our experiments show DCSC has outperformed other methods with $10\%$ advantage at most under different experiment settings. Therefore, our contributions can be summarised as:

\begin{itemize}

\item Applying Unsupervised Contrastive Loss and Supervised Contrastive Loss\cite{khosla2020supervised} for unlabeled samples and labeled samples, which makes the text representation from backbone more friendly for clustering task.

\item Building a deep-learning-based clustering approach for semi-supervised tasks, which jointly optimize the clustering ability and representation ability.

\item Conducting comparative experiments on two public datasets, demonstrating that DCSC is well-performing and robust for text clustering under different experiment circumstances.

\end{itemize}

The rest of this paper is organized as follows. In Section 2, we introduce some previous related work which have inspired us. In Section 3, we discuss our proposed approach as a semi-supervised text clustering model. Then we conclude our experiments on public datasets in Section 4, and make the conclusion in Section 5.

\section{Related Work}
Our model DCSC mainly utilizes contrastive learning and deep clustering to achieve current performance. In this section, we are going to discuss some previous works that has inspired us from contrastive learning, deep clustering, and semi-supervised clustering.

\textbf{Contrastive Learning.}
To optimize representation in unsupervised way, contrastive learning augment samples for different views, and train the model to distinguish views of the same sample from a large batch. As shown SimCLR\cite{chen2020simple}, model pretrained with contrastive learning achieves accurate performance in downstream tasks. Meanwhile, SimCSE\cite{gao2021simcse} provides a simple but effective idea for data augmentation of NLP tasks when applying contrastive learning, it lets a sample propagate through backbone with dropout twice to get different embeddings and conduct contrastive learning on such outputs, and it achieves an average of $76.3\%$ Spearman's correlation respectively with BERT\cite{devlin2018bert} (base)
on standard semantic textual similarity (STS) tasks. Also, contrastive learning can also be extended to supervised tasks\cite{khosla2020supervised}, trying to pull the samples belonging to the same class together in embedding space, while push apart samples from different classes.

\textbf{Deep Clustering.}
Jointly optimizing representation and clustering through deep networks, will guide the representation to be more suitable for clustering space. Early works like DEC\cite{xie2016unsupervised} replace K-Means with deep networks and iteratively optimize networks. However, such deep learning methods may cause trivial solution because most instances might be assigned to the a single cluster. SwAV\cite{caron2020unsupervised} solve this as a optimal transport problem, it uses Sinkhorn-Knopp algorithm\cite{cuturi2013sinkhorn} to produce soft pseudo assignments and optimize the networks through backpropagation. Meanwhile, similar to contrastive learning, it uses a "swapped" prediction mechanism where the model is trained to make prediction for a view under the soft assignment from another view's representation, trying to align the representation of different views. Such training strategy make SwAV\cite{caron2020unsupervised} achieve $75.3\%$ top-1 accuracy on ImageNet with ResNet-50.

\textbf{Semi-Supervised Clustering.}
Recently, some researches has contributed to intent discovering with semi-supervised clustering. DeepAligned\cite{zhang2021discovering} proposes a two-stage training strategy, in which the backbone is firstly trained to classify labeled samples for better representation (supervised learning) , and then secondly trained to classify samples with pseudo labels produced by K-Means iteratively. This paper has also conducted experiments on Clinc\cite{larson2019evaluation} and Banking\cite{casanueva2020efficient}, and randomly chooses a fraction of intents as known ones. DeepAligned\cite{zhang2021discovering} has outperformed other methods across all experiment settings and become the state-of-the-art model at the time. Also, with the same experiment settings, SCL\cite{shen2021semi} has achieved better results recently mainly with the improvements from contrastive learning and better backbone (MPNet\cite{song2020mpnet}).

\section{Our Approach}
In this section, we are going to introduce Deep Contrastive Semi-supervised Clustering (DCSC), which is for discovering new intents from raw corpus. The training procedure of DCSC mainly includes two stages, warm up stage and clustering stage, which will be discussed in detail. The overall modeling procedure of DCSC is shown as Fig.\ref{fig1}.

\begin{figure*}[ht]
  \centering
  \includegraphics[width=\textwidth]{fig1.png}
  \caption{Training procedure of DCSC. In warm up stage, the model is trained on labeled samples using Cross Entropy Loss and Supervised Contrastive Loss\cite{khosla2020supervised}, while also trained on all samples from train set using traditional contrastive loss for even better representation. In clustering stage, the model is trained on samples with pseudo soft assignments produced by Sinkhorn-Knopp algorithm\cite{cuturi2013sinkhorn} using Cross Entropy Loss and Supervised Contrastive Loss\cite{khosla2020supervised}, while also trained on labeled samples as in warm up stage.}
  \label{fig1}
\end{figure*}

\subsection{Warm Up Stage}
Our backbone is a language model (such as BERT\cite{devlin2018bert} and MPNet\cite{song2020mpnet}). Based on the last hidden states of backbone, we use mean pooling to get an instance vector of hidden size \begin{math}D\end{math}, and then build one more dense layer to get the final representation. For data augmentation, we use the same strategy as SimCSE\cite{gao2021simcse}, make an instance propagate through the backbone with dropout twice to get different views. Considering a batch \begin{math}X=\left \{x_{1},x_{2},\cdots,x_{N} \right \}\end{math} with batch size \begin{math}N\end{math}, we can let it through the backbone and get two output representations (views), which are \begin{math}Z=\left \{z_{1},z_{2},\cdots,z_{N} \right \}\end{math} and \begin{math}Z'=\left \{z_{N+1},z_{N+2},\cdots,z_{2N}\right \}\end{math}, and corresponding index \begin{math}I=\left \{1,2,\cdots,2N\right \}\end{math} as well.

For supervised learning on labeled samples, with labels \begin{math}Y=\left \{y_{1},y_{2},\cdots,y_{N} \right \}\end{math}, we can calculate cross entropy loss \begin{math}\mathcal{L}_{ce}^{warm up}\end{math}:
\begin{equation}
\mathcal{L}_{ce}^{warm up}=-\frac{1}{N}{\textstyle\sum_{i=1}^{N}}\mathrm{log}\frac{\mathrm{exp}\left(w_{y_{i}}\cdot z_{i}\right)}{{\textstyle\sum_{j=1}^{K}\mathrm{exp}\left(w_{j}\cdot z_{i}\right)}},
\end{equation}
where \begin{math}K\end{math} is the number of known intents, \begin{math}W=\left \{w_{1},w_{2},\cdots,w_{K} \right \}\end{math} is the classifier weights with shape \begin{math}\left(K,D\right)\end{math}. This warm up step is the same as DeepAligned\cite{zhang2021discovering} (except DeepAligned doesn't augment samples for two views), but we think such classification task will not produce an ideal representation space for clustering. Therefore, we add another Supervised Contrastive Loss\cite{khosla2020supervised} to readjust distance between any two instances according to whether they belong to the same class or not. Therefore, we can calculate supervised contrastive loss \begin{math}\mathcal{L}_{sc}^{warm up}\end{math}:
\begin{equation}
\mathcal{L}_{sc}^{warm up}={\textstyle\sum_{i=1}^{2N}}\frac{-1}{\left|P\left(i\right)\right|}{\textstyle\sum_{p\in P\left(i\right)}^{}}\mathrm{log}\frac{\mathrm{exp}\left(z_{i}\cdot z_{p}/\tau\right)}{{\textstyle\sum_{j\in A\left(i\right)}^{}}\mathrm{exp}\left(z_{i}\cdot z_{j}/\tau\right)},
\end{equation}
where \begin{math}A\left(i\right)\equiv I\setminus\left\{i\right\}\end{math}, \begin{math}P\left(i\right)\equiv\left\{p\in A\left(i\right):\tilde{y}_{p}=\tilde{y}_{i}\right\}\end{math}, and \begin{math}\tau\in\mathcal{R}^{+}\end{math}. Therefore, we have the loss for supervised learning \begin{math}\mathcal{L}_{sup}^{warm up}\end{math}:
\begin{equation}
\mathcal{L}_{sup}^{warm up}=\mathcal{L}_{ce}^{cluster}+\mathcal{L}_{sc}^{cluster}.
\end{equation}

Furthermore, we apply traditional contrastive loss on all samples, trying to improve the initial representation further from backbone before the clustering stage. We can calculate contrastive loss \begin{math}\mathcal{L}_{unsup}^{warm up}\end{math}:
\begin{equation}
\mathcal{L}_{unsup}^{warm up}=-{\textstyle\sum_{i=1}^{2N}}\mathrm{log}\frac{\mathrm{exp}\left(z_{i}\cdot z_{m\left(i\right)}/\tau\right)}{{\textstyle\sum_{j\in A\left(i\right)}^{}}\mathrm{exp}\left(z_{i}\cdot z_{j}/\tau\right)},
\end{equation}
where \begin{math}z_{m\left(i\right)}\end{math} indicates another view augmented from the same instance as \begin{math}z_{i}\end{math}. In our warm up stage, we alternately input a batch for supervised training or unsupervised training, which is like a dual-task procedure.

\subsection{Clustering Stage}
After warm up stage, we are going to initialize the weights of the cluster head at first. We extract the representations for all instances using the trained backbone, and we apply K-Means++\cite{arthur2006k} on the representations to get cluster centers $C'$ with shape \begin{math}\left(G,D\right)\end{math}, where $G$ is the ground truth number of intents (we are not going to investigate how to estimate G in this paper). Then, we use Hungarian algorithm\cite{kuhn1955hungarian} to find the optimal mapping between $W$ and $C'$, since $W$ contains a subset of intents ($K<G$), we extract the centers most likely to be the known intents from $C'$ and get \begin{math}C=\left\{c_{1},c_{2},\cdots,c_{K} \right\}\end{math} with the corresponding index as $W$. For simplicity, we can resort $C'$ as \begin{math}C'=\left\{c_{1},c_{2},\cdots,c_{K},c_{K+1},c_{K+2},\cdots,c_{G} \right\}\end{math}. The reason why we need to extract the centers of known intents will be discussed in the last paragraph of this subsection.

In clustering stage, as in warm up, we input a batch to get pairs \begin{math}Z=\left \{z_{1},z_{2},\cdots,z_{N} \right \}\end{math} and \begin{math}Z'=\left \{z_{N+1},z_{N+2},\cdots,z_{2N}\right \}\end{math}. For self-supervised clustering, we mainly refer to they way of SwAV\cite{caron2020unsupervised} training representations for images. In detail, firstly we calculate the prediction logits from cluster head for $Z$ and $Z'$ and get \begin{math}Q=\left \{q_{1},q_{2},\cdots,q_{N} \right \}\end{math} and \begin{math}Q'=\left \{q_{N+1},q_{N+2},\cdots,q_{2N}\right \}\end{math}, where:
\begin{equation}
q_{ij}=c_{j}\cdot z_{i},\forall i\in\left\{1,\cdots,2N\right\},j\in\left\{1,\cdots,G\right\}.
\end{equation}
Then we use Sinkhorn-Knopp algorithm\cite{cuturi2013sinkhorn} to get soft pseudo cluster assignments for $Q$ and $Q'$, as \begin{math}A=\left \{a_{1},a_{2},\cdots,a_{N} \right \}\end{math} and \begin{math}A'=\left \{a_{N+1},a_{N+2},\cdots,a_{2N} \right \}\end{math}, with the shape $\left(N,G\right)$. Also, we can use argmax to get the hard assignments as \begin{math}B=\left \{b_{1},b_{2},\cdots,b_{N} \right \}\end{math} and \begin{math}B'=\left \{b_{N+1},b_{N+2},\cdots,b_{2N} \right \}\end{math}, with the shape $\left(N\right)$. Sinkhorn-Knopp algorithm set the soft assignment for a instance considering not only its own logits, but also the other logits from the same batch, which can calculate the optimal distribution in a batch for all intents and avoid trivial solution. According to the soft pseudo assignments, we can calculate "swapped" cross entropy loss\cite{caron2020unsupervised} \begin{math}\mathcal{L}_{sinkhorn}^{cluster}\end{math}:
\begin{equation}
\mathcal{L}_{left}^{cluster}=-\frac{1}{N}{\textstyle\sum_{i=1}^{N}}{\textstyle\sum_{j=1}^{G}}\left(a_{\left(i+N\right)j}\cdot\mathrm{log}\frac{\mathrm{exp}\left(q_{ij}\right)}{{\textstyle\sum_{r=1}^{G}}\mathrm{exp}\left(q_{ir}\right)}\right),
\end{equation}
\begin{equation}
\mathcal{L}_{right}^{cluster}=-\frac{1}{N}{\textstyle\sum_{i=N+1}^{2N}}{\textstyle\sum_{j=1}^{G}}\left(a_{\left(i-N\right)j}\cdot\mathrm{log}\frac{\mathrm{exp}\left(q_{ij}\right)}{{\textstyle\sum_{r=1}^{G}}\mathrm{exp}\left(q_{ir}\right)}\right),
\end{equation}
\begin{equation}
\mathcal{L}_{sinkhorn}^{cluster}=\left(\mathcal{L}_{left}^{cluster}+\mathcal{L}_{right}^{cluster}\right)/2,
\end{equation}
where \begin{math}c\in C'\end{math}. To make samples belong to the same cluster closer in the representation space and get better clustering performance, we also add supervised contrastive loss according to the pseudo labels $B$ and $B'$, and get \begin{math}\mathcal{L}_{pseudo}^{cluster}\end{math}:
\begin{equation}
\mathcal{L}_{pseudo}^{cluster}={\textstyle\sum_{i=1}^{2N}}\frac{-1}{\left|H\left(i\right)\right|}{\textstyle\sum_{h\in H\left(i\right)}^{}}\mathrm{log}\frac{\mathrm{exp}\left(z_{i}\cdot z_{p}/\tau\right)}{{\textstyle\sum_{j\in A\left(i\right)}^{}}\mathrm{exp}\left(z_{i}\cdot z_{j}/\tau\right)},
\end{equation}
where \begin{math}H\left(i\right)\equiv\left\{h\in A\left(i\right):\tilde{b}_{p}=\tilde{b}_{i}\right\}\end{math}. Thus, the final loss for our deep clustering is:
\begin{equation}
\mathcal{L}_{main}^{cluster}=\mathcal{L}_{sinkhorn}^{cluster}+\mathcal{L}_{pseudo}^{cluster}.
\end{equation}

With the clustering stage discussed above, we notice that the classification accuracy on known intents decreases after several epochs, which seems like that the model "forgets" the information learned in warm up stage. This phenomenon might also reduce the clustering performance. To maintain the performance on classifying known intents, we keep the model trained on labeled instances. To let the labeled information better guide the clustering learning, we make the classifier layer and cluster layer share the same weights $C$, which is mentioned in the first paragraph of this subsection. Thus, we can calculate supervised loss \begin{math}\mathcal{L}_{sup}^{cluster}\end{math}:
\begin{equation}
\mathcal{L}_{ce}^{cluster}=-\frac{1}{N}{\textstyle\sum_{i=1}^{N}}\mathrm{log}\frac{\mathrm{exp}\left(c_{y_{i}}\cdot z_{i}\right)}{{\textstyle\sum_{j=1}^{K}\mathrm{exp}\left(c_{j}\cdot z_{i}\right)}},\forall c\in C,
\end{equation}
\begin{equation}
\mathcal{L}_{sc}^{cluster}={\textstyle\sum_{i=1}^{2N}}\frac{-1}{\left|P\left(i\right)\right|}{\textstyle\sum_{p\in P\left(i\right)}^{}}\mathrm{log}\frac{\mathrm{exp}\left(z_{i}\cdot z_{p}/\tau\right)}{{\textstyle\sum_{j\in A\left(i\right)}^{}}\mathrm{exp}\left(z_{i}\cdot z_{j}/\tau\right)},
\end{equation}
\begin{equation}
\mathcal{L}_{sup}^{cluster}=\mathcal{L}_{ce}^{cluster}+\mathcal{L}_{sc}^{cluster}.
\end{equation}
In the clustering stage, as in warm up, we also alternately input a batch for supervised learning or cluster learning. Therefore, we can get the overall training process as shown in Fig.\ref{fig1}.

\section{Experiments}
In this section, we introduce the details of our experiments, and discuss our model performance specifically.

\subsection{Datasets}
We conduct experiments on two public datasets consist of user queries and labeled intents. Details are shown in Table \ref{table1}.

\textbf{Banking.} It provides user queries and labeled intents from banking domain for text classification or text clustering, with totally 13083 samples and 77 types of intents\cite{casanueva2020efficient}.

\textbf{Clinc.} It contains 22500 samples of user queries in total and 150 unique labeled intents, which can be used for text classification or text clustering as well\cite{larson2019evaluation}.

\subsection{Baselines}
We choose currently popular methods for discovering new intents using semi-supervised clustering, including DeepAligned\cite{zhang2021discovering}, and SCL\cite{shen2021semi}. We directly report the results of these baselines from their papers if the results are available, otherwise we run the official code with current experiment settings and make the report.

\subsection{Experiment Settings}
We keep the same evaluation settings as in DeepAligned\cite{zhang2021discovering} for intuitive comparison. Specifically, we keep the same data split as DeepAligned for training set, validation set, and test set. To simulate the scenario as discovering new intents from raw corpus, we randomly select a certain percentage of intents as known ($25\%$, $50\%$, and $75\%$ in our cases), and then randomly select $10\%$ queries of known intents as labeled instances to get a new labeled subset, and treat the remaining samples as unlabeled ones. The models can be trained on the unlabeled training set and the labeled subset, and will be evaluated for clustering performance on test set.

For our method, we train DCSC in warm up stage and clustering stage for both 100 epochs. We set batch size 512 only for cluster training, and 128 for other cases. To optimize the net works, we use AdamW optimizer\cite{loshchilov2017decoupled} with learning rate 0.00005 and decaying rate 0.01. Besides, as DeepAligned\cite{zhang2021discovering} does, we freeze the weights of embedding layer and all transformer layer except the last one during training, which will not reduce the performance but will greatly improve the efficiency. For fair comparison with baselines, we have tested BERT\footnote[1]{The official pretrained "bert-base-uncased"\cite{devlin2018bert} available on Hugging Face\cite{wolf2020transformers}} and MPNet\footnote[2]{MPNet\cite{song2020mpnet} that is further pretrained for better sentence embedding\cite{reimers2019sentence} "sentence-transformers/paraphrase-mpnet-base-v2" available on Hugging Face\cite{wolf2020transformers}} as backbone of our model. After training, we extract the representation of sentences from test set and conduct K-Means++ to predict cluster assignments for final evaluation.

\subsection{Evaluation Metrics}
To evaluate the performance of models, we use Accuracy (ACC), Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), which are commonly used to evaluate clustering performance.

\begin{table}
  \centering
  \caption{Statistics of Banking and Clinc, where "Classes" indicates the number of unique intents, and "Training", "Validation", "Test" indicate the number of instances in the corresponding set.}
  \label{table1}
  \begin{tabular}{ccccc}
    \toprule
    Dataset&Classes&Training&Validation&Test\\
    \midrule
    Banking&77&9003&1000&3080\\
    Clinc&150&18000&2250&2250\\
    \bottomrule
  \end{tabular}
\end{table}

\subsection{Main Results}
We have summarized the clustering results from different methods in Table \ref{table2}, and DCSC has achieved the best results across all settings and datasets, which indicates its robustness and accuracy in various scenarios.

\begin{table*}[ht]
  \centering
  \caption{Clustering results of different experiment settings and datasets, where $25\%$, $50\%$, and $75\%$ indicate the fraction of known intents. $\dagger$ indicates the result we have actually run, $\ddagger$ indicates the result reported in SCL\cite{shen2021semi}, otherwise the result is reported from its own paper.}
  \label{table2}
  \begin{tabular}{cccccccccccc}
    \toprule
    \multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{3}{c}{$25\%$}&\multicolumn{3}{c}{$50\%$}&\multicolumn{3}{c}{$75\%$}\\
    \cmidrule(lr){4-6}
    \cmidrule(lr){7-9}
    \cmidrule(lr){10-12}
    Dataset&Backbone&Model&ACC&ARI&NMI&ACC&ARI&NMI&ACC&ARI&NMI\\
    \midrule
    \multirow{4}{*}{Banking}
    &BERT&DeepAligned&$49.51^{\dagger}$&$37.29^{\dagger}$&$70.26^{\dagger}$&$59.44^{\ddagger}$&$47.07^{\ddagger}$&$76.14^{\ddagger}$&64.90&53.64&79.56\\
    &MPNet&SCL&58.73&47.47&76.79&67.28&55.50&80.25&76.55&65.43&85.04\\
    &BERT&DCSC&60.15&49.75&78.18&68.30&56.94&81.19&75.18&64.55&84.65\\
    &MPNet&DCSC&\textbf{68.85}&\textbf{58.41}&\textbf{82.26}&\textbf{74.05}&\textbf{63.03}&\textbf{84.56}&\textbf{77.54}&\textbf{67.92}&\textbf{86.59}\\
    \midrule
    \multirow{3}{*}{Clinc}
    &BERT&DeepAligned&$75.20^{\ddagger}$&$65.36^{\ddagger}$&$89.12^{\ddagger}$&$80.70^{\ddagger}$&$75.26^{\ddagger}$&$91.50^{\ddagger}$&86.49&79.75&93.89\\
    &MPNet&SCL&71.23&62.02&88.30&78.36&70.71&91.38&86.91&81.64&94.75\\
    &BERT&DCSC&\textbf{79.89}&\textbf{72.68}&\textbf{91.70}&\textbf{84.57}&\textbf{78.82}&\textbf{93.75}&\textbf{89.70}&\textbf{84.41}&\textbf{95.28}\\
    \bottomrule
  \end{tabular}
\end{table*}

\textbf{Effect of cluster learning.}
First of all, $\mathrm{DCSC_{BERT}}$ has a better clustering performance than DeepAligned across all situations, and it also has outperformed SCL (with a better backbone) in most cases. SCL directly train the backbone on labeled queries with contrastive learning, while it doesn't make a self-supervised cluster training to optimize the representation space further as DeepAligned and DCSC does. In the settings of $25\%$ and $50\%$ known intents for Clinc dataset, the results of SCL is worse than DeepAligned, which indicates current method is not robust enough and there's a large potential for improvement considering MPNet\footnotemark[2] should be better on extracting sentence embeddings. Even compared with DeepAligned, DCSC is more efficient and accurate at the cluster learning stage. DeepAligned use K-Means to update pseudo labels, so it requires encoding all training instances additionally and apply K-Means for clustering after every epoch. DCSC doesn't predict pseudo labels globally, it assign pseudo labels simultaneous when given a training batch. Furthermore, DCSC jointly optimize the instance representation and cluster assignments, which can better guide the clustering procedure. Besides, though $\mathrm{DCSC_{BERT}}$ has improved the results a lot, $\mathrm{DCSC_{MPNet}}$ can achieve even better results with MPNet as backbone. Thus, better backbone or better initial sentence embedding, is still an improvement method worth trying.

\textbf{Effect of contrastive learning.}
Both DCSC and SCL use contrastive learning for better representation, although through different methods. DeepAligned mainly relies on classification loss to optimize representation, which is weak since it lacks margin constrain of hidden space for clustering based on distance.

\subsection{Ablation Study}
In this subsection, we analyze the effect of our model improvements through ablation studies.

\begin{table}[ht]
  \centering
  \caption{Ablation study of DCSC with BERT as backbone, where $\dagger$ indicates without supervised training in clustering stage.}
  \label{table2}
  \begin{tabular}{cccccc}
    \toprule
    Dataset&Fraction&Method&ACC&ARI&NMI\\
    \midrule
    \multirow{6}{*}{Banking}
    &\multirow{2}{*}{$25\%$}
    &$\mathrm{DCSC^{\dagger}}$&57.32&48.43&77.81\\
    &&DCSC&\textbf{60.15}&\textbf{49.75}&\textbf{78.18}\\
    \cmidrule(lr){2-6}
    &\multirow{2}{*}{$50\%$}
    &$\mathrm{DCSC^{\dagger}}$&62.80&53.54&80.48\\
    &&DCSC&\textbf{68.30}&\textbf{56.94}&\textbf{81.19}\\
    \cmidrule(lr){2-6}
    &\multirow{2}{*}{$75\%$}
    &$\mathrm{DCSC^{\dagger}}$&65.36&56.65&82.13\\
    &&DCSC&\textbf{75.18}&\textbf{64.55}&\textbf{84.65}\\
    \midrule
    \multirow{6}{*}{Clinc}
    &\multirow{2}{*}{$25\%$}
    &$\mathrm{DCSC^{\dagger}}$&78.22&\textbf{72.84}&\textbf{92.77}\\
    &&DCSC&\textbf{79.89}&72.68&91.70\\
    \cmidrule(lr){2-6}
    &\multirow{2}{*}{$50\%$}
    &$\mathrm{DCSC^{\dagger}}$&81.85&76.63&\textbf{93.81}\\
    &&DCSC&\textbf{84.57}&\textbf{78.82}&93.75\\
    \cmidrule(lr){2-6}
    &\multirow{2}{*}{$75\%$}
    &$\mathrm{DCSC^{\dagger}}$&83.42&78.46&94.31\\
    &&DCSC&\textbf{89.70}&\textbf{84.81}&\textbf{95.28}\\
    \bottomrule
  \end{tabular}
\end{table}

\textbf{Supervised training in clustering stage.} After comparing $\mathrm{DCSC^{\dagger}}$ and DCSC, we can figure out that the more intents are known, the more the clustering performance will decreases. This is because the model will learn complete supervised information during warm up stage in the setting of $50\%$ and $75\%$ known intents, thus it will drop more information in clustering stage without the guide of classification label.

\section{Conclusion}
In this paper, we propose Deep Contrastive Semi-supervised Clustering (DCSC), which is for discovering new intents from raw user queries. DCSC is trained through a two-stage dual-task process, to fully utilize the limited supervised information and improve the representation space with contrastive learning. Furthermore, DCSC builds a deep-learning-based clustering approach as a semi-supervised tasks, which jointly optimize the clustering and the representation to improve the final performance. We compare our model with other methods through the experiments on two public datasets, and DCSC has achieved the best results across all experiment settings and datasets, indicating that the improvements we've made can greatly improve the robustness and accuracy on text clustering.

\bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


text:
Four ways to curb light pollution, save bugs
by Talia Ogliore, Washington University in St. Louis
A swarm of bugs flying around light from stadium floodlights at night. Credit: Shutterstock
Artificial light at night negatively impacts thousands of species: beetles, moths, wasps and other insects that have evolved to use light levels as cues for courtship, foraging and navigation.
Writing in the scientific journal Biological Conservation, Brett Seymoure, the Grossman Family Postdoctoral Fellow of the Living Earth Collaborative at Washington University in St. Louis, and his collaborators reviewed 229 studies to document the myriad ways that light alters the living environment such that insects are unable to carry out crucial biological functions.
"Artificial light at night is human-caused lighting—ranging from streetlights to gas flares from oil extraction," Seymoure said. "It can affect insects in pretty much every imaginable part of their lives."
Insects and spiders have experienced global declines in abundance over the past few decades—and it's only going to get worse. Some researchers have even coined a term for it: the insect apocalypse.
"Most of our crops—and crops that feed the animals that we eat—need to be pollinated, and most pollinators are insects," Seymoure said. "So as insects continue to decline, this should be a huge red flag. As a society of over 7 billion people, we are in trouble for our food supply."
Unlike other drivers of insect declines, artificial light at night is relatively straightforward to reverse. To address this problem, here are four things that Seymoure recommends:
1. Turn off lights that aren't needed
The evidence on this one is clear.
"Light pollution is relatively easy to solve, as once you turn off a light, it is gone. You don't have to go and clean up light like you do with most pollutants," Seymoure said.
"Obviously, we aren't going to turn off all lights at night," he said. "However, we can and must have better lighting practices. Right now, our lighting policy is not managed in a way to reduce energy use and have minimal impacts on ecosystem and human health. This is not OK, and there are simple solutions that can remedy the problem."
Four characteristics of electrical light matter the most for insects: intensity (or overall brightness); spectral composition (how colorful and what color it is); polarization; and flicker.
"Depending on the insect species, its sex, its behavior and the timing of its activity, all four of these light characteristics can be very important," Seymoure said.
"For example, overall intensity can be harmful for attracting insects to light. Or many insects rely upon polarization to find water bodies, as water polarizes light. So polarized light can indicate water, and many insects will crash into hoods of cars, plastic sheeting, etc., as they believe they are landing on water."
Because it is impossible to narrow down one component that is most harmful, the best solution is often to just shut off lights when they are not needed, he said.
2. Make lights motion-activated
This is related to the first recommendation: If a light is only necessary on occasion, then put it on a sensor instead of always keeping it on.
3. Put fixtures on lights to cover up bulbs and direct light where it is needed
"A big contributor to attraction of light sources for most animals is seeing the actual bulb, as this could be mistaken as the moon or sun," Seymoure said. "We can use full cut-off filters that cover the actual bulb and direct light to where it is needed and nowhere else.
"When you see a lightbulb outside, that is problematic, as that means animals also see that light bulb," he said. "More importantly, that light bulb is illuminating in directions all over the place, including up toward the sky, where the atmosphere will scatter that light up to hundreds of miles away resulting in skyglow. So the easiest solution is to simply put fixtures on light to cover the light bulb and direct the light where it is needed—such as on the sidewalk and not up toward the sky."
4. Use different colors of lights
"The general rule is that blue and white light are the most attractive to insects," Seymoure said. "However, there are hundreds of species that are attracted to yellows, oranges and reds."
Seymoure has previously studied how different colors of light sources—including the blue-white color of LEDs and the amber color of high pressure sodium lamps—affect predation rates on moths in an urban setting.
"Right now, I suggest people stick with amber lights near their houses, as we know that blue lights can have greater health consequences for humans and ecosystems," Seymoure said. "We may learn more about the consequences of amber lights. And make sure these lights are properly enclosed in a full cut-off fixture."
More information: Avalon C.S. Owens et al, Light pollution is a driver of insect declines, Biological Conservation (2019). DOI: 10.1016/j.biocon.2019.108259
Journal information: Biological Conservation
Provided by Washington University in St. Louis
Citation: Four ways to curb light pollution, save bugs (2019, November 18) retrieved 2 February 2023 from https://phys.org/news/2019-11-ways-curb-pollution-bugs.html
Ecological benefits of part-night lighting revealed
A new kind of light bulb traps insects that carry vector-borne diseases
Light pollution a reason for insect decline
Putting animals in their best light—Researchers find some shades of LED lamps threaten wildlife
Help track 4 billion bogong moths with your smartphone–and save pygmy possums from extinction
Researchers find LEDs attract more flying invertebrates than conventional lighting
Protected areas fail to safeguard more than 75% of global insect species, finds study
Relentless sex drive may threaten survival of quolls
Researchers find deer carry SARS-CoV-2 variants that are extinct in humans


text:
https://www.barrons.com/articles/philip-morris-altria-stock-merger-talks-51566914016
Philip Morris Stock and Altria Drop After Merger Talks Confirmed. A Deal Still Makes Sense.
Teresa Rivas
Updated Aug. 27, 2019 1:00 pm ET / Original Aug. 27, 2019 9:54 am ET
Rumors of an Altria-Philip Morris combination are no longer rumors after Philip Morris International (ticker: PM) confirmed Tuesday the two companies are in merger talks and Altria Group (ticker: MO) also followed suit. The two companies are discussing a "potential all-stock, merger of equals," Philip Morris said in a statement. But the company warned there's no guarantee a deal will result from the talks or that a transaction will be completed even if a deal is reached.
The news sent Philip Morris stock down 6.5% to $72.74 at 12:35 p.m., while Altria Group (MO), which had been on pace for its largest percent increase since 2008, has fallen 2.5% to $45.94. The Dow Jones Industrial Average has fallen 0.5%, while the S&P 500 has declined 0.4%.
Philip Morris and Altria were at one time a single company, but the former was spun off a little more than a decade ago. Philip Morris was tasked with handling international markets, while Altria focused on the U.S.
Read more: Philip Morris Stock Slumped on Rumors It Might Merge With Altria
Both companies are preparing for a cigarette-free future, though investors have been more keen on Philip Morris's prospects this year. Although the shares are off their highs, they're still up a market-beating 16.4% in 2019, while Altria is down more than 4%. Investors are optimistic about Philip Morris's heat-not-burn product, iQOS, entering the U.S. market, but have been less impressed with Altria's strategic investments. Those include a stake in vaping startup Juul Labs, the nicotine pouch product on!, and cannabis company Cronos Group (CRON). Coming regulation decisions are another worry.
A deal is no sure thing. Philip Morris said a merger would be subject to the approval of the two companies' boards and shareholders, and regulators. That doesn't mean it isn't a good idea.
"It makes strategic sense for the Marlboro, iQOS, and JUUL brands to have a unified global strategy run by one company," RBC Capital Markets analyst Nik Modi wrote Tuesday, before the talks were confirmed.
Modi says a combined Philip Morris and Altria would have a U.S. profit pool as a source of stable cash—Philip Morris currently must repatriate its foreign earnings to pay dividends and buy back stock. The combined company would also face reduced foreign exchange headwinds—a source of volatility for Philip Morris in recent years.
A combined company would be able to market Juul globally, while also fully sharing the benefits of iQOS sales. This is key, because "the global tobacco market is likely going to have pockets where popularity of vaping devices vs. heat-not-burn will be split." Modi says. "Having access to the leading products in both categories globally would be a strategic advantage for Philip Morris."
If a deal were to happen, Modi estimates that Altria could command a takeout price of $60 to $62 a share, more than 17% above the current price. He notes there would be minimal synergies at best, but Philip Morris "could make the deal EPS [earnings per share] neutral at that price while increasing leverage," which looks manageable.
Modi isn't the only one who thinks that a deal would make sense. Wells Fargo analyst Bonnie Herzog, who made her pitch for a recombination on Monday, reiterated her stance following Philip Morris's confirmation of talks. She likes the additional scale a combined company would have, especially as it faces more competition around the world and potentially more stringent regulation, and believes shareholders would benefit from a more stable cash flow as well. If Altria and Philip Morris were to merge, it could benefit the impending rollout of iQOS in the U.S., she believes, and a more diverse revenue base would also reduce the impact of a strong dollar on Philip Morris's earnings. Thus, she sees "tremendous value creation for shareholders."
Of course, not everyone agrees: Philip Morris, which has been the stronger of the two firms, dropped on the rumors yesterday, and continues to do so. Investors may be disappointed that Philip Morris will once again be subject to U.S. regulation, or skittish about the company gaining exposure to Juul, which, as the biggest U.S. vaping player, has been under scrutiny, with critics claiming its products appeal too much to teens and could pose as yet unknown health risks. Still others may not want a combined company to finance a deal with increased debt.
Altria's stock, meanwhile, initially rose on the announcement, but fell into the red as the market dropped. Only time will tell if bringing the two companies together again brings benefits to both.
Write to Teresa Rivas at teresa.rivas@barrons.com
Rumors of an Altria-Philip Morris combination are no longer rumors after Philip Morris International (ticker: PM) confirmed Tuesday the two companies are in merger talks and Altria Group (ticker: MO) also followed suit.


text:
Nigeria Government Should Hands-off Power Sector, Allow Private Sector Drive Competition, Power Expert Umeh Says
The Nigerian government has been advised to hands-off the country's power sector and should only provide the enabling regulations that will allow private individuals to run the power sector.
Chukwueloka Umeh, a Chief Executive of Century Power Generation and a key player in the power sector appeared on ARISE News and said the federal government's move to protect the citizens from "price gouging" is a right move that is being carried out the wrong way.
"I have said it several times, privatization is just the first step. People expected that once the sector was privatized you immediately start seeing 24/7 power, that's not true," Dr Umeh, an American-trained engineer and an alumnus of General Electric told ARISE News.
"I will give you an example, it takes about three years to build a Genco, a baseload generating plant, and before you start building you would have spent another three years just developing that project, so that's six years to build just one plant.
"Now beyond building the plant, you also have the discos problem, you have to change assets in the discos, you have to metre people, this all takes time. So if you don't start to see the right tariffs being charged, unfortunately, you are not going to see all these changes that I am talking about.
"The government through NERC is trying to protect the citizens from what they call price gouging, but unfortunately, I think we are trying to do the right thing the wrong way. I keep saying, government should allow this sector to be run by the private sector period. Government's job is to provide the correct regulations that will allow this market to grow.
"You cannot regulate a market into existence. Government is trying to regulate this market into existence and it will never work. I am saying, for this to work, allow the private sector to drive the entire value chain of the power industry, allow competition to come in and drive the right pricing," he said.
Dr Umeh is of the view that there's a lot of misinformation in the power sector which has led to the public not getting all the information they need.
According to him "there's supposed to be increases (on electricity tariff) happening since 2015, those increases haven't happened and unfortunately, it was like a rude shock we got late last year when tariffs were increased almost by a 100% for most users.
"People didn't understand that that particular increase we saw in 2020 was supposed to happen gradually since 2015, that's the reality and unfortunately we have seen it just in one go."
By Abel Ejikeme
Officials of Trump Cabinet Discussing Invoking 25th Amendment Following Violence at Capitol
Trump Pledges Orderly Transfer of Power, as Congress Affirms Biden's Electoral College Victory


Here at American Majority, we believe that government is intended to provide the freest political space within the bounds of ordered liberty, protecting our inherent rights while giving each individual the freedom to pursue his or her own dreams.
Right now, though, we are facing a runaway government and a crushing deficit. Americans are desperate for solutions to our problems. We here at American Majority have come up with Ten Solutions for America.
We fight each and every day to return the U.S. government, at all levels, back to its proper role. We are implementing these solutions by training a fresh crop of activists and candidates who are committed to them. In 2010, we trained a total of 9,165 activists and candidates. And already in 2011, over 500 tea party groups from across the country have joined our New Leaders Project. Each one of those pledge signers represents 10 new free market candidates that American Majority will train. 500 pledge signers, 5,000 new conservative candidates. With your help, our unique training program will continue to grow and expand, and we'll come closer and closer to these solutions becoming a reality.
After reading the ten solutions, I ask you to make a generous donation to American Majority right now as we fight to place this great nation once more on a path of freedom and prosperity.
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Show your support for our Ten Solutions for America by making a contribution today as we fight to implement them, one activist and candidate at a time.
We are in this fight because America needs new leadership, right now, at all levels of government. We need leaders who have the ability to effectively communicate the ideas of free enterprise, limited government, fiscal responsibility, and individual freedom, while at the same time running sound campaigns.
But we only exist with your help Friend. We are not funded by any political party. We don't get contributions from corporations who depend on incumbents for special favors or handouts.
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I look forward to keeping you updated and hope you are having a wonderful summer.
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"Miami Photographer Exploring Florida Outdoors". Art and Gator. Retrieved May 24, 2017.
"Artist Works Food". ArtPeople. Retrieved May 25, 2017.
"The Artful Activist Collective Fights Climate Change and Toxic Politics". Creators. Retrieved May 24, 2017.
Bryan, Deirdra Funcheon, Becca (7 December 2015). "Meeting Tomorrow on Fracking in Big Cypress". browardpalmbeach.com. Retrieved May 24, 2017.
"ActivateNow News - Inform, Educate, Mobilize". Activate Now. Retrieved May 24, 2017.
"Bike trail across the Everglades draws protests". miamiherald.com. Retrieved May 25, 2017.
"Collier planning board scraps decade-old proposal to build bike path to Miami". naplesnews.com. Retrieved May 25, 2017.
"Naples News". youtube. Retrieved May 25, 2017.
"Local Environment". Retrieved May 25, 2017.


text:
Home / Jobseekers / Maternity & Parental Leave
Does my leave count towards the return to work period for OMP?
I have opted for occupational maternity pay from my employer and in the maternity policy it clearly states that I must return to work for at least 13 weeks or pay back the OMP (not the SMP). My maternity leave is due to finish in March but I have then taken some weeks annual leave until April. I am assuming that the 13 weeks commences from the end of my maternity period and that those weeks of leave contribute to the 13 weeks. Is this correct? I can't see in the policy anything about OMP and annual leave during those 13 weeks.
An award-winning family friendly culture
Without seeing the actual policy, my view is that there should be established a definite date for the end of the maternity leave period and the return to work date so as to establish that the continued absence is due to your taking holiday and not continuing with the maternity leave.
I think that upon that basis you should be able to establish where the 13 weeks starts ie at the end of the maternity leave and the beginning of the holiday leave which continues the absence either in part of in full.
Lorna Valcin
Community Staff Nurse - Various hours/shifts available
Bromley (City/Town), London (Greater)
Key Account Manager - part time, 32 hours per week or full time
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Read More On Maternity & Parental Leave
period and the return to work date so as to establish that the continued absence is due to your taking holiday and not continuing with the maternity leave.
", "dateCreated": "2019-01-15 09:49:19", "upvoteCount": 0, "url": "https://www.workingmums.co.uk/does-leave-count-towards-return-work-period-for-omp/", "author": { "@type": "Person", "name": "Lorna Valcin" } } } }


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